Kin investor deck and narrated film

A code repository built for AI.

A graph-native code repository for AI agents and the people who build with them.

Seed round / October 2026

Patent pending

kinlab.ai

AI is backing teams into a corner.

AI tools generate more code. Teams still have to understand it, check it and keep it running.

Review

+442%

median time in review

Faros, April 2026

Unreviewed

+31%

pull requests merged with no review

Faros, April 2026

Production

+125%

monthly production incidents

Faros, September 2026

The bill

39%

of enterprises report higher-than-expected AI coding costs

Mavvrik, July 2026

Faros: 22,000 developers across 4,000 teams. April compares low and high adoption. September studies deeper use among high-adoption teams. Findings are correlational. Mavvrik and Benchmarkit : 396 enterprises, surveyed April–May 2026. Cost overruns are self-reported.

We keep paying to rediscover how code connects.

Without a shared record, the next person or agent may have to trace the same connections again.

Cart total

Checkout amount

Receipt line

One small change

Discount rule

− Max discount 100%

+ Max discount 50%

76%

of tokens went to reading in one coding-agent study.

Claude Sonnet 4.5 on SWE-bench Verified.

Illustration based on Kin's saved discount example, September 16, 2026. Dashed paths mark connections to check. SWE-Pruner, 2026 : read operations were 76.1% of tokens in this study, not a measure of avoidable reading or human review time.

Kin versions code and its connections so people and AI can reuse the answers.

The discount rule's recorded connections answer the same question at the next handoff.

What uses the discount rule?

Discount rule

Cart total

Checkout amount

Receipt line

Answered from Kin's record

One question. No re-reading.

+442% time in review

Faster review

+31% unreviewed changes

More code checked

+125% monthly incidents

Earlier risk checks

39% report AI cost overruns

Less repeat reading

From Kin's saved example of September 16, 2026, on Kin 0.7.19. Kin can miss connections. The bottom row is what Kin is designed to improve; the benchmark on the next slide measured tokens.

Kin answered the same questions with 91% fewer tokens.

A sealed test on held-out questions in React and Flask. Kin supplies the map, so AI can focus on the work.

91%

fewer tokens

Total prompt + completion tokens

Searching files

849,673

With Kin

79,553

79%

Correct on the first response. No follow-up query.

19 of 24 runs

0

LLM tokens for Kin's lookup. The graph supplies the answer.

The AI response still used tokens.

8 sealed questions × 3 seeds = 24 paired runs; all 24 Kin final answers scored correct. React + Flask; same Qwen3.8-27B model. Read-only, unreleased answer-first build, tuned earlier and frozen. Kin's answer entered the first prompt. Uncached tokens; no speed or customer-bill result.

A team of 200 developers could gain ~$750K in annual value.

Illustrative annual scenario for a team using AI, before KinLab fees.

Review

$520K

Engineer time returned

Unreviewed

$45K

Less rework after merge

Production

$45K

Less incident response work

The bill

$137K

Lower AI usage spend

As AI use grows, so can the value.

Beyond the estimate: fewer mistakes, stronger customer trust and retention, and more time to deliver value.

Annual assumptions for 200 developers. Review: 6 h/week × 46 weeks × 10% saved. Rework: 10 fixes/month × 4 h. Incidents: 1/month × 40 h. Labor: $94.47/h. AI: $300/dev/month in variable usage × 19% assumed savings. Hours do not overlap. Sources: Bosu and Carver, BLS, Anthropic , LinearB . All reductions are hypothetical.

Kin is free. KinLab is the paid team platform.

The first core pricing proposal is a subscription per developer seat.

Kin

Free

The open foundation

Run it yourself. For individual developers and teams. Your code, context and history.

KinLab

Proposed core plan

$40

per developer seat per month

The managed team platform

Shared code context Coordinated reviews and releases Team and agent access controls

Potential expansion: hosted agents and automation usage, optional add-ons and enterprise packages.

Pricing hypothesis. KinLab is in development. $40 per developer seat/month is the proposed core subscription; packaging, included usage and willingness to pay require validation. Expansion is planned and separately priced. Related models: Cursor , Devin and CodeRabbit .

Kin can grow from developer use or a leadership mandate.

First customers: teams of 20 to 200 developers building with AI agents on large codebases.

DEVELOPERS BUILD MOMENTUM

LEADERS DRIVE ROLLOUT

3

The organization sees value

A shared answer to cost, review and coordination.

2

Developers share it

Teammates try it in their own work.

1

One developer starts

Less repeated investigation. More useful AI context.

1

Leadership feels the pain

AI bills, delivery friction or risk demand action.

2

CTO / CISO sets a standard

A leadership-backed pilot becomes a rollout.

3

Teams adopt together

A shared foundation across the organization.

Kin

One shared foundation

Start with one team. Build repeat use. Expand with KinLab.

Proposed adoption strategy. Kin is free and in public beta; KinLab is in development. Developer-led sharing and leadership-led rollout are paths to test, not demonstrated customer traction.

Troy Fortin Jr

FOUNDER

Troy Fortin Jr

Founder and creator of Kin. Full-time on Kin when the round closes.

I built Kin after spending my own money on AI that kept losing context and sending me back to fix its work .

Hands-on experience

AI engineering at The Home Depot

Built from that experience, self-funded, by directing coding agents

Kin, in public beta

123

releases since June

14

languages supported

17,600+

automated tests

linkedin.com/in/troy-fortin-jr github.com/firelock-ai/kin

Kin puts code connections inside the repository's own history.

The map becomes part of the foundation people and agents build on.

SELECTED EXISTING APPROACHES

Extend the existing repository model.

Each adds a layer on top of it.

Code hosts

Context engines

Agent records

GitHub

GitLab

Sourcegraph

Augment

Entire

Agent Trace

Supported by Cognition

We believe more layers are a costly, short-term answer.

Kin

Code and recorded connections, versioned together.

Tracked content

Recorded connections

One repository history

Commit, branch and merge them together, entity by entity.

Patent pending

A versioned, tamper-evident graph of code and its connections.

Stays current

Edits update only the connections they touch. Kin flags anything not yet rechecked.

Works beside Git

Kin reads Git history without changing it, so teams adopt it one repository at a time.

Architectural thesis. Products overlap in graph, context and history features; logos link to documentation. Agent Trace is an open spec. Kin architecture .

Our first year after funding moves from hiring to paying teams.

Milestones and targets for the first four quarters after the seed round closes.

Q1

Q2

Q3

Q4

Hire the first team

Reliability and developer-experience engineers.

First hires

Work with design partners

Three teams, mid-Q1 through mid-Q2.

3 design partners

Win the first paid teams

Paid pilots from Q2, shaped by their feedback.

Paid pilots + product improvements

Expand the team

A hosted-deployment engineer in Q4.

Team expansion

What we raise on next: repeat team use, pilots that become production teams, and a repeatable way to win them.

Planned sequence from our operating model. Kin is self-funded to date; fundraising for the next round starts around month 15.

A $3.75M seed takes Kin from public beta to paying teams.

Spending follows what each milestone proves.

SEED ROUND

$3.75M

24 months of runway

Valuation and instrument in conversation.

Product and engineering

70%

The first engineers, reliability, security reviews and hosted KinLab.

Go-to-market and community

9%

Design partners, developer adoption and paid pilots.

Operations

3%

Legal, accounting and insurance.

Reserve

18%

A three-month operating reserve, plus room to scale what works.

Superintelligence needs a better foundation.

Rounded from our operating model, available on request during diligence.