# Keeyu - Data Room (US)

All figures in USD.

---

## Summary

Helpdesks manage tickets. Keeyu prevents them.

Keeyu is the proactive AI platform for post-purchase e-commerce. We connect to every system that touches a customer's order, including storefronts, ERPs, warehouses, carriers, returns platforms, and helpdesks. We detect when something goes wrong, and resolve it before the customer knows.

The result: fewer tickets, lower operational cost, retained revenue.

### Where We Are Today

| Metric | Value |
|--------|-------|
| ARR | $424K |
| Customers | 29 (zero churn, 18 months). 5% annual churn modelled from Dec 2026 (M8) onward. Impact: -$58K ARR over the NTM period. |
| NRR | 108% |
| ACV | $25K (3x in 6 months) |
| Sales Pipeline | $635K (44% close rate) |
| Sales Motion | Founder-led. Recent SDR hire |
| ROI | 4.6x avg across 18 business cases. 0 negative. |
| Runway | 18 months |

### The Market

| Market | ACV | Stores | Opportunity |
|--------|-----|--------|-------------|
| Proactive AI (today) | $25k | 18k | $450m |
| Replace Helpdesk (next) | $40k | 179k | $7.2B |
| Agentic Post-Purchase OS | $55k | 326k | $17.9B |

### The Raise

$4M-$5M USD (base case $4M). Lean by design, with a US-weighted execution plan.

| | Today | Month 12 |
|--|-------|----------|
| ARR | $424K | $3.5M |
| Customers | 29 | 140+ |
| US Revenue | 14% | 50%+ |
| Team | 13 | 28 |

Use of funds:

- **Sales & US growth (52%).** Jevon + 3 US AEs, 1 AU AE, 1 US Partnerships Mgr (BPO + agency channel), 1 US Marketing Mgr (paid lead gen for both markets), 7 SDRs (2 per AE pod). First hires start M3 (Jul '26). Founder on the ground in LA from July '26.
- **Product (33%).** 2 AU-based AI Engineers. Doubling down on a truly agentic Keeyu - not a helpdesk replacement. The product that takes ACV from $25K to $40K.
- **Ops & G&A (15%).** 2 CSMs, Implementation Engineer. Clean books, fractional CFO, operational infrastructure.

**Ending cash:** ~$1.93M at M12. **Runway:** ~5 months gross / ~13 months net of M12 recognised revenue ($234K/mo, reaching $292K/mo by M13 May '27). Series A optionality, not urgency.

### Why Now

Demand. Brands want proactive. But couldn't. 80% of e-commerce CX is still fully reactive.

Insight. We lived it. PE Nation, SurfStitch, Papinelle. Saw where it broke. Knew how to fix.

Tech. AI agents can now act live. We cracked it. 50+ integrations, 80+ problem types, 18 months of compounding data.

### The Defensibility

Four structural advantages that compound with scale:

- **Proprietary data** - 30+ brands, 50+ integrations, 80+ problem types. Grows with every order.
- **Deep workflow embedding** - We read and write back. Operational control plane, not a dashboard.
- **Emerging ecosystem** - 50+ integrations forming the architecture of a platform.
- **Customer advocacy** - A CX leader refused to take a job unless the company adopted Keeyu.

### The Team

Tracy Godtschalk, CPO. PE Nation. 20 years e-commerce and CX. Lived the chaos. Ended it.

Tahir Rauf, CTO. Mastercard. Woolworths. PE Nation. Enterprise architect. Built the impossible.

Jevon Le Roux, CEO. Hurley (exited). Surf Stitch (MD). PE Nation (CEO). Closed all 29 customers personally.

We're not building a better helpdesk. We're killing the helpdesk category.

---

## Execution Plan

All figures in USD.

### 15-Month Strategy to Scale from $424K to $3.5M ARR

Raise: $4M-$5M USD (base case $4M for modelling) | Execution: July 2026 - June 2027

Starting Team: 11 people | Ending Team: 28 people

### The Seed Story

We built a proactive post-purchase platform that helps e-commerce brands prevent helpdesk tickets.

$424K ARR. 29 customers. 108% NRR. Zero churn on logos to date. 5% annual churn modelled from Dec 2026 (M8) onward. Impact: -$58K ARR over the NTM period. We did this with 13 people and 1 recent SDR hire to support founder sales.

Now we're raising $4M-$5M to do three things: scale the US sales engine through partnerships, double down on a truly agentic Keeyu, and build the GTM systems that let a small team punch above its weight.

In 15 months, we'll be at $3.5M ARR with 140+ customers across two continents — built lean, with a US-weighted execution plan.

The wedge is working. The expansion is obvious. We're ready to turn a sharp product into a category.

### 15-Month Target

| Metric | Target |
|--------|--------|
| ARR | $3,500,000 |
| Team | 28 people |
| Customers | 140+ |
| US Revenue | 50%+ |
| Ending Cash | ~$1,925,000 |
| Runway (gross burn at M12) | ~5 months |
| Runway (net of M12 recognised revenue) | ~13 months ($234K/mo recognised at M12, reaching $292K/mo by M13) - Series A optionality, not urgency |

### Unit Economics (Forward-Looking)

| Metric | Value | Notes |
|--------|-------|-------|
| ACV (new deals) | $25,000 - $40,000 | Recent 10-deal cohort; blended all-customer $11K, trending up |
| Gross Margin | 35% - 65% | Scales from M1 to M12 |
| NRR | 108% | Expansion exceeds churn |
| Logo Retention | 100% | Zero churned customers to date. 5% annual churn modelled from Dec 2026 (M8) onward. Impact: -$58K ARR over the NTM period. |
| LTV | $43,000 | Current blended ACV basis; improves to $88K as $25K cohort dominates |
| CAC (Rolling) | $11,000 | Feb 2026 actuals; improved 30% MoM, trending down |
| LTV:CAC | 3.9x | Improved from 2.0x (Jan) to 3.9x (Feb); trajectory to 8x+ as ACV expands |
| Payback Period | 14 months | Improved from 27 months (Jan) to 14 months (Feb); trending to <6 months |

### Revenue Build

We've shifted the mix: less reliance on a large AE bench, more on partnerships, paid demand gen, and founder-led closing.

| Source | Amount | Assumption |
|--------|--------|------------|
| Direct Sales | $1,400,000 | Jevon + 2 US AEs (AE #1 from M3, AE #2 from M6) + AU AE #2 (M10); 2 SDRs per AE pod. Cold-call agency bridges outbound Apr-Aug. |
| Partnerships | $1,000,000 | BPO + agency + tech partners; ~40 partner-sourced deals @ $25K, driven by 1 US Partnerships Mgr |
| Inbound (Marketing) | $850,000 | US Marketing Mgr runs paid lead gen for both markets + trade shows; 34 deals @ $25K |
| Expansion | $350,000 | 108% NRR on growing base |
| **Total New ARR** | **$3,600,000** | $250K buffer above the $3.35M required to hit $3.5M ARR |

### Channel Mix (New ARR, ex-expansion)

| Channel | % of New Logo ARR |
|---------|-------------------|
| Direct (founder + AEs) | 43% |
| Partnerships (BPO + agencies + tech) | 31% |
| Inbound (paid + events + content) | 24% |

### ARR Progression

| Milestone | ARR |
|-----------|-----|
| Starting (at raise) | $424K ($505K cash) |
| M3 | $700K |
| M6 | $1,330K |
| M9 | $2,240K |
| M12 | $3,500K |

### Headcount Build (11 → 28)

Existing team is 13 people today. We're cutting 2 offshore devs as part of the leaner plan, taking the existing baseline to 11. We then add 17 new hires across AU and US, with a clear US tilt.

| Department | Start (post-cut) | End | New Hires |
|------------|------------------|-----|-----------|
| Sales | 1 | 12 | 11 (AU: 1 AE, 3 SDRs. US: 2 AEs, 4 SDRs, 1 Partnerships Mgr). 2 SDRs per AE pod. |
| Customer Success | 1 | 3 | 2 (CSM #1 AU M6, CSM #2 US M12) |
| Product / Design | 1 | 1 | - |
| Engineering | 5 | 8 | 3 (AI Engineers x2, Implementation Engineer, all AU) |
| Operations / Marketing | 0 | 1 | 1 (Marketing Mgr US) |
| Founders | 3 | 3 | - |
| **Total** | **11** | **28** | **+17 (first hires M3 Jul '26)** |

### Hire Timing by Month (Staggered M3-M12)

No hires start before M3 (Jul '26). Recruitment opens M2. Each AE pod = 1 AE + 2 SDRs. A cold-call agency bridges outbound coverage Apr-Aug across both regions (AU: Apr-Jun, US: Jun-Aug) at US$4K/region/month, exiting as in-house SDRs ramp.

| Month | Hires | What's Happening | Owner |
|-------|-------|-------------------|-------|
| M3 (Jul '26) | US AE #1, 2 US SDRs (Pod 1), AU SDR, US Partnerships Mgr | Sales engine starts. US Pod 1 live with 2 SDRs per AE. Partnerships in market. | Jevon, Tracy |
| M4 (Aug '26) | US Marketing Mgr, AI Engineer #1 | Paid lead gen turns on for both markets. Agentic dev sprint starts. | Jevon, Tahir |
| M6 (Oct '26) | US AE #2, 2 US SDRs (Pod 2), CSM #1 (AU) | 2nd US closer + full Pod 2 SDR coverage. CS handoff begins. | Jevon, Tracy |
| M7 (Nov '26) | Implementation Engineer (AU) | Onboarding velocity for growing customer base. | Tahir |
| M9 (Jan '27) | AI Engineer #2 | AI dev pair complete. Agentic Keeyu V2 sprint. | Tahir |
| M10 (Feb '27) | AU AE #2, 2 AU SDRs (AU Pod 2) | AU closing capacity doubles. 2 SDRs per AE. | Jevon |
| M12 (Apr '27) | CSM #2 (US) | US customer success ramp for growing US base. | Tracy |

### Use of Funds

| Category | Amount | % | What It Funds |
|----------|--------|---|---------------|
| People + contractors + cold-call agency | ~$2,440,000 | 62% | 11-person existing team + 17 new hires staggered M3-M12 (Option B US AE comp: $126K OTE). Cold-call agency bridges Apr-Aug. |
| Non-people opex (marketing, rent, legal, travel, other) | ~$880,000 | 22% | Paid lead gen, focused events, content, AU expansion + US coworking, legal, travel |
| Infrastructure (COGS) | ~$380,000 | 10% | AWS, AI inference, merchant fees |
| Buffer | ~$215,000 | 6% | Contingency |
| **Total** | **~$3,915,000** | **100%** | |

### Cash Flow Math — Where Cash Comes From and Goes To

We want this transparent. Here is the full reconciliation of how $4M raised gets us to $3.5M ARR with ~$1.93M still in the bank at M12.

**Sources of Cash (Year 1)**

| Source | Amount | Notes |
|--------|--------|-------|
| Opening cash (at raise) | $505,000 | Cash on hand at close |
| Seed raise | $4,000,000 | This round |
| Revenue (12 months) | ~$1,340,000 | Sum of MRR as ARR ramps from $424K to $3.5M (reflects 2-month billing lag on back-loaded new ARR; MRR exit run-rate $234K/mo = $2.8M annualised) |
| R&D rebate (M5) | $350,000 | First tax rebate (AU R&D credit) |
| R&D rebate (M11) | $490,000 | Second tax rebate (AU R&D credit) |
| **Total Sources** | **~$6,685,000** | |

**Uses of Cash (Year 1)**

| Use | Amount | Notes |
|-----|--------|-------|
| People + contractors + cold-call agency | ~$2,440,000 | Existing team + 17 new hires (M3 start, Option B US AE comp) + cold-call bridge |
| Non-people opex (marketing, rent, legal, travel, other) | ~$880,000 | ~$73K/mo avg |
| Infrastructure (COGS) | ~$380,000 | Cloud, AI inference, merchant fees |
| Buffer | ~$215,000 | Contingency |
| **Total Uses** | **~$3,915,000** | |

**Reconciliation**

| Line | Amount |
|------|--------|
| Total Sources | ~$6,685,000 |
| Less: Total Uses | (~$3,915,000) |
| **Ending Cash (M12)** | **~$1,925,000 (~$1.93M)** |

Ending cash improved vs prior $1,750K due to lower US AE compensation (Option B: $126K OTE) and delayed hire start (M3 Jul '26).

**Runway from ~$1.93M ending cash:**

| View | Monthly Burn | Runway |
|------|--------------|--------|
| Gross burn at M12 (full team, no revenue offset) | ~$385,000/mo | ~5 months |
| Net burn at M12 (gross spend less M12 recognised revenue of ~$234K/mo) | ~$151,000/mo | ~13 months |

The net-burn view is the relevant one for Series A planning: at $3.6M ARR with $234K/mo recognised revenue at M12, reaching $292K/mo by M13 (May '27), we have ample cushion to run a Series A process on our own timetable rather than being forced into it.

### Key Milestones

| Month | Milestone | Why It Matters |
|-------|-----------|----------------|
| M3 (Jul '26) | US AE #1 + 2 US SDRs (Pod 1) + AU SDR + Partnerships Mgr live | Sales engine starts. 2 SDRs per AE pod. Cold-call agency bridges Apr-Jun (AU) and Jun-Aug (US). |
| M4 (Aug '26) | Marketing Mgr + AI Engineer #1 onboard | Paid lead gen turns on; agentic dev begins |
| M5 (Sep '26) | R&D rebate ($350K) | Cash injection |
| M6 (Oct '26) | US AE #2 + Pod 2 SDRs + CSM #1 (AU) | Full US closing capacity; CS handoff |
| M7 (Nov '26) | Implementation Engineer onboard | Onboarding velocity |
| M9 (Jan '27) | AI Engineer #2; Agentic Keeyu V2 sprint | Product velocity up |
| M10 (Feb '27) | AU AE #2 + 2 AU SDRs | AU closing capacity doubles |
| M11 (Mar '27) | R&D rebate ($490K) | Cash injection |
| M12 (Apr '27) | Series A ready; CSM #2 (US) | $3.5M ARR, 140+ customers, US 50%+ |

### What Success Looks Like at Month 12

| Metric | Target | Why It Matters |
|--------|--------|----------------|
| ARR | $3.5M | Series A fundable |
| Customers | 140+ | Proves market size |
| NRR | 108%+ | Proves product value |
| Logo Retention | 100% to date | Proves sticky product. 5% annual churn modelled from M8 onward. |
| Team | 28 people | Proves we can scale capital-efficiently with US tilt |
| US Revenue | 50%+ of new ARR | Proves US traction |
| Product | Truly agentic Keeyu (V2 GA) | Proves the agentic thesis, not a helpdesk replacement |
| New ARR Buffer | $250K above plan | Direct + Partnerships + Inbound = $3.6M new ARR vs $3.35M required |

---

## USA GTM

### Building the Proactive CX Category in the World's Largest E-Commerce Market

### Current Traction

We are already testing paths to market and messaging for the US market. Keeyu directly translates to the US.

| Customer | Status | ACV (USD) | Notes |
|----------|--------|-----------|-------|
| IM8 Health | Closed | $40,000 | David Beckham's wellness brand. Our largest customer. |
| Desky | Closed | ~$25,000 | Uniquely designed sit stand desks |

Total committed US ARR: $64,000+

We've proven the US sales motion works. This plan isn't about testing market fit. It's about scaling what's already working.

**The Commitment:** Jevon is relocating to LA as soon as the round closes.

### Year 1 Targets (July 2026 - June 2027)

| Scenario | US ARR | New Customers | Success Criteria |
|----------|--------|---------------|------------------|
| Target Case | $2.0M | 70 - 80 | Channel working, partnerships pipeline live, 3 US AEs ramped |

### Positioning: The Core Reframe

We're not competing on features. We're changing how buyers think about customer experience:

| Old Buyer Mindset | New Buyer Mindset (Keeyu Creates) |
|-------------------|-----------------------------------|
| "How do we handle more tickets?" | "Why are we getting these tickets at all?" |
| "Let's automate responses" | "Let's eliminate the need to respond" |
| "Support is a cost center" | "Post-purchase ops is a retention driver" |
| "AI will respond for us" | "AI should prevent the need to reach out" |
| "Faster response = better CX" | "No response needed = best CX" |

### Category Name: "Proactive CX"

We're creating the Proactive CX category. The name is intuitive, differentiated, and defensible:

- **Intuitive:** Buyers immediately understand the contrast with reactive support
- **Differentiated:** No one else is using this terminology
- **Defensible:** We'll publish benchmarks, host events, and build community around it

### Target Customers

**Ideal Customer Profile (ICP)**

| Attribute | Specification | Why |
|-----------|---------------|-----|
| GMV | $20M - $150M annually | Large enough for meaningful ACV, not so large that sales cycles drag |
| Orders/Month | 20K - 500K | Volume where proactive ops creates meaningful ROI |
| Platform | Shopify Plus (required) | Our integration is deepest here; buyer is most sophisticated |
| Support Team | 3 - 15 people (or BPO) | Enough pain to justify investment; not so large that change is impossible |
| Dedicated CX/Ops Hire | Yes | This is our buyer. Someone whose job is post-purchase experience |
| Current Helpdesk | Using a major e-commerce helpdesk | Already invested in CX; feeling limitations |

### Highest-Pain Verticals

| Vertical | Why They Hurt | Buying Trigger |
|----------|---------------|----------------|
| Fashion/Apparel | 30 - 40% return rates, constant WISMO, sizing anxiety | Return rate creeping up, support costs >$5/order |
| Health & Supplements | Subscription complexity, delivery sensitivity, regulated | Subscription churn >8%/month, compliance concerns |
| Home & Furniture | High-touch delivery, damage issues, long lead times | NPS below 40, negative reviews citing delivery |
| Beauty & Skincare | Subscription + high SKU count + sensitivity issues | CAC:LTV ratio declining |
| Pet | Subscription-heavy, emotional customers, auto-ship complexity | Support ticket volume growing >20% QoQ |

### Go-to-Market Channels: Partnership-Led, Founder-Closed, GTM-Engineered

We are running a US-tilted team (Jevon + 2 US AEs) and leaning hard into partnerships and paid demand gen. The Partnerships Manager drives BPO + agency pipeline from M3, and the US Marketing Manager runs paid lead generation for both markets. Each AE pod has 2 dedicated SDRs. A cold-call agency bridges outbound coverage Apr-Aug across both regions (AU: Apr-Jun, US: Jun-Aug) at US$4K/region/month while in-house SDRs are recruited.

| Channel | Year 1 Contribution | Why |
|---------|---------------------|-----|
| Direct (founder + AEs) | 43% | Jevon + 2 US AEs (AE #1 from M3, AE #2 from M6) + AU AE #2 (M10). 2 SDRs per AE pod. Highest signal, highest conversion. |
| Partnerships (BPO + agencies + tech) | 33% | 1 dedicated US Partnerships Mgr from M3 (Jul '26). Hundreds of warm brands. |
| Inbound (paid + events + content) | 24% | US Marketing Mgr runs paid lead gen across both markets + trade shows. |

### Direct Sales (43%)

Months 1-2 (May-Jun '26): Jevon closes existing AU pipeline. Louise (existing AU SDR) produces outbound. Cold-call agency bridges outbound coverage across both regions (AU: Apr-Jun at US$4K/mo, US: Jun-Aug at US$4K/mo) while in-house SDRs are recruited.

Month 3 (Jul '26): US AE #1 + 2 US SDRs (Pod 1) + AU SDR + Partnerships Manager start. Jevon is the primary US closer alongside AE #1. Founder-led sales builds brand credibility and surfaces product insights. 2 SDRs per AE pod for high-velocity outbound.

Month 4 (Aug '26): Marketing Manager starts feeding paid + event-sourced leads. Jevon and AE #1 work the pipeline jointly. Average deal cycle: 6-10 weeks.

Month 6 (Oct '26): US AE #2 + 2 US SDRs (Pod 2). Full US closing capacity (Jevon + 2 AEs). Pipeline coverage from 4 SDRs + Partnerships Mgr + paid lead gen feeds the team.

Months 7-12: Jevon stays in the closing seat for strategic and Tier 1 deals. AEs #1 and #2 ramp to primary closers for partner-sourced and inbound deals. CSM #1 (M6) handles AU onboarding. CSM #2 (M12) ramps US customer success. Execute. Ship. Close.

### Partnerships (33%) - The Lift

This is where we're leaning in. The Partnerships Manager is one of our most important hires.

**BPO Partners.** BPO providers manage customer support agents for hundreds of e-commerce brands fitting our ICP. They're transitioning from per-seat to outcome-based pricing — Keeyu is a wedge into that model. Target: 1 anchor BPO partnership signed by M2, second by M6.

| Value | Detail |
|-------|--------|
| Warm pipeline | Hundreds of brands fitting Keeyu ICP |
| Product feedback | Active input on features, co-development |
| VC reference | Will speak to investors about partnership value |
| Trust signal | Existing client relationships create warm intros |

**CX + Development Agencies.** Shopify Plus dev agencies, CX consultancies, and e-commerce ops advisors influence tool selection. A recommendation from a trusted partner accelerates deals significantly.

| Partner | Profile | Approach |
|---------|---------|----------|
| Barrel | Shopify Plus agency, strong DTC portfolio | Tech stack integration, co-sell |
| PartnerHero | CX operations, mid-market DTC brands | CX transformation, replace helpdesk |
| Peak Support | E-commerce CX outsourcing + consulting | Outcome-based CX, warm intros |
| Awesome CX | E-commerce CX for major DTC brands | Post-purchase ops transformation |
| Disco Labs | Shopify Plus dev, deep in ecosystem | Tech stack recommendation |

### Inbound - Paid Lead Generation + Events (24%)

The US Marketing Manager (M4 hire) owns paid lead gen for **both** the US and AU markets, plus trade shows, content, and the executive dinner programme. This is the lever that lets a small AE team punch above its weight.

**Mix:**
- **Paid:** LinkedIn ABM, Google search on competitor + helpdesk-replacement intent, retargeting from podcasts and content
- **Trade shows:** Surface MQLs from booth conversations + executive dinners attached to major events
- **Content engine:** Flagship reports, founder LinkedIn presence, podcast tour
- **Community:** Proactive CX Community feeds nurture and warm leads

### US Pricing Structure

| Tier | Orders/Month | Platform Fee | Per-Order | Typical ACV |
|------|-------------|--------------|-----------|-------------|
| Growth | 20K - 75K | $1,200/mo | $0.04 | $18 - 24K |
| Scale | 75K - 200K | $2,000/mo | $0.03 | $30 - 48K |
| Enterprise | 200K+ | $3,500/mo | $0.02 | $60K+ |

### Year 1 US Team Structure

| Role | Start Month | Comp (OTE) | Focus |
|------|-------------|------------|-------|
| Founder (Jevon) | M1 (May 2026) | - | AU pipeline M1-2. Primary US closer M3-8. Strategic + Tier 1 deals M9-12. |
| AE (US) #1 | M3 (Jul 2026) | $126K OTE | Pod 1 lead. Co-closes with Jevon early, ramps to primary closer M7+. 2 SDRs assigned. |
| AE (US) #2 | M6 (Oct 2026) | $126K OTE | Pod 2. Full US closing capacity. Inbound + partner-sourced deals. 2 SDRs assigned. |
| SDR (US) #1a | M3 (Jul 2026) | $91K loaded | Pod 1 outbound volume + inbound qualification |
| SDR (US) #1b | M3 (Jul 2026) | $91K loaded | Pod 1 outbound volume |
| SDR (US) #2a | M6 (Oct 2026) | $91K loaded | Pod 2 outbound volume + inbound qualification |
| SDR (US) #2b | M6 (Oct 2026) | $91K loaded | Pod 2 outbound volume |
| Partnerships Manager (US) | M3 (Jul 2026) | $168K loaded | Owns BPO + agency + tech partner channel |
| Marketing Manager (US) | M4 (Aug 2026) | $137K loaded | Paid lead gen for US + AU. Events. Content. |
| CSM #2 (US) | M12 (Apr 2027) | $109K loaded | US customer success + onboarding |

### Brand Building & Demand Generation

**Events Calendar (Year 1) — US-Weighted**

We're investing heavily in US air cover.

| Event | When | Where | Budget |
|-------|------|-------|--------|
| NRF Big Show 2027 | January 2027 | NYC | $8 - 10K |
| Shoptalk Spring 2027 | March 2027 | Las Vegas | $15 - 20K |
| eTail West 2027 | February 2027 | Palm Springs | $8 - 12K |
| ChargeX | September 2026 | - | $5K |

(CommerceNext + SubSummit deferred to Y2.)

### Content & Community

- **Podcasts:** Target 25-30 appearances in Year 1
- **Slack Community:** "Proactive CX Community" — target 500 members by end of Year 1
- **LinkedIn:** Personal brand building — target 5K+ followers by end of Year 1
- **Flagship content:** "State of Proactive CX" Report (Q1 2027), BFCM Proactive CX Report (Q4 2026)
- **Executive dinners:** 8-10 dinners across Y1, attached to major conferences, $5-8K per dinner

---

## Product-Market Fit

### The Signal

29 customers. Zero churn. 18 months. (5% annual churn modelled from Dec 2026 onward in our forward plan. Impact: -$58K ARR over the NTM period.)

No sales team. No paid acquisition. Every customer closed through founder-led sales and word of mouth. When your product becomes the thing a CX leader won't work without, you don't need to outspend incumbents on marketing.

### What Customers Say

> "If Keeyu was gone tomorrow I would go with it."
> -- EHP Labs (Global supplements brand, $40K ACV)

> "I wouldn't have taken the job if they didn't sign up Keeyu."
> -- IM8 Health

> "We went from reactive to proactive. We see issues before customers report them."
> -- A Man and his Cave

> "There are no other platforms like this."
> -- Kivari

> "Two team members used to spend eight hours a day on reporting."
> -- Decjuba (Enterprise, replaced manual AP21 reporting)

> "Keeyu helped us successfully resolve the operational pressure during the Christmas peak."
> -- Muscle Republic (Replaced Shopify multi-views entirely)

> "The team can finally see what's happening in the warehouse without calling anyone."
> -- Budgy Smuggler

### Four Themes That Keep Coming Back

#### 1. Multi-System Consolidation

Customers replace 3-5 disconnected tools (Shopify views, spreadsheets, per-platform logins) with one operational layer. This is the most consistently reported value across the entire customer base.

Customers citing this: EHP Labs, Muscle Republic, Decjuba, A Man and his Cave, Budgy Smuggler, Kivari, Chief Nutrition

#### 2. Reactive to Proactive Shift

The most emotionally resonant outcome. Brands describe a fundamental change. They went from finding out about problems after customers complain to detecting failures before they escalate.

Customers citing this: A Man and his Cave, EHP Labs, Decjuba, Muscle Republic, Budgy Smuggler

#### 3. Fulfilment SLA Visibility

Real-time visibility into fulfillment performance across warehouses, 3PLs, and carriers. Brands see SLA breaches and carrier delays before they become customer-facing issues.

Customers citing this: EHP Labs, A Man and his Cave, Decjuba, Muscle Republic, Kivari

#### 4. Reporting Time Elimination

Manual reporting is the highest-friction pain point. Decjuba had 2 staff spending 8 hours/day on reporting. EHP Labs described hours of manual work replaced. This is the strongest ROI proof point.

Customers citing this: Decjuba, EHP Labs, Budgy Smuggler, Muscle Republic

### The Bottom Line

Daily usage, workflow dependency, expansion signals, and emotional attachment to the product. No customer has churned. The customers who use Keeyu most can't imagine operating without it. This is not a tool people try and forget. It becomes how they run post-purchase operations.

### Industry Validation: Arman Taheri, COO of TalentPop

TalentPop manages CX for 750+ e-commerce brands. Arman operates inside more CX tech stacks than almost anyone in the industry.

> "I've seen the space. I know a lot of the AI players. I know the help desk players. We work and collaborate with them very very well. It's not what you're doing."

> "The biggest part for sure that I would say most brands do not have the visibility on is whether the order was fulfilled within a specific time frame." Confirming the universal blind spot across 750+ brands.

> "You're creating a ticket proactively rather than waiting for the customer to reach out. This is a better level of experience for the customer."

### Customer Testimonials

These testimonials were recorded during our detection phase - before the launch of proactive AI. Customers were already seeing transformational results from visibility and detection alone. Proactive prevention has since expanded the value significantly.

---

## Product Roadmap

Helpdesk tickets are a symptom. We're treating the cause.

Replacing them, and the 20 tools that create them, with one agentic post-purchase OS.

### The Thesis

Helpdesk tickets exist because something failed upstream and nobody caught it. The customer complains. The support agent scrambles across 15 tabs with no idea what went wrong. It's broken by design.

Keeyu connects to every system that touches the customer promise and fixes issues in real time, before the complaint, before the ticket. If you fix the failure, the ticket never exists. The helpdesk is eliminated.

No competitor can do this. Their architecture is reactive. Ours is proactive. That's what makes this roadmap possible.

### Where We Are Today

March 2026. Proactive AI is live and in production.

- 29 brands, zero churn in 18 months
- ACV tripled since January ($8K to $25K)
- 50+ integrations across storefronts, ERPs, warehouses, carriers, returns
- V4 platform migration underway

### Phase 1: Scale the Foundation (Now)

- V4 platform + SOC II, opens enterprise conversations
- B2B, subscriptions, pre-orders, expands use cases and ACV
- Automated onboarding, compresses go-live toward 24 hours

Every module solves more of the post-purchase problem. ACV grows with product surface area.

### Phase 2: Eliminate the Helpdesk

The bridge. Eliminate the category that created the problem.

If you prevent the problem, there's no ticket. If there's no ticket, you don't need a helpdesk.

**Agentic Workflow Builder.** Automated resolution across every connected system. Refunds, rerouting, notifications, inventory, all without human intervention. Reactive platforms act inside one system. We orchestrate across all of them.

**Helpdesk Widget.** Surface Keeyu's operational context inside existing support tools. Make the helpdesk better, then make it unnecessary.

**Keeyu Inbound.** Direct customer channel where Keeyu already knows the problem. Most issues resolved before anyone reaches out.

**Prevention Scorecard.** Measure tickets prevented, escalations avoided, revenue retained. The metric that makes the helpdesk category irrelevant.

ACV: $18K - $28K. Eliminating the helpdesk is not the end state. It's the unlock.

### Phase 3: The Agentic Post-Purchase OS

This is the big bet. This is why we exist.

A retailer doing 360K orders/year spends $210K to $266K across 15 to 20 disconnected tools to manage post-purchase. Order management, inventory, warehouse, shipping, tracking, returns, refunds, cross-border, comms, fraud, warranty, subscriptions.

That fragmentation is the root cause of the operational failures that create helpdesk tickets. The tools that are supposed to prevent problems are structured in a way that guarantees them.

Keeyu replaces all of it. One platform. $245K in fragmented tools replaced by $39K. And it's not just cheaper, it eliminates the fragmentation that causes failures in the first place.

**Operational Intelligence API.** Keeyu's intelligence exposed to every tool in the stack. Churn signals to marketing, risk scores to the storefront, carrier performance to routing. Keeyu becomes infrastructure, not a tool you open.

**Predictive Orchestration.** Move from detecting problems to predicting them. Reroute before the first delay. Trigger contingency before the SLA breach. Reactive platforms can predict delivery dates. They can't change operations upstream. We can.

**Multi-Channel Operations Hub.** Marketplaces, social commerce, B2B, all managed proactively from one layer. E-commerce is fragmenting into more channels. Nobody handles multi-channel post-purchase proactively. Greenfield.

**Post-Purchase Intelligence Engine.** Feed intelligence upstream into ERP, WMS, 3PL, and carrier systems. Warehouse patterns that predict failures. Product signals that predict returns. Carrier behaviours that predict delays. Stop problems at the source, not after they cascade.

ACV: $28K - $39K. ICP expands from 18K accounts (helpdesk operators) to 179K accounts (all merchants at scale).

- $18K ACV x 18K ICP = $315M ARR (current)
- $39K ACV x 179K ICP = $7.0B ARR (OS)

### The End State

- Shopify for commerce
- Klaviyo for comms
- Yotpo for loyalty
- **Keeyu for everything after the buy button**

Shopify created a category by making it easy to sell online. Keeyu is creating a category by making it easy to run the operations.

### Why the Moat Compounds

Every competitor (Narvar, AfterShip, Gorgias, Yuma, Siena) starts from the conversation and tries to reach operations. To match what we have, they'd rebuild their integration architecture, data model, and product philosophy from scratch. That's not a pivot. That's a new company.

Every order we process deepens operational intelligence. Every integration expands detection surface. Every workflow trains the system. The architecture is proactive. The moat is compounding. The category is ours to define.

---

## Market Sizing

### Post-Purchase CS Labor Replacement Model

Document Purpose: Data room validation for the $4B obtainable opportunity (bottom-up)

### Executive Summary

Keeyu is not replacing software. Keeyu is replacing labor.

When brands evaluate Keeyu, they compare it to the cost of a full-time FTE, not to Zendesk's subscription fee. This market sizing reflects that reality: we size against the global CX labor pool, not the helpdesk software market.

| Market | Value | Key Assumptions |
|--------|-------|-----------------|
| Agentic Post-Purchase OS | $57B. 2.3M Shopify stores globally | Full agentic post-purchase OS replacing 15 disconnected tools. $25K ACV bottom-up across all Shopify stores scoped to platforms and verticals Keeyu can serve |
| With Helpdesks | $8B. 326K stores with helpdesks | 326K stores already using helpdesks (invested in CX, feeling limitations of reactive approach). Filtered for platform fit and operational maturity |
| Proactive AI | $4B. 179K stores matching our ICP | 179K stores matching Keeyu ICP. Bottom-up: 179K x $25K ACV = $4B obtainable opportunity |

### The Bottom-Up Model

**How We Size This Market**

We don't use top-down industry reports. We count stores, filter for fit, and multiply by ACV.

The funnel:

```
2.3M     Shopify stores globally
  -> 326K   with helpdesks (invested in CX)
    -> 179K   match our ICP
```

### Three Phases of Expansion

The market opportunity grows as the product expands. Today SOM reflects addressable market at current ACV ($25K). Future SOM reflects addressable market at expanded ACV as the product matures into the agentic post-purchase OS.

| Phase | Product | ACV | ICP Size | Opportunity |
|-------|---------|-----|----------|-------------|
| Today | Proactive AI | $25K | 18K early ICP | $450M |
| Next | Replace Helpdesk | $25K - $40K | 179K full ICP | $4B at $25K ACV ($7B at $40K) |
| Future | Agentic Post-Purchase OS | $55K | 326K with helpdesks | $8B at $25K ACV ($18B at $55K) |

### Why Labor, Not Software?

Traditional market sizing for CX startups benchmarks against helpdesk software spend (~$15B globally). This understates Keeyu's opportunity by 6x.

The reality:

- A brand doing 360,000 orders/year employs ~6 FTEs
- Those staff cost $50-70K each fully loaded (salary + benefits + tools + management)
- Total CS labor cost: $300-420K/year
- Keeyu costs them 0.5 FTEs ($25-35K equivalent)
- ROI conversation ends before it starts

Keeyu doesn't compete with Zendesk's $200/seat/month. Keeyu competes with a $55K/year staff member who spends 80% of their time on preventable issues.

### ACV Expansion Path

ACV grows with order volume and new products:

| Stage | ACV | What It Does | What It Replaces |
|-------|-----|--------------|------------------|
| Launch | $4K | Basic monitoring | Manual checks |
| Detect | $8K | Issue detection + alerts | Reactive firefighting |
| Proactive AI (today) | $25K | Detect + prevent + act | CS headcount |
| Replace Helpdesk | $40K | Proactive + reactive in one | Helpdesk software + labor |
| Agentic Post-Purchase OS | $55K | One platform replaces 15 tools | Entire point-solution stack ($350K cost today) |

---

## Competition

### Executive Summary

Keeyu is a structural solution to a structural problem. Helpdesks are designed to receive complaints. A ticket exists because something already went wrong. That is not a feature gap. It is an architectural constraint. You cannot build proactive issue detection on top of a system whose core object is the ticket. The complaint must exist before the system can function.

Keeyu is built the other way around. The core object is the customer promise, not the complaint. Keeyu connects every system that touches that promise in real time, detects when something threatens it, and acts before the customer notices. No complaint. No ticket. No damage done.

This is why incumbents cannot follow fast. Not because they lack resources. Because their architecture slows them down.

### The Competitive Matrix

We mapped the market on two axes: Handles Tickets to Prevents Tickets. Reactive to Proactive.

**The Four Quadrants**

- **Bottom-Left. Helpdesk (Zendesk, Freshdesk):** Manage tickets after complaints. Reactive by design.
- **Transitional. Gorgias:** Doubling down on Shopify, even as its own customers roll back reactive AI features.
- **Bottom-Right. Reactive AI (Yuma.ai, Siena AI, Go Minimal):** Automates responses, still reactive. Faster triage is not prevention.
- **Top-Left. Post-Purchase Tracking (Narvar, AfterShip, Loop, Wonderment):** Shows you the problem; you can't fix it. Narvar tried to go proactive. Their CEO admitted publicly it didn't work.
- **Top-Right. Proactive AI (KEEYU):** Stops problems before customers notice. First mover in proactive.

New startups do not have bi-directional integrations. Horizontal AI tools like Claude and ChatGPT can reason but cannot act. They have no live integrations, no order data access, no ability to trigger a refund or reroute a shipment. You do not prompt your way to 50+ battle-tested integrations and 18 months of failure pattern data.

Nobody is in the top right. We're leading, with Proactive AI preventing tickets.

Reactive architecture cannot be retrofitted to be proactive. That's not a product gap. That's a structural barrier to an entirely different business model.

### The Four Competitor Buckets

#### 1. Legacy Help Desks: Gorgias, Zendesk, Freshdesk

The help desk category includes a long tail of solutions (Freshdesk, Help Scout, Intercom, Re:amaze) but only one is purpose-built for e-commerce: Gorgias. Gorgias holds greater market share among Shopify merchants than Zendesk, driven in part by Shopify's direct strategic investment in the business. Gorgias reported approximately $69M ARR and a $530M valuation as of 2024.

Gorgias faces a structural problem, not a competitive gap. Their business model is built on tickets and seats. Per-ticket volume and per-agent licensing are their revenue engine. Prevention is not a feature they can add. It is a model they would have to destroy.

**Direct Customer Evidence. Gorgias AI Rollback:**

> While they saw some value from Gorgias AI in automating general questions, they had to turn off the AI during peak season due to its inability to handle the high volume of delay issues. The AI's lack of deep integration meant it couldn't solve the underlying operational problems, leading to customers receiving incorrect information and increased resolution times. -- Kate Nicholson, The Somewhere Co, Keeyu discovery call

#### 2. Reactive AI Triage Tools: Yuma, Siena, GoMinimal, Narvar NAVI

A wave of AI-powered tools has emerged to accelerate helpdesk performance: Yuma (automates Shopify support replies), Siena (empathetic AI customer service), GoMinimal (lightweight AI triage). These tools reduce handle time, improve first-response quality, and cut agent workload inside the reactive model.

They do not solve the structural problem. By the time these tools engage, the complaint already exists. The customer is already frustrated. These are faster reactions, not prevention.

Narvar's January 2026 launch of NAVI is the clearest public signal of this ceiling. Within weeks of launch, Narvar's CEO acknowledged at NRF 2026 that "traditional tools weren't designed to solve" the proactive detection problem. NAVI is a proactive capability bolted onto 13-year-old reactive infrastructure.

#### 3. New Startups

New entrants can theoretically build proactive architecture from scratch. This is the most credible long-run competitive threat.

The challenge is the compounding moat. To build what Keeyu does, a new entrant needs: (1) Real-time bidirectional integrations across 50+ fragmented merchant systems, (2) A failure pattern library built from live deployments, (3) The accuracy credibility from proving the system works in production.

Keeyu has 18 months of live data, 29 customers, and 50+ integrations already built. A new entrant starts at zero on all three.

**Founding team advantage.** Keeyu's founders bring decades of domain expertise across e-commerce operations, CX, and enterprise architecture. We have already signed credible brand names (IM8 Health, Decjuba, EHP Labs, Camilla) and demonstrated the ability to execute at pace. Domain knowledge combined with execution speed is the hardest thing for a new entrant to replicate.

#### 4. Horizontal AI Tools: Claude Skills, ChatGPT Plugins

The critical distinction is vertical versus horizontal. Horizontal AI tools are generalized. Built to work across many industries. For simple, low-volume retailers with clean data, a configured tool might handle basic detection.

This is not Keeyu's market. Keeyu is vertical. Purpose-built for e-commerce post-purchase operations. Horizontal tools cannot: normalize fragmented schemas across 50+ live systems, build embedded workflow relationships, or accumulate accuracy credibility from 18 months of deployments.

Harvey is the reference point. Harvey used Claude as the underlying reasoning layer but built a vertical legal AI product. Harvey's defensibility was never the AI model. It was vertical distribution and compounding domain knowledge. Anthropic could not compete with Harvey by offering a horizontal product. The same logic applies here.

### The Four-Pillar Moat

Connecting fragmented e-commerce systems to be proactive is hard. It took eighteen months of schema matching across 50+ point solutions to create one source of truth.

On top of that foundation sit four things Keeyu has perfected:

#### 1. Access

Connected to the promise. Every point solution is connected and linked back to the customer promise. Keeyu links every tool back to the customer promise.

#### 2. Signal

See what others can't. Point solutions cannot achieve cross-system failure detection. Keeyu's cross-tool failure detection sees what no single system sees alone.

#### 3. Action

Bi-directional. Keeyu does not just read data. We write back. Triggering fixes, updating records, closing the loop. No reactive AI solution can do that. Keeyu reads, writes back, and closes the loop.

#### 4. Resolution

Merchant-built workflows. This is the amazing part: we've made it super easy for users to vibe code their own workflows inside Keeyu. Guard-railed by Keeyu's edge case library.

But here's what makes it a moat. E-commerce operations have zero tolerance for error. The only way to build for edge cases is to have lived them. We have.

### Sources and References

- Gorgias $69M ARR. Latka Database, 2024
- Shopify investment in Gorgias. Gorgias press release, Shopify SEC filings
- Narvar CEO quote. NRF 2026 keynote, January 2026
- Kate Nicholson / Somewhere Co Gorgias AI rollback. Keeyu discovery call
- EHP Labs 56% headcount reduction, $424K saved. Customer case study
- Keeyu 18+ months zero churn, 108% NRR. Internal metrics.
- Arman Taheri / TalentPop quotes. Keeyu discovery calls, 2026
- TalentPop 750+ brands. talentpop.co website.

---

## Defensibility & Seed Raise Positioning

### How We Think About Defensibility

Most software companies are being repriced right now because the market has decided that code is becoming free. The question every investor is asking: if someone can rebuild your product in a weekend with AI, what makes you worth backing?

We think about defensibility across four dimensions that matter for Keeyu specifically. These aren't theoretical. They are the structural advantages we are compounding every day.

### 1. Proprietary Data That Compounds

Keeyu is building the only proprietary dataset of post-purchase failure patterns at scale.

- 30+ brands generating continuous real-time order monitoring data
- 50+ system integrations feeding signals across the entire post-purchase journey
- 80+ distinct problem types detected, classified, and resolved, growing monthly
- Every order processed makes our detection engine smarter

Keeyu's founders bring decades of experience understanding brands end to end. We built gold-class workflows and Keeyu has 18 months of compounding enhancements across 29 brands.

Keeyu's founding team brings 60+ years of combined experience across e-commerce operations, CX, and enterprise architecture, including executive roles at PE Nation, SurfStitch, Mastercard, and Woolworths. That domain expertise is baked into the product's detection logic and workflow design.

This data cannot be replicated by a new entrant. It requires years of multi-brand, multi-system order flow across storefronts, warehouses, carriers, payment gateways, and returns platforms to build reliable pattern detection. A startup launching tomorrow starts at zero. Keeyu has 18 months of compounding signal across 29 brands.

Helpdesks see tickets. We see the operational failures that cause tickets, before they're created. That asymmetry is our data moat.

**Summary:** Keeyu's data asset gets better with every interaction. Every brand onboarded, every order monitored, every issue detected adds to the corpus. This is not static data. It compounds.

### 2. Deep Workflow Embedding

There is a meaningful difference between shallow workflow tools and deep workflow tools. A ticket triage layer sits on top. An operational control plane sits underneath.

Keeyu is the latter.

- Connected to storefronts (Shopify, BigCommerce, Magento), ERPs (NetSuite, CIN7, SAP), warehouses (ShipStation, ShipBob, Starshipit), carriers (Australia Post, StarTrack, Aramex), returns platforms (Loop, ReturnGo, Refundid), and helpdesks (Gorgias)
- Keeyu sits at the intersection of every system that touches a customer's order
- Keeyu doesn't just observe. Keeyu acts. Trigger a replacement. Reroute a shipment. Fix a sync error. Issue a refund.

The deeper you're embedded in a company's operations, moving their data, running their workflows, touching their money, the harder you are to displace. Keeyu is not a dashboard. It is the connective tissue running post-purchase operations.

**Summary:** Switching off Keeyu means losing real-time visibility across every system that touches a customer order. No brand that relies on us for proactive ops will rip us out for a marginally cheaper alternative. The switching cost is operational blindness.

### 3. Emerging Ecosystem and Platform Trajectory

- 50+ integrations already live, forming the early architecture of a platform
- As we expand the Workflow Hub and AI automation layer, the integration surface area grows
- The trajectory is toward becoming the orchestration layer for post-purchase, a platform that other tools plug into

This mode is emerging, not established. But the trajectory is clear: every integration we add makes the platform stickier and harder to replicate. You can rebuild a monitoring dashboard. You cannot rebuild 50+ battle-tested integrations and the operational logic connecting them.

**Summary:** We are building the ecosystem effect for post-purchase. Shopify's moat isn't hosting. It's the thousands of third-party apps built on top. Keeyu's moat will be the operational integrations and automated workflows that brands depend on daily.

### 4. Customer Advocacy as Distribution

Keeyu has an unusual distribution advantage: our customers sell for us.

- An existing customer made Keeyu a condition of joining David Beckham's nutrition brand IM8, which became our largest customer
- A customer being recruited by a competitor said: "Do you have Keeyu? No? Then I won't come."

This is not traditional channel distribution. It is earned distribution through operational dependency. When your product becomes the thing a CX leader won't work without, you don't need to outspend incumbents on marketing. Your customers do the work.

**Summary:** Keeyu's organic expansion, customer to customer, brand to brand, is the strongest signal of product-market fit. We don't have a sales team yet. We have 29 brands closed through founder-led sales and customer advocacy. That's distribution you can't buy.

### Defensibility Summary

| Dimension | Strength | Trajectory |
|-----------|----------|------------|
| Proprietary compounding data | Established | Strengthening with every brand onboarded |
| Deep workflow embedding | Established | Deepening with every integration and automation |
| Ecosystem / platform | Emerging | 50+ integrations, expanding to orchestration layer |
| Customer advocacy distribution | Emerging | Organic brand to brand expansion already happening |

Keeyu is structurally defensible today and strengthening on every dimension with scale.

---

## NRR & Cohort Analysis

### 108% Net Revenue Retention. How It Works

Keeyu's first 18 customers were onboarded at introductory pricing before the full product suite launched. They were grandfathered at their original rates for 12 months.

The latest cohort of 11 new customers are paying the current rack rate, averaging $24K ACV. All new sign-ups are on the Automate tier (not Base), reflecting the maturity of the product and the strength of the automation value proposition. The gap between what the original 18 pay and what new customers pay for the same product is the NRR expansion engine. As grandfathered customers renew and migrate to rack rate, revenue expands without adding a single new logo.

Not all customers have expanded yet - expansion depends on where each customer is in their journey from Base monitoring to full Automation. As Keeyu accelerates automation rollout, more customers will move up-tier, unlocking additional ARR from existing logos.

### The Numbers (First 18 Customers)

These are Keeyu's first 18 customers, onboarded at introductory pricing and grandfathered for 12 months. The numbers below reflect this cohort only.

| Metric | Value |
|--------|-------|
| Contracted ARR (Jan 2026) | $147,000 |
| Current ARR (Apr 2026) | $199,000 |
| Rack Rate ARR (what new customers pay) | $341,000 |
| Expansion: Contracted - Current | +$52,000 (+36%) |
| Unrealised expansion to rack rate | +$143,000 (+72%) |
| Customers in cohort | 18 (grandfathered) |
| Logo churn | 0 |
| Revenue churn | 0 |
| NRR | 108% |

### Customer-by-Customer Cohort

All figures are ARR. Contracted = Jan 2026 (grandfathered rate). Current = Apr 2026 billing. Discount = gap between current billing and rack rate.

| Customer | Contracted ARR | Current ARR | Rack Rate ARR | Discount |
|----------|---------------|-------------|---------------|----------|
| Decjuba | $16,800 | $25,200 | $53,300 | 53% |
| EHP Labs | $12,600 | $24,800 | $42,400 | 42% |
| Muscle Republic | $12,600 | $18,900 | $34,400 | 45% |
| Bronze Snake | $10,500 | $15,750 | $38,600 | 59% |
| Tony Bianco | $10,500 | $15,750 | $15,750 | 0% |
| Camilla | $12,600 | $12,600 | $18,900 | 33% |
| eShopping Group | $12,600 | $12,600 | $22,100 | 43% |
| Chief Nutrition | $8,400 | $12,600 | $17,000 | 26% |
| Kivari | $8,400 | $12,600 | $18,900 | 33% |
| Who is Elijah | $8,400 | $8,400 | $8,400 | 0% |
| Briscoe (Rebel) | $8,400 | $8,400 | $8,400 | 0% |
| Budgy Smuggler | $2,500 | $8,000 | $25,200 | 68% |
| A Man & His Cave | $5,500 | $6,300 | $11,600 | 45% |
| 2XU | $4,200 | $4,200 | $4,200 | 0% |
| Clutch Glue | $4,200 | $4,200 | $3,800 | -11% |
| La Casa | $2,900 | $2,900 | $2,900 | 0% |
| Five by Flynn | $2,900 | $2,900 | $2,900 | 0% |
| Helly Hansen | $2,500 | $2,500 | $12,600 | 80% |
| **TOTAL** | **$147,000** | **$199,000** | **$341,000** | |
| **Average ACV** | **$8,100** | **$11,000** | **$19,000** | |

### New Customer Rack Rate Reference

These 11 customers are on full rack rate with no grandfathered discounts.

| Customer | ARR (Rack Rate) |
|----------|-----------------|
| The Oodie | $63,000 |
| Boardriders | $43,300 |
| IM8 | $36,400 |
| Puma | $24,200 |
| Mister Zimi | $18,900 |
| Alternative Brewing | $12,100 |
| Elliat | $13,200 |
| Okanui | $13,200 |
| Pharmacy Online | $13,000 |
| Desky | $12,100 |
| Life Interiors | $10,700 |
| **TOTAL** | **$260,100** |
| **Average ACV** | **$23,600** |

### What Drives Expansion

The product has three tiers, each unlocking more value:

| Tier | What It Does | Expansion Trigger |
|------|--------------|-------------------|
| Base | Real-time monitoring + issue detection | Entry point. Gets customer on platform |
| Automate | AI workflows + automated resolution | Customer sees value in detection, wants automation |
| Omnichannel | Multi-brand, B2B, Ship-from-Store, Click & Collect | Operational dependency - expand to all channels |

The expansion path is natural: brands start with visibility (Base), see the issues Keeyu detects, then want automation (Automate), then expand across their full operation (Omnichannel). Every tier upgrade increases ARR without adding headcount or acquisition cost.

### Summary

Zero churn to date + 108% NRR = compounding revenue machine. 5% annual churn modelled from Dec 2026 (M8) onward as a prudent forward assumption. Impact: -$58K ARR over the NTM period.

- Every dollar of ARR today is worth $1.08 next year, with no incremental sales cost
- The 72% expansion gap in the current cohort is unrealised upside sitting in the base
- As customers migrate to current pricing, ARR grows from existing logos alone
- New customer acquisition is additive on top of this organic expansion

That's the unit economics investors look for at seed.

---

## ROI & Business Cases

### The Problem We Solve

Every e-commerce brand we speak to describes the same operational reality. Their post-purchase experience is held together by manual processes, disconnected systems, and a CX team that spends more time tracing orders across tabs than actually helping customers.

The average prospect we engage has 5 to 8 disconnected systems touching the customer promise: storefronts, ERPs, warehouses, carriers, returns platforms, and helpdesks. None of them talk to each other. When something goes wrong with an order, the CX team has to manually investigate across all of them. That investigation takes 15 to 45 minutes per ticket, and most of those tickets could have been prevented entirely if the issue had been detected before the customer noticed.

That is what Keeyu solves. We connect to every system in the post-purchase chain, detect issues before they generate complaints, and either resolve them automatically or surface them to the right person with full context. The result is fewer tickets, lower operational cost, and retained revenue from customers who would otherwise churn silently.

### How We Prove Value: The Three-Pillar ROI Model

Every business case we build follows the same three-pillar framework. Each pillar captures a distinct, measurable source of value.

**Pillar 1: Ticket Reduction.** Direct CX cost savings from preventing inbound tickets before they occur. When Keeyu detects a delayed shipment, a payment anomaly, or a fulfillment error and resolves it proactively, the customer never needs to contact support. Average annual savings: $107,000 across modeled prospects.

**Pillar 2: Operational Efficiency.** Cost savings from eliminating manual operational work outside of ticket handling. Report generation, data reconciliation, manual returns processing, management investigation time. Average annual savings: $37,000 across modeled prospects.

**Pillar 3: Revenue Uplift.** Incremental revenue retained from customers who would otherwise churn due to poor post-purchase experiences. Average annual savings: $54,000 across modeled prospects.

The methodology is deliberately conservative. Where we do not have confirmed data from discovery, we use industry benchmarks. In every case where a prospect subsequently provides their actual data, the confirmed figures exceed the benchmarks. The business cases we present are floors, not ceilings.

### The Numbers: 18 Prospects, Zero Negative Business Cases

In 2026, we have built and presented detailed business cases to 18 prospects across a range of sizes, verticals, and operational complexity levels. Every single business case shows a positive return.

**Average ROI: 4.6x.** For every dollar a prospect spends on Keeyu, the business case shows $4.60 in measurable savings. The median is 4.5x. Not skewed by outliers.

**Average payback: 2.7 months.** Prospects recover their full annual Keeyu investment in under three months on average. Fastest: 1.3 months (Mecca). Longest: 6 months (The Oodie, conservative multi-geography assumptions).

**Total annual savings modeled: $3.0M.** Across all 18 prospects, combined annual savings total $3,007,000. Against a combined ACV of $603,000, that represents an aggregate portfolio ROI of 5.0x.

### Value Scales With Complexity

The more systems, warehouses, carriers, and storefronts a prospect manages, the more manual work Keeyu eliminates and the higher the ROI.

#### Enterprise (50,000+ orders per month)

**Mecca (In Pipeline).** ~100,000 orders/month, 50 onshore CX FTEs, Salesforce Commerce Cloud + Service Cloud + Blue Yonder WMS + multiple carriers. Business case: $852,000 annual savings, 9.5x ROI, 1.3-month payback. Largest driver: Pillar 1 at $14.58 cost per ticket with 180,000 annual tickets.

**Brand Collective (In Pipeline).** 42,000 orders/month, multiple retail brands, omnichannel (online, wholesale, retail). Business case: $313,000 annual savings, 4.5x ROI, 2.7-month payback. Revenue uplift (Pillar 3) dominant at $182,000.

#### Mid-Market (5,000 to 50,000 orders per month)

**Boardriders (Billabong, Quiksilver, RVCA).** 40,000 orders/month, complex omnichannel. Business case: $153,000 annual savings, 3.5x ROI. Signed contract 26 March 2026. Validating the business case through commercial commitment.

**The Oodie (Davie Group).** 90,000 orders/month across AU, NZ, US, CA, UK with 4 x 3PLs and 22 carriers. Business case: $126,000 annual savings, 2.0x ROI. Signed.

#### SMB (1,000 to 5,000 orders per month)

**Petzyo (In Pipeline).** 3,333 orders/month. Business case: $37,000 annual savings, 3.8x ROI.

### Qualitative Patterns From Discovery

**The swivel-chair problem is universal.** Every CX team describes the same workflow: receive complaint, open helpdesk, open Shopify, open carrier, open ERP, open returns platform. Piece together what happened. 15-45 minutes per ticket. The customer has already had a negative experience before the agent begins.

**Nobody is doing proactive post-purchase today.** When we ask "how do you currently detect issues before customers complain?", the answer is consistently "we don't."

**The build-vs-buy objection is fading.** One prospect described their internal build attempt as "three months of engineering time that covered about 5% of what we actually needed."

**Peak season is the forcing function.** Multiple prospects cite BFCM, EOFY, or seasonal peaks as the trigger. They know manual processes will break under volume.

### Conversion and Pipeline Momentum

- 28 business cases produced across prospects ranging from 1,000 to 300,000 orders per month
- 18 fully modeled with ROI data, covering $603,000 in total ACV
- 4 deals currently at contract stage representing $157,000 in closing pipeline (Puma, The Oodie, Alternative Brewing/Desky, plus Boardriders which signed 26 March)
- 11 demos scheduled in the next 7 days as of 26 March 2026
- Zero churn from any customer who has deployed the platform
- Average deal cycle: 6-8 weeks (mid-market), 8-12 weeks (enterprise)

### Summary Statistics

| Metric | Value |
|--------|-------|
| Prospects with business cases | 28 |
| Prospects with full ROI modeling | 18 |
| Average ROI multiple | 4.6x |
| Median ROI multiple | 4.5x |
| Average annual savings per prospect | $167,000 |
| Median annual savings per prospect | $92,000 |
| Average payback period | 2.7 months |
| Total modeled annual savings | $3,007,000 |
| Total ACV (modeled prospects) | $603,000 |
| Aggregate portfolio ROI | 5.0x |
| ROI range | 2.0x to 9.5x |
| Order volume range | 1,000 to 300,000 per month |
| ACV range | $12,000 to $107,000 |
| Negative business cases | 0 |

Statistics reflect signed customers and active pipeline only.

---

## Financial Model

### Key Parameters

- Raise: $4M-$5M USD (base case $4M for modelling), closing June 2026
- Starting ARR: $424K
- Starting cash: $505K (at raise close)
- M12 ARR target: $3.5M
- ACV: $25K new deals (recent cohort)
- Revenue collection: 60-day lag on new customer payments
- Logo retention: 100% to date | NRR: 108% | 5% annual churn modelled from Dec 2026 (M8) onward. Impact: -$58K ARR over the NTM period.

### Gross Margin Progression

| Quarter | GM % |
|---------|------|
| Q1 (Jul-Sep) | 35% |
| Q2 (Oct-Dec) | 42% |
| Q3 (Jan-Mar) | 52% |
| Q4 (Apr-Jun) | 65% |

COGS = cloud infrastructure (AWS), AI inference, merchant fees (Stripe). Lower headcount + agentic-only product (no voice/chat) keeps inference costs contained.

### Monthly P&L (Key Months)

| | Jul (M1) | Oct (M4) | Jan (M7) | Jun (M12) |
|--|----------|----------|----------|-----------|
| Revenue | $38K | $76K | $147K | $292K |
| COGS | $25K | $44K | $66K | $102K |
| Gross Profit | $13K | $32K | $80K | $190K |
| GM% | 35% | 42% | 55% | 65% |
| People | $190K | $260K | $290K | $305K |
| Marketing | $40K | $70K | $80K | $90K |
| G&A + Other | $30K | $30K | $30K | $30K |
| Total OpEx | $260K | $360K | $400K | $425K |
| EBITDA | -$247K | -$328K | -$320K | -$235K |

### Headcount Ramp (11 → 28)

Existing team is 13 today. We're cutting 2 offshore devs, taking the existing baseline to 11. Then add 17 new hires staggered M3-M12. No hires start before M3 (Jul '26). Each AE pod = 1 AE + 2 SDRs. Cold-call agency bridges outbound Apr-Aug across both regions (AU: Apr-Jun, US: Jun-Aug) at US$4K/region/month.

| Month | New | Who | Total |
|-------|-----|-----|-------|
| Jul (M3) | +5 | US AE #1, 2 US SDRs (Pod 1), AU SDR, US Partnerships Mgr | 16 |
| Aug (M4) | +2 | US Marketing Mgr, AI Engineer #1 | 18 |
| Oct (M6) | +4 | US AE #2, 2 US SDRs (Pod 2), CSM #1 (AU) | 22 |
| Nov (M7) | +1 | Implementation Engineer (AU) | 23 |
| Jan (M9) | +1 | AI Engineer #2 | 24 |
| Feb (M10) | +3 | AU AE #2, 2 AU SDRs (AU Pod 2) | 27 |
| Apr (M12) | +1 | CSM #2 (US) | 28 |

### Cost Base (12-Month Execution)

| Category | Amount | % |
|----------|--------|---|
| People + contractors + cold-call agency | ~$2,440K | 62% |
| Non-people opex (marketing, rent, legal, travel, other) | ~$880K | 22% |
| Infrastructure (COGS) | ~$380K | 10% |
| Buffer | ~$215K | 6% |
| **Total** | **~$3,915K** | **100%** |

### Revenue Build (New ARR)

| Source | Amount | Basis |
|--------|--------|-------|
| Direct Sales | $1,400K | Jevon + 2 US AEs (M3, M6) + AU AE #2 (M10); 2 SDRs per AE pod |
| Partnerships | $1,000K | 40 partner-sourced deals @ $25K (BPO + agencies + tech) |
| Inbound | $850K | 34 deals @ $25K (paid + events + content) |
| Expansion | $350K | 108% NRR on growing base |
| **Total New ARR** | **$3,600K** | $250K buffer above the $3.35M required |
| Starting ARR | $424K | |
| **Ending ARR** | **~$3.5M** | |

Revenue collected over 12 months (with 60-day billing lag on new ARR): ~$1,340K (reflects 2-month billing lag on back-loaded new ARR; MRR exit run-rate $234K/mo = $2.8M annualised)

### Cash Flow Math — Where Cash Comes From and Goes To

**Sources of Cash (Year 1)**

| Source | Amount |
|--------|--------|
| Opening cash (at raise) | $505K |
| Seed raise | $4,000K |
| Revenue (12 months) | ~$1,340K |
| R&D rebate (M5) | $350K |
| R&D rebate (M11) | $490K |
| **Total Sources** | **~$6,685K** |

**Uses of Cash (Year 1)**

| Use | Amount | Notes |
|-----|--------|-------|
| People + contractors + cold-call agency | ~$2,440K | Existing team + 17 new hires (M3 start, Option B US AE comp) + cold-call bridge |
| Non-people opex (marketing, rent, legal, travel, other) | ~$880K | ~$73K/mo avg |
| Infrastructure (COGS) | ~$380K | Cloud, AI inference, merchant fees |
| Buffer | ~$215K | Contingency |
| **Total Uses** | **~$3,915K** |

**Reconciliation**

| Line | Amount |
|------|--------|
| Total Sources | ~$6,685K |
| Less: Total Uses | (~$3,915K) |
| **Ending Cash (M12)** | **~$1,925K (~$1.93M)** |

Ending cash improved vs prior $1,750K due to lower US AE compensation (Option B: $126K OTE) and delayed hire start (M3 Jul '26).

### Cash Position (Quarterly)

| Quarter | Starting | + Rev | + R&D | - OpEx | Ending |
|---------|----------|-------|-------|--------|--------|
| Q1 (Jul-Sep) | $4,280K | $70K | $0 | $1,000K | $3,350K |
| Q2 (Oct-Dec) | $3,350K | $210K | $350K | $1,250K | $2,660K |
| Q3 (Jan-Mar) | $2,660K | $490K | $0 | $1,375K | $1,775K |
| Q4 (Apr-Jun) | $1,775K | $980K | $490K | $1,505K | $1,965K |

Q1 starting balance includes $505K opening + $4,000K raise. R&D Rebates: M5 (Nov) $350K + M11 (May) $490K = $840K total.

### Runway from ~$1.93M Ending Cash

| View | Monthly Burn | Runway |
|------|--------------|--------|
| Gross burn at M12 (full team, no revenue offset) | ~$385K/mo | ~5 months |
| Net burn at M12 (gross spend less M12 recognised revenue of ~$234K/mo) | ~$151K/mo | ~13 months |

The net-burn view is the relevant one for Series A planning: at $3.6M ARR with $234K/mo recognised revenue at M12, reaching $292K/mo by M13 (May '27), we have ample cushion to run a Series A process on our own timetable rather than being forced into it.

### Ending Position (Apr 2027, M12)

- Ending cash: ~$1.93M
- Runway: ~5 months gross / ~13 months net of M12 recognised revenue ($234K/mo, reaching $292K/mo by M13)
- M12 EBITDA: -$205K/month (on recognised revenue of $234K/mo; improves to -$167K at $292K/mo recognised in M13)
- EBITDA breakeven trajectory: ~M14-15 (faster than original plan due to leaner cost base)

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*This document is confidential and intended for prospective investors only.*