It watches every target, writes the next move in seconds, runs the play, and credits the win — then does what no tool does today: attributes every coaching move to the number it moved. From a user's action, to a manager's signal, to a platform owner's proof.
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.
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.
Fewer than half of users now hit quota — down sharply since 2021. Boards demand measurable enablement ROI, not more dashboards.
The average revenue org runs 13 tools and is aggressively consolidating. We replace 4–6 line items with one — and prove it pays for itself.
Each role gets the same loop in the language of their job. 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, and a KPIQ report that explains the why behind every move.
Where the team gets coached — and the manager gets coached too. The heatmap shows who's drifting before they ask, and the KPImpact report turns the ledger into a board-ready ROI receipt.
Where compliance and integrations live. Signed audit trail of every motion. Native SSO via Entra ID or any SAML provider. Single pane for every connector — Salesforce, Dynamics 365, Teams, Outlook, Slack — so IT becomes a stakeholder, not a gatekeeper.
All three cockpits are built and clickable today — the AI coach and the closed-loop attribution ledger run end-to-end. Pre-revenue. This round ships the production MVP.
The top 10% don't need help. The bottom 30% get a manager. The middle 60% has never had an always-on coach. Five points × six people beats fifteen points × one.
Every coaching motion maps to the number it moved — a live ledger with a receipt for each play, packaged as the KPImpact report. This is the answer to the board question every enablement tool dodges.
We fold the coaching tool, the enablement library, the scorecard, and the attribution layer into one line item. 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. Salesforce and Gong record the activity but never close the loop; coaching tools never touch the number.
| 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 | — |
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.
We were the middle 60%, the manager stitching CRM exports by hand, the agency asked to prove the spend actually worked.
CEO of CXGlobals (15+ yrs), B2B tech demand-gen agency behind campaigns for the world's largest enterprise software brands. 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, built and demoable today.
Two tiers, sold by role. A team lands on the IC and Manager cockpits together — a pilot that proves the ROI. Enterprise then adds the platform owner's signed audit trail.
IC + Manager cockpits, coaching & the attribution ledger. The land — a team pilot.
+ Platform cockpit, SLA, signed audit trail. Switching cost: prohibitive.
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.
The round funds one transition: from product-building risk to commercialization proof. Product capacity, a dedicated seller and founders who defer their own pay — so the seed is raised on evidence, not promise. Milestones below are the Base case; the plan starts in January 2027.
Almost half funds engineering: two senior engineers hired in Costa Rica / LATAM, where the founders' network runs deep and senior comp goes roughly 2× further than the US, plus an additional engineer from January through October 2027. The founders defer their own pay — Marco draws $5K a month from July 2027, Jake $3K and Darren $2K from November 2027 — and every dollar that frees up goes into engineering. The rest builds the revenue engine: a founding seller from March 2027 and the go-to-market budget behind them. No agency spend, no SOC 2 until there's revenue to justify it.
| Line item | Purpose | Amount | % of round |
|---|---|---|---|
| Engineering · 44% · $351K | |||
| Senior Engineer · Backend (1) | Prototype → production, loop infra, first CRM integration · 16-mo, LATAM-based | $135K | 17% |
| Senior Engineer · ML/AI (1) | The coaching model + the closed-loop attribution engine · 16-mo, LATAM-based | $130K | 16% |
| Additional engineer | Funded by deferred founder pay · January–October 2027 (≈10.6 engineer-months) | $86K | 11% |
| Founder Pay · Deferred · 10% · $80K | |||
| Founder pay | Marco $5K/mo from July 2027 · Jake $3K and Darren $2K/mo from November 2027 · revised once all three are full-time with paying customers | $80K | 10% |
| Founding Seller · 19% · $154K | |||
| Founding seller (1) | Hired March 2027 · ramps to 1.5 new customers/month · fully loaded | $154K | 19% |
| GTM & Design Partners · 18% · $145K | |||
| Design-partner landing | Pilot onboarding, founder-led sales, light content | $40K | 5% |
| Outbound tooling | Enrichment, sequencer, CRM for our own pipeline | $25K | 3% |
| Pipeline programs | Events, travel, content behind the seller | $80K | 10% |
| Infrastructure & AI · 6% · $50K | |||
| Cloud + AI inference | AWS, Anthropic/OpenAI API, vector DB | $38K | 5% |
| Tooling | GitHub, Linear, Notion, Stripe | $12K | 1% |
| Legal & Ops · 3% · $20K | |||
| Legal · incorporation, SAFE, contracts | MSA + DPA templates, data-privacy baseline | $20K | 3% |
| Total | 16-month operating plan · January 2027 – April 2028 | $800K | 100% |
on an $8M post-money SAFE — ≈10% at conversion — to take the product to production, add an engineer, hire a founding seller, and reach seed-ready by August 2027 in the Base case.
We've already built the prototype the whole way through. This round turns it into a product — the AI companion that helps every user meet and exceed their KPIs, and proves it.
Clean cap table, no debt, a single instrument at a single cap. Every figure below comes from our 24-month operating model, which reconciles line-for-line to the use-of-funds table in the previous section. We underwrite to the Base case; question 07 shows the Downside and Upside.
The Base case is built to earn a seed priced well above our $8M cap: seed-ready by August 2027, ~$540K ARR by April 2028, and ~$275K still in the bank.
The model carries a $12M seed price — the conservative end. Here is what an $800K investment at our $8M cap becomes as the seed price rises with traction:
| Seed prices at | Stake after seed | Worth | On paper |
|---|---|---|---|
| $12M — model assumption | 6.72% | $806,000 | 1.01× |
| $15M | 7.13% | $1,070,000 | 1.34× |
| $20M | 7.55% | $1,510,000 | 1.89× |
These figures are net of the standard 12% employee share pool, which is created before the new money and dilutes the founders and the earlier investor — not the incoming one. We show it because it materially changes the answer.
$50,250 a month at full plan — and burn after revenue falls to near break-even by April 2028.
Two senior engineers in Costa Rica, a founding seller from March 2027, plus go-to-market, infrastructure and legal. Founder pay starts in July 2027 (Marco) and November 2027 (Jake and Darren); until then, the money it would have used funds an additional engineer. Burn peaks at $55,000 in November 2027, as the additional engineer finishes and the founders' pay is fully on, then settles at $50,250.
| Base case | Customers | ARR | Burn after revenue |
|---|---|---|---|
| Apr 2027 · first customers | 1 | $20,503 | $46,666/mo |
| Jul 2027 · seller ramped · Marco's pay starts | 7 | $123,576 | $43,077/mo |
| Aug 2027 · seed-ready | 8 | $157,826 | $40,223/mo |
| Apr 2028 · end of plan | 27 | $536,999 | $5,500/mo |
Burn steps up again at the seed when we hire beyond the founding team. The model shows that transition explicitly rather than burying it — today's figure is not the run-rate for the round after this one.
Ordinary shares only. No preferred stock, no debt, no prior rounds — a clean cap table.
The raise is a post-money SAFE: money in now, converting at the agreed cap at the next priced round. Until conversion it is a right to shares, not shares. On conversion it becomes Series Seed Preferred with the customary 1× non-participating liquidation preference — capital returned first in a sale.
| Holder | Today | After $2.5M at $12M |
|---|---|---|
| Founders — ordinary | 100% | 60.5% |
| Seed investor — preferred | — | 20.8% |
| Employee share pool | — | 12.0% |
| This round, converted | 10.0% | 6.7% |
| Debt | None | None |
Founders at 60.5% after the seed is the healthy range — enough ownership to stay motivated through a Series A. We run a single instrument at a single cap by design, on the standard post-money SAFE with no custom economics; pro-rata rights go in a side letter for meaningful lead checks only. No interest, no maturity date.
Acquisition by a larger platform in five to seven years. We own the attribution layer none of them have.
The buyers are the systems we already connect to: Salesforce, Microsoft, HubSpot, Gong, Salesloft/Outreach, Seismic, SAP. Every one of them records what a seller did. None can prove which coaching moved the number. That record is the asset, and it is the reason this is an acquisition target rather than a feature.
At roughly 4.0% after a seed, Series A and Series B:
| Sale price | $800K position worth | Return |
|---|---|---|
| $50M acquisition | $2.02M | 2.5× |
| $100M acquisition | $4.03M | 5.0× |
| $150M acquisition | $6.05M | 7.6× |
| $300M acquisition | $12.09M | 15.1× |
We priced at an $8M post-money cap: $800K buys 10% at conversion. Simple math, one instrument, no discount.
The cap reflects what most pre-seed companies don't have: a working product. We have one. Three role cockpits, the AI coach with four voices, and the closed-loop attribution ledger — built, clickable, and shipped founder-led before any outside capital.
We kept it there deliberately. Bringing in the right early investors at an unambiguously fair price is worth more to us than the last point of dilution, and a single cap keeps the table clean for the seed.
Yes — 24 months, monthly, as a working spreadsheet rather than a static slide.
Revenue is driven by selling capacity — founder time and the founding seller — not typed in. The workbook carries an Investor Summary, a Downside / Base / Upside dashboard, modeled unit economics (Base case: ~$11.6K CAC, ~8-month payback), a Proof Tracker of what we must prove before the seed, and every driver in its own labelled cell. Change one assumption and the whole model recalculates.
Cost lines reconcile to the full $800,000 from January 2027 to April 2028 — the same figures as the use-of-funds table above, to the dollar. Whatever deferred founder pay frees up is reinvested in engineering, never left idle.
The $800K is sized so the Downside still reaches seed-ready — by November 2027 — and no case runs out of cash through December 2028, even with no seed.
Base wins three-quarters of the planned customers, two months late, with 1.5% monthly churn. Downside wins half, three months late, with 2% churn. Upside is the original plan.
| $800K raise | Downside | Base | Upside |
|---|---|---|---|
| Seed-ready | Nov 2027 | Aug 2027 | Jun 2027 |
| Customers · Oct 2027 | 7 | 13 | 20 |
| ARR · Apr 2028 | $318K | $537K | $845K |
| Cash · Apr 2028 | $163K | $276K | $451K |
| Lowest cash · through Dec 2028 | $82K | $275K | $410K |
Built with a buffer: even in the Downside, cash never drops below $82K. A slower start delays the seed — it does not put the company at risk. The founding seller is what turns the capital into a revenue engine.