What is an AI SDLC control plane?

An AI SDLC control plane is the layer between an enterprise requirement and production that carries a change through the whole software development lifecycle and governs every step. Findry builds the Engineering Memory Graph, Blueprint, and evidence Ledger, and orchestrates coding agents, policy guardrails, and continuous delivery, so autonomous change is scoped, governed, and audited rather than just generated.

Do we have to replace our existing agents, scanners, or continuous delivery?

No. Every orchestrated slot ships a working Findry default and accepts your own tool behind the identical adapter interface. Start on the batteries-included defaults, then swap in your own agents, scanners, and continuous delivery one at a time, without changing how the control plane drives them.

Is LLM output ever treated as evidence?

No. Findry never treats language-model output as evidence: anything AI-inferred enters as llm-inferred and is capped below trusted status until a human confirms it. The moat is the evidence-backed graph and the governance around every change, not the agent that writes the diff. Findry never builds its own code-generation model.

What Findry is

The AI SDLC control plane for software delivery governance.

Findry is the control plane between an enterprise requirement and production. It carries a change through the whole software development lifecycle: design, architecture, code, tests, infrastructure, and a gated release, and it governs every step so the speed is safe. AI coding is commoditised; the defensible thing is evidence-backed understanding of the whole software estate and the governance wrapped around every autonomous change.

See how it works Request a pilot

What the control plane doesAgents build, the graph coordinates, policy governs, the Ledger proves

Four capabilities, one governed pipeline.

A control plane is more than a code generator. Findry pairs autonomous execution with an evidence-backed graph, embedded policy, and an audit trail, so a requirement can move at machine speed without giving up the controls a regulated enterprise depends on.

Agents everywhere

the promise

Autonomous agents generate design, architecture, code, tests, and infrastructure, then ship through canary releases: the whole lifecycle, not a code snippet.

Dependency graph

the mechanism

Repository parsing builds an evidence-graded graph of ownership, coupling, and blast radius, to coordinate multi-repo, multi-team change safely.

Embedded policy guardrails

the mechanism

Graph-aware policy is evaluated in-line at every gate, so standards and approvals apply from the first stage, not bolted on at the end.

Auditability by design

the permission

Every step emits immutable evidence with exportable artifacts: the reason a regulated enterprise can let autonomous agents ship at all.

Read them in order and the whole thesis is there: agents make it fast, the graph makes it safe, and auditability makes it permitted.

Six pillars, two classesBuild = the moat. Orchestrate = batteries-included adapters.

Three pillars Findry builds, three it orchestrates.

The control plane resolves into six pillars across two ownership classes. Three Findry builds as its own moat surface, the Brain, Blueprint, and Ledger. Three it orchestrates, Forge, Guardrails, and Ship, behind adapter interfaces. One story throughout: “I want this feature, show me it deployed in production.”

The Brain

Build

The Engineering Memory Graph: an evidence-graded model of the software estate, cross-repo, ownership, incidents, and deploys, every edge carrying provenance, confidence, and freshness.

Blueprint

Build

Requirement to impact across the software estate to a governed change plan: owners, risk, tests, rollout, rollback. Flows and ADRs, not wireframes.

Ledger

Build

An append-only, hash-chained attestation trail for every lifecycle step. Standards-aligned, and Sigstore-signable later.

Forge

Orchestrate

Agent execution via adapters: the Findry-managed default agent or your own. Findry never builds its own code-generation model, the graph is the moat, not the model.

Guardrails

Orchestrate

A policy decision point with graph-aware rules: a change touching payment_transactions requires Risk approval and contract tests before it can proceed.

Ship

Orchestrate

The cross-repo rollout plan generated from the graph, then conducted, watched, and gated through your continuous-delivery system.

Batteries included, bring your ownOne adapter interface: run on ours, or plug in yours

Findry defaults for everything, and the same interface for your own tools.

Every orchestrated slot ships a working Findry default and accepts a replacement behind the identical adapter contract. Start on the batteries-included defaults, then swap in your own agents, scanners, and continuous delivery, one at a time, without changing how the control plane drives them.

Agents

default
A Findry-managed default agent runs out of the box.

bring-your-own
Bring your own agents through the Forge adapter.

Scanners

default
Default policy and security gates ship in Guardrails.

bring-your-own
Plug in your own scanners behind the same gate contract.

Continuous delivery

default
A default rollout conductor drives the canary in Ship.

bring-your-own
Point Ship at your own CD; it conducts and gates it.

Findry never builds its own code-generation model. The moat is the evidence-backed graph and the governance around every change, not the agent that writes the diff.

Next: read the end-to-end sequence in how it works, or go deep on the mechanism in the Engineering Memory Graph.

Common questionsAnswers written first-sentence-first, visible by default

Questions we get asked.

What is an AI SDLC control plane?: An AI SDLC control plane is the layer between an enterprise requirement and production that carries a change through the whole software development lifecycle and governs every step. Findry builds the Engineering Memory Graph, Blueprint, and evidence Ledger, and orchestrates coding agents, policy guardrails, and continuous delivery, so autonomous change is scoped, governed, and audited rather than just generated.
Do we have to replace our existing agents, scanners, or continuous delivery?: No. Every orchestrated slot ships a working Findry default and accepts your own tool behind the identical adapter interface. Start on the batteries-included defaults, then swap in your own agents, scanners, and continuous delivery one at a time, without changing how the control plane drives them.
Is LLM output ever treated as evidence?: No. Findry never treats language-model output as evidence: anything AI-inferred enters as llm-inferred and is capped below trusted status until a human confirms it. The moat is the evidence-backed graph and the governance around every change, not the agent that writes the diff. Findry never builds its own code-generation model.