Autonomous agents that build & ship software

PATENT PENDING

Enterprise delivery,
months to hours.
Unsupervised.

Transforming enterprise software delivery from months to hours; auditable, secure, alive.

Agents carry a requirement all the way to production: design, architecture, code, tests, deploy. Nobody has to watch it, because an evidence-graded graph of your software estate scopes every change, and every step seals immutable, exportable proof. Every agent change clears secret scanning and carries an independent review before it ships, and that seal is the change-management evidence your SOC 2 and PCI DSS controls ask for.

Request a pilot

Fastbecause the graph makes it safeprovable because it’s audited

The shift

AI made writing code fast. It did not make shipping features fast, because enterprise features are not code problems: they are coordination problems. A real feature cuts across services, teams, and quarters of sequencing, and the AI that accelerates each repo does nothing for the space between them.

That space is where the months go. Impact analysis by hallway consensus, rollout order by spreadsheet, sign-off by ceremony. The estate is one system; nothing treats it as one.

Mission

Our mission is to compress enterprise feature delivery from months to hours. We make change across the entire software estate computable, executable, and provable, so agents can do it unsupervised: intent in, every impacted service found, every lane executed under gates at once, the whole run sealed.

Vision

We imagine an enterprise where stating a feature is shipping it: the estate rebuilds itself around intent, engineers direct agents instead of chasing status, and the coordination quarter disappears from the calendar. Speed at that scale is only ever permitted by proof, which is why the ledger is not the paperwork. It is what makes the speed possible.

How we do it

The whole story, four pages deep.

The promise is efficiency: enterprise delivery from months to hours. The graph is the mechanism that makes the speed safe, and governance is the permission to go fast. Here is each thread, with a link to the full page.

The mechanism

The Engineering Memory Graph.

The live map of the whole estate: every service, repo, API, queue, and schema, every edge graded by the evidence behind it. AI system awareness before code generation is what makes estate-wide impact computable instead of guessable.

Generic agents know files; Findry understands the software estate. An evidence-backed dependency graph spans code, APIs, events, tables, ownership, and deploys, with no trusted edge without evidence, a database constraint, not a convention. The graph is the moat.

Read: the graph

The platform

An AI SDLC control plane.

State the feature once. Findry computes the full impact set across the estate, service by service, explains every match, and hands agents one plan per impacted service. Route it past a person first if your policy says so.

Findry is the control plane between an enterprise requirement and production. It builds the Engineering Memory Graph, the Blueprint, and the evidence Ledger, and orchestrates coding agents, policy guardrails, and continuous delivery behind one adapter interface: batteries included, or bring your own.

Read: what Findry is

How it works

One governed pipeline, spec to sealed change.

Every impacted repo gets its own agent lane and they run at once, each under gates: build, tests, secret scan, independent review. A quarter of sequencing collapses into one governed run.

A requirement is scoped on the graph, graded for risk, planned, and executed by governed agents. Human approval is a policy you switch on, not a step in the pipeline. Agent dispatch, review, scanner gates, and a staged rollout all run under policy, and every stage is sealed into an evidence ledger you can audit.

Read: the end-to-end flow

The evidence

The Ledger.

Hash-chained, exportable, verifiable offline. The reason an enterprise can permit hours where it used to demand months.

Every stage of every run seals into the chain, and simulated is always labeled simulated. LLM output is never evidence.

Read: the evidence chain

The FDE program

Expertise that becomes product IP.

Forward Deployed Engineers convert enterprise expertise into product IP. An engineer works inside a design partner’s software estate, and every hour leaves something durable behind: a new extractor, a policy, a Blueprint template, a confirmed edge.

Read: the FDE program

Watch it run

A governed run, exactly as the console records it: one feature, many repos moving at once, a lane fails, resumes, and the seal names every attempt.

One governed run
Speca9f2c1…Complete spec, capturedA requirement becomes a full, structured specification, so every later stage works from something precise.
Impactb3e7d0…Blast radius, mappedImpact is scoped on the Engineering Memory Graph in minutes, traced to evidence, not meetings.
Riskc1a44e…Graded before codeThe scoped impact is graded for risk against graph-aware policy, before anyone writes a line.
Plansd8b209…Plans drafted for youTest, approval, deployment, and rollback plans are drafted from the impact and the risk grade.
ApprovalpolicyOptional human yesOff by policy, execution starts here on its own. On, a person reviews the evidence-backed impact and the plan first, and their decision starts it.
Execution5% → 50% → 100%Governed rolloutGoverned agents execute behind scanner gates, with a canary that advances on healthy signals and rolls back on breach.
SealedsealedSealed with proofEvery stage transition is sealed into an append-only, hash-chained evidence ledger you can hand to a regulator.

Weeks of coordination become one governed run: scoped before code, gated by policy rather than by a queue, sealed with proof.

Watch a governed run

A feature request becomes a governed production change.

Every stage in this film is sealed to the evidence ledger. Labeled simulation where simulated.

Where the time comes back

Every place the calendar leaks, the graph gives it back.

Months become hours not by cutting corners, but by removing the work that never needed a human in the first place (discovery, scoping, and plumbing) and by making the risky parts governed. Here is each cost, and what Findry does about it.

01Planning takes quarters

8–16 weeks of PRD walkthroughs and review cycles before build.

Impact analysis in minutes, from the graph

A requirement traverses the software estate graph to its impacted services, APIs, events, tables, and owners, deterministically, before anyone writes code.

02Blind discovery

Grepping code and chasing Slack to guess a change's blast radius.

Owners and blast radius resolved from evidence

The graph already knows who owns what and what couples to what, and every impacted-node claim links the evidence that justifies it.

03Rework over reuse

Hand-rolling scaffolds, CI/CD, IaC, and auth while docs drift.

Codified templates and extractors

Recurring structure is captured through the FDE program as reusable extractors, policies, and Blueprint templates, ready to shape the next change.

04Risky shipping

Fragmented stacks and late policy driving failed releases.

Gated canary with attested rollback

Rollout is generated from the graph and gated through policy; a canary advances on healthy signals and rolls back on breach, each transition sealed into the audit trail.

Integrations

Plugs into the estate you already run.

Ingestion, notifications, delivery, and inference all ride the same adapter seams, so Findry meets your stack where it is. The solid set below is wired in the product today. The dashed set is the roadmap: planned, not yet built.

Connected today

  • GitHub

    App ingestion · pull requests

  • Jira

    Issue ingestion · sync

  • Backstage

    Catalog ingestion · sync

  • Datadog

    Telemetry ingestion · sync

  • Slack

    Webhook event notifications

  • Temporal

    Durable run orchestration

  • GitHub Actions

    Staged CD rollouts

  • Argo CD

    Staged CD rollouts

  • Jenkins

    Staged CD rollouts

  • Anthropic Claude

    Writer and reviewer models

  • Moonshot Kimi

    Policy-selected writer and reviewer

  • Voyage AI

    Embeddings for graph matching

On the roadmap · planned, not yet built

  • GitLab
  • Bitbucket
  • Azure DevOps
  • Linear
  • ServiceNow
  • PagerDuty
  • Grafana
  • OpenAI

Planned means planned: nothing here ships until it lands behind the same adapter seams the connected set rides

Request a pilot

Point Findry at your software estate. Watch a requirement reach production in hours, provable claim by claim.

We’re onboarding a small number of design partners. The parsers, graph, and governed workflow spine are real; anything simulated is labelled as such. No fake demos.

Request a pilot