It watches every target, suggests the next move in seconds, runs the play, and credits the win. Then it does what no tool does today: it closes the loop — attributing every coaching move to the number it moved, from a rep's action to a manager's signal to a platform owner's proof.
2026 · Confidential · $400K on a $5M post-money SAFE · ~16-month runway
The top 10% don't need help. The bottom 30% get a manager's attention. The middle 60% — where most of your revenue lives — gets a quarterly review and a content library nobody opens. They fall behind on their KPIs mid-cycle, and by the time anyone notices, the quarter is already lost.
An AI coach that knows the work — and credits the person when it lands. The same five steps repeat for every employee, every day. Pick the voice that gets your team moving; the coach gets sharper with every move it suggests.
Every target tracked. Every gap detected the moment it opens.
AI writes the next move in under 2 seconds, in your team's voice.
One click runs the play. Or the manager assigns it.
Every win attributed back to the move that caused it.
The coach you have at month 6 isn't the one you started with.
For the first time, an AI can coach with the nuance of a senior manager — at a cost per nudge that dropped 40× in two years. Personalized, always available, never tired.
Fewer than half of reps now hit quota — down sharply since 2021 (industry surveys). Boards demand measurable enablement ROI — not more dashboards. The middle 60% is where the lift hides.
The average revenue org runs 13 tools. Buyers are aggressively consolidating. KPIcons replaces 4–6 line items with one — and proves it pays for itself.
Performance Enablement + AI Coaching + Revenue Intelligence · 2026
Mid-market orgs (50–1,500 employees) · NA + EU + LATAM
5-year obtainable share · 600 logos × $30K blended ACV
We start in the revenue org — the wedge where the ROI is provable and the buyer is clear. AI-native coaching is growing 38% CAGR, and enablement budgets are consolidating toward tools that can prove they paid for themselves.
Each role gets the same loop, in the language of their job: the rep does the work, the manager coaches and sees the signal, the platform owner gets a signed, auditable trail of every motion. Customers land on a team — IC and Manager cockpits together — then expand across the org to the Platform cockpit, one shared loop graph underneath.
Where the work happens — and the core promise lives: meet and exceed your KPIs. Live targets, the AI coach with four voices, content surfaced the exact moment it's relevant — and a KPIQ report that explains the why behind every move, in the coach's own voice.
Where the team gets coached on what only a human can do — and where the manager gets coached too. The heatmap shows who's drifting before they ask, and the live ledger proves which coaching move moved which number — banked in the KPImpact report, the board-ready ROI receipt.
Where compliance and integrations live. Signed audit trail of every motion. Native SSO. Single pane for every connector — IT becomes a stakeholder, not a gatekeeper.
All three cockpits are built and clickable today as a full working prototype — the AI coach and the closed-loop attribution ledger run end-to-end; training the model on live customer data is the core post-raise build. Pre-revenue. This round ships the production MVP.
The top 10% don't need help. The bottom 30% get a manager. The middle 60% — where most revenue lives — has never had an always-on coach. Five points × six people beats fifteen points × one. The whole product starts from one promise: help every person meet and exceed their KPIs.
The manager dashboard maps every coaching motion to the number it moved — a live ledger with a receipt for each play, packaged as the KPImpact report. No correlation hand-waving. This is the answer to the board question every enablement tool dodges: "did it actually work?"
The average revenue org runs 13 tools and is aggressively consolidating. KPIcons folds the coaching tool, the enablement library, the scorecard, and the attribution layer into one line item — and proves it pays for itself. In a budget-cut cycle, "replaces 4–6 tools" wins the room.
Because we sit where the work happens, every coaching move and its outcome becomes proprietary behavioral performance data — the closed loop nobody else holds. Each attributed win makes the model sharper. Salesforce and Gong record the activity but never close the loop; coaching tools never touch the number.
Three founders with 15+ years selling enablement, marketing, and channel software to the exact enterprises KPIcons is built for. Founder-market fit isn't a talking point — we were the middle 60%, the manager stitching CRM exports by hand, the agency asked to prove the spend actually worked. Every product decision is graded against one question: would the version of us five years ago have paid for this?
CEO of CXGlobals (15+ yrs), B2B tech demand-gen agency behind campaigns for the world's largest enterprise software brands. 15+ yrs scaling digital marketing, channel programs, and partner ecosystems across NA & LATAM. San José, Costa Rica.
Channel Engagement Manager at SAP, leading partner enablement across the SAP ecosystem. Co-author of "You Buyin' This?" — a modern playbook on enterprise sales. Previously Nimbl, marketing & SAP talent.
Former EVP at CXGlobals; senior leadership stints at Microsoft, Algolia, and Alfresco. Channel, P&L, and demand-gen veteran with deep enterprise GTM playbook. Greater Seattle.
Most pre-seed teams raise on a wireframe and a thesis. Our proof point is execution velocity: a complete, clickable platform — all three cockpits, the AI coach with four voices, and the closed-loop attribution ledger — built and demoable today. The bet you're underwriting is the team that shipped this much, this fast, pre-funding.
Fill the two ‹N› counts with real pipeline before this deck goes out — external validation is the one number an investor circles on this slide.
Two tiers, sold by role. A team lands on the IC and Manager cockpits together — a pilot that proves the ROI. The Enterprise tier then adds the platform owner's signed audit trail: data and trust too costly to rip out. Each expansion adds switching cost.
IC + Manager cockpits, coaching & the attribution ledger. The land — a team pilot.
+ Platform cockpit, SLA, signed audit trail. Switching cost: prohibitive.
Land with a team pilot at $299/seat. Expand across the org once the ledger proves ROI. The Enterprise tier — data + audit trail — is what makes the account impossible to rip out.
| Capability | KPIcons | Gong / Salesforce Revenue tools |
Lattice / Culture Amp People tools |
BetterUp Coaching |
|---|---|---|---|---|
| AI coach with multiple voices, on the work | ✓ 4 personas | ~ tips | — | ~ human, scheduled |
| Every motion attributed to the number it moved | ✓ live ledger | — | — | — |
| Closes the loop — coaching tied to the outcome | ✓ proven | — | — | — |
| Every AI action explains itself, in plain language | ✓ KPIQ report | — | — | ~ human notes |
| Board-ready ROI proof, attributed | ✓ KPImpact report | — | — | — |
| Replaces 4–6 point tools in one platform | ✓ consolidates | ~ revenue only | ~ people only | — |
The wedge: nobody else puts an always-on AI coach on the work and attributes every motion to the number it moved. That closed loop is the behavioral-data moat — and the reason the category is ours to define.
A pre-seed isn't measured in ARR — it's measured in de-risking. This round takes us from "impressive prototype" to "validated product with paying design partners and a proven attribution lift" — the exact proof a seed investor underwrites. Every milestone below removes a specific reason to say no at seed.
$400K · ~16-month runway · built to reach seed-ready, nothing more. Two-thirds funds the two senior engineers — hired in Costa Rica / LATAM, where the founders' network runs deep and senior comp goes roughly 2× further than the US — who turn the prototype into a real product. The rest lands the first paying design partners and keeps the lights on. No AE yet, no agency spend, no SOC 2 until there's revenue to justify it — every line is the leanest path to the milestones on Slide 11.
| Line item | Purpose | Amount | % of round |
|---|---|---|---|
| Team · 66% · $265K | |||
| Senior Engineer · Backend (1) | Prototype → production, loop infra, first CRM integration · 16-mo, LATAM-based | $135K | 34% |
| Senior Engineer · ML/AI (1) | The coaching model + the closed-loop attribution engine · 16-mo, LATAM-based | $130K | 33% |
| GTM & Design Partners · 16% · $65K | |||
| Design-partner landing | Pilot onboarding, founder-led sales, light content | $40K | 10% |
| Outbound tooling | Enrichment, sequencer, CRM for our own pipeline | $25K | 6% |
| Infrastructure & AI · 12% · $50K | |||
| Cloud + AI inference | AWS, Anthropic/OpenAI API, vector DB | $38K | 10% |
| Tooling | GitHub, Linear, Notion, Stripe | $12K | 3% |
| Legal & Ops · 5% · $20K | |||
| Legal · incorporation, SAFE, contracts | MSA + DPA templates, data-privacy baseline | $20K | 5% |
| Total | ~16-month runway · to seed-ready | $400K | 100% |
on a $5M post-money SAFE — to ship the MVP, land our first paying design partners, and reach seed-ready by mid-2027.
We've already built the prototype the whole way through. This round turns it into a product. Join us in building the AI companion that helps every rep meet and exceed their KPIs — and proves it. Phase 1 ships that wedge; the same closed-loop engine expands across every team and role that runs on a number.