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SHIPPROVEN · BY VERRIAN

Autonomous code, proven before it ships.

ShipProven is autonomous software engineering that can't ship unsafe code. Every change our AI agents produce must survive the same production pipeline a human engineer's would — and win the approval of a consensus of independent models — before it can merge.

Provider-agnostic · Multi-cloud · Regulated-grade, for every team.

A change only merges when every gate is green.

An agent proposes a change on the left. A dashed vertical gate barrier separates the agent from five gates — Build, Deploy, End-to-end and UI, Security, Consensus — which move through pending, running and passed states and then reset and replay. The verdict line beneath reads "ALL GATES GREEN — MERGE AUTHORISED" whenever every gate is passed.

Build
Deploy
E2E + UI
Security
Consensus

ALL GATES GREEN — MERGE AUTHORISED

A change only merges when every gate is green.

Generating code is solved. Trusting it isn't.

The last two years made generating code effortless. They did almost nothing to make trusting it safe. Today's agentic tools optimise for generation speed and leave a human to catch whatever's wrong afterwards — which, for anyone shipping to production, is the expensive part.

The code an agent writes is only worth as much as your confidence that it's correct, secure, and won't break in production. In a regulated environment, "the model is usually right" is not an answer you can give an auditor.

The industry built a faster way to write code and never built the trust layer underneath it. That's the layer ShipProven is.

Trust is manufactured by the pipeline — not assumed from the model.

Five mechanisms, each one a moat — and one principle underneath them all. Together they make it structurally impossible for our agents to ship code that hasn't earned its way in.

3.2

Multi-provider consensus review

One review, many independent models, a quorum that must agree.

One review, several providers, and a quorum that must agree — through our own gateway.

  1. Provider-diverse quorum.Models from a set number of independent providers must approve — no single vendor can wave code through.

  2. Independent meta-judge.A separate model rules on the full set of reviews. Its verdict has to pass too.

  3. A compliance-team artifact.You can put the record in front of an auditor — every decision is attributable, every provider named.

No single model, and no single vendor, can wave code through.

3.3

Compounding memory — the Scrum-Master loop

Every failure makes the next release safer.

Every failure and its resolution feeds future agent runs, through both prompt calibration and retrieval memory. The system carries a growing catalogue of hard-won lessons, and that institutional knowledge is ours — not a frontier lab's. A dedicated Scrum-Master agent closes the loop: it runs the retro after each cycle and feeds what it learns straight back into the memory that steers every other agent. The result is a delivery system that gets more reliable the more it's used.

3.4

The gateway moat

Own the gateway, own the economics and the model flywheel.

Every model call routes through our own provider-agnostic gateway — so cost, quality and evolution are ours to steer.

  1. Cost control at the door.Meter every call and enforce per-tenant budgets — economics a tool captive to closed APIs can never own.

  2. A live model laboratory.A/B-test new models in production as review candidates; the best graduate into agent duty.

  3. A self-hosted flywheel.The strongest signal feeds fine-tuning on our own code conventions — not a frontier lab's.

Live in production today across two clouds, fronting six-plus providers.

3.5

Self-directing & self-improving

It creates its own work — and upgrades itself — but still can't bypass the gate.

The organisation doesn't just run the Plans it's given — it creates and upgrades them itself, and every one still faces the same gate.

  1. The Infra agent creates work.Cluster-health signals become new Plans automatically — no queue-tender required.

  2. The Scrum-Master agent improves the system.Retrospectives become Plans that upgrade the agents' own tooling — like local pre-flight verification before a full PR run.

  3. The gate never yields.Self-generated changes pass the same ungameable pipeline and consensus review as anything else.

Full autonomy and full control at once — a system that improves itself without ever being trusted to police itself.

3.6

One principle underneath it all: composable, not monolithic

The best tool for every job — swappable, for good.

The industry is betting on ever-larger monolithic models. Production delivery demands the best tool for every job.

  1. Resilience.No model or provider is a single point of failure — if one is down, we route around it.

  2. Best-in-class quality per task.Deterministic tools where independence matters (security, policy checks); models where judgment matters.

  3. Future-proofing.Every piece is swappable, every model call routes through our gateway. Models change month to month; the architecture doesn't.

Never rely only on a model to tell you code is safe.

SCENE 1

Each rung stricter than the last.

Every real rung is a set of Tekton checks. Rung 1 opens the repo; rung 2 stands up a preview namespace on demand, per PR, before merge; rung 3 exercises the change on integrated staging; rung 4 promotes into production behind a human GitOps merge.

RUNG 1

Enter the repo

5

RUNG 3

Staging

12

RUNG 4

Production

15

Gateway fan-out — multi-provider consensus. A single review request enters the Gateway, fans out in parallel to multiple hosted and self-hosted models, returns into a quorum check and an independent meta-judge, and lands as one verdict that gates the PR. Review requestone PRGatewayFAN-OUTMETERED · VIRTUAL KEYSClaudehostedDeepSeekhostedOllamaself-hostedvLLMself-hostedQuorumproviders must agreeMeta-judgerules on the set ONE VERDICT · GATES THE PR

The gate speaks in artefacts.

A designed extract of a real ai-review comment on a real PR in this repo. Quoted historical results from PR #17, 4 September 2026 — not live figures, not wired to any live API in this step.

ai-review-botcommented on PR #17 · 4 September 2026

96/100out of 100

AI Code Review · Excellent · cluster az · 5 reviewers · quorum met

ReviewerModelScoreWall
claudeclaude-opus-4-81007.58s
deepseekdeepseek-chat964.32s
qwen self-hostedqwen2.5-coder:7b9524.47s
codestralcodestral-latest952.33s

Findingdeepseeklanding.component.ts:1024

"Magic number 200 used in endAt calculation is not from the named constants block."

The same change on gcp ran 3 reviewers — no local GPU there, so no qwen. Different clusters, different reviewer sets, one verdict each.

Coverage

PASS

72.0%

threshold 30.0%

Dependency scan

PASS

Grype 0/0/0/0

crit / high / med / low

Security scan

WARN

Semgrep 4 warnings

Gitleaks 0 secrets · 0 errors

A gate that only ever goes green isn't a gate.

Not an AI developer. An autonomous delivery team.

ShipProven isn't a single coding assistant. It's a delivery organisation of six role-specialised agents — BA, Dev, QA, Infra, Scrum-Master and Design — all subordinate to the same pipeline no agent can bypass. Each has a job; none can self-certify.

Agent org and Axon bus. Six agent nodes — BA, Dev, QA, Infra, Scrum-Master and Design — connected to a central horizontal Axon bus. All six run Live. The Infra to BA handoff is highlighted in accent green. Axon — native K8s agent busBAframes the workLIVEDevholds its PRLIVEQAdrives qualityLIVEInfraspots · spawnsLIVEScrum-Masterretros → PlansLIVEDesigngenerates · verifies UILIVE INFRA → BA (SPAWN) Self-directing agents create their own Plans — never their own approvals
A delivery organisation on one shared bus. All six agents run Live — Design shipped 2026-08-11 with this site.

BA agent

Live

Turns requirements into structured, buildable work. Frames the problem so the rest of the team can act on it.

Dev agent

Live

Writes the change, opens the pull request, and iterates against the pipeline until every gate is green — holding its own PR the entire time.

QA agent

Live

Drives quality signals and validation, ensuring changes are exercised the way real usage would exercise them before they're allowed near production.

Infra agent

Live

Confirms the release actually shipped and checks cluster health after merge.

Scrum-Master agent

Live

Runs the retrospective and feeds every lesson back into the shared memory that steers the whole team.

Design agent

Live

Generates and verifies the UI — brand tokens, component visuals, accessibility contract — and holds its own PR against the same pipeline as every other agent. First shipped output: this site.

The advantage compounds.

Owning the whole pipeline means owning the data nobody else can get.

The flywheel — every release makes the next one safer. A closed clockwise cycle of five stages. Generate proposes a change. Gate applies deterministic checks and a model quorum. Ship releases into production. Outcome asks whether it worked in production — the step only a system that ships the code can see, drawn with heavier visual weight than the others. Corpus captures the review decisions and run records. An arrow labelled "better models" closes the loop from Corpus back to Generate. better modelsGeneratepropose a changeGatedeterministic checks + model quorumShiprelease into productionOutcomedid it work in production?the step only a system that ships the code can seeCorpusreview + runtime signals
The flywheel — five stops that make the moat self-reinforcing. The outcome step is the one only a system that ships the code can see, and it feeds the corpus that trains the next generation.
Flywheel stops, in order.
StopWhat happens
GeneratePropose a change.
GateDeterministic checks + model quorum.
ShipRelease into production.
OutcomeDid it work in production? The step only a system that ships the code can see.
Corpus → GenerateReview decisions and run records feed better models.

Generating code is solved; the bottleneck moved to verifying it. But to learn from verification you need to know outcomes — did this change actually work in production.

A code-review tool sees a diff. An IDE sees keystrokes. A consultancy sees a ticket. Only a system that ships the code knows whether it worked. ShipProven owns generation, gating, delivery and runtime — so every change yields a review decision, a run record and an outcome.

That is a proprietary training signal that cannot be bought, scraped or licensed. It is structural, not a head start.

Every release makes the next one safer. The advantage widens with usage instead of eroding when the next frontier model ships.

A corpus that compounds — per seat.

Every developer working through ShipProven generates about 35 reviewed decisions a day, and roughly one labelled production outcome — measured on our own build. A ten-person team produces around 88,000 reviewed decisions a year. A thousand-developer engineering organisation produces 8.75 million, and clears the threshold for a model fine-tuned on its own conventions within a day of going live. The signal scales with the seats you deploy, and it is signal nobody outside the pipeline can see.

We own the model layer.

Our own gateway, our own GPUs, our own weights — deployable in the customer's cloud, so models improve on their conventions and their code never leaves their estate. Model-agnostic by design: when frontier models get cheaper and better, we get cheaper and better.

Every run is measurable.

Any autonomous run can be reconstructed from logs after its pods are gone — tool mix, turns, errors, wall clock, cost — and agent behaviour is attributable to a prompt version. One prompt change moved an agent's wait-to-tool ratio from 2:7 to 8:1. We tune on measurement, not intuition.

Already in the loop.

A model we trained runs in the pipeline today as an advisory pre-check, with a daily eval gate and a weekly eval-gated retrain.

Annual reviewed decisions by team size — the corpus scales per seat. A single-series log-scale line chart. One developer working through ShipProven generates about 8,750 reviewed decisions a year — measured at 35 a day across 250 working days. A ten-person team produces around 88,000 a year. A hundred-developer team produces around 875,000 a year. A thousand-developer engineering organisation produces 8.75 million — enough to clear the threshold for a model fine-tuned on its own conventions within a day of going live. A single endpoint marker sits beside the label "8.75M / year". 1K10K100K1M10Mreviewed decisions / year (log scale)8.75M / year1 dev10 devs100 devs1,000 devsTeam size
Annual reviewed decisions by team size — one measured rate (35/day per developer) projected across four illustrative team sizes. One series, one axis, log scale — the shape is the argument.
Annual reviewed decisions by team size.
Team sizeReviewed decisions / year
1 dev8,750
10 devs88,000
100 devs875,000
1,000 devs8,750,000

The corpus is the training signal for models fine-tuned on each customer's own conventions, gated by the same evals before promotion. That is the natural destination of the flywheel — every release sharpens the signal, and every gate protects the code that trained on it.

Generation-first tools trust the model. We trust the gate.

Generation-first toolsShipProven
Trust the output because "the model made it" Trust the output because it survived the same production gate a human PR must
One LLM reviews another LLM Deterministic gates + multi-provider consensus + an independent meta-judge
Captive to frontier-API pricing Own the gateway — own the cost and a fine-tuning flywheel on your own conventions
A single coding assistant A governed delivery team: BA, Dev, QA, Infra, Scrum Master
Built for the individual developer Built to the standard regulated enterprises demand — for any team that ships software. Multi-cloud, data residency, per-tenant audit and budgets, from self-serve to full BYOC.
Their cloud, their rules Your cloud, your data residency (BYOC)

Nobody optimising for generation speed does provider-diverse consensus review or pipeline-as-judge governance. That's the open lane, and it's the one we're built for.

Made for teams that cannot ship on trust alone.

ShipProven is fintech-native — built and hardened inside a regulated software organisation, because banks, fintech and healthcare cannot adopt on trust alone. That is our beachhead. The governance isn't bolted on; it's the architecture — and it travels with the product to every team that ships software.

Cloud-agnostic / BYOC

Runs in your cloud, not ours. The pipeline and gateway operate cloud-agnostically across Azure and GCP today.

Data residency

Your code and model traffic stay where compliance requires.

Multi-cloud by design

The same governed delivery system runs across clouds.

Auditable by construction

Every safety guarantee is an explicit, enforced pipeline step, and every model call routes through the gateway.

Per-tenant budgets and metering

Exact cost metering and hard per-tenant budgets, enforced at the gateway.

Consensus as a compliance artifact

Multi-provider quorum plus meta-judge review gives compliance teams something concrete to sign off against.

Built with ShipProven.

The last wave of software was cloud-native. The next is AI-native and MCP-native. In finance, the wave after is chain-native. ShipProven builds it in by default — and everything it produces passes the same governed pipeline + consensus review before it ships.

The two builds below are live, fully-built demonstrations — proofs of capability, not customer deployments and not trading companies. We shipped them through ShipProven itself so you can point at the running site and the running pipeline in the same sentence.

We ship a new proof regularly — each one built through the same governed pipeline you see running on this repo.

Two ways to run ShipProven.

Pricing scales with how you want to deploy. Talk to us for a figure tailored to your estate.

Tier 1

Enterprise (BYOC)

For banks and regulated organisations.

Runs inside your own cloud — full data residency, per-tenant audit and budgets, consultancy-assisted onboarding.

Tier 2

Direct

For teams that want ShipProven without running the infrastructure.

Multi-tenant, hosted on our self-hosted cluster; lower cost from low marginal inference cost.

No public price list.

See autonomous code that can't ship unsafe.

Book a technical walkthrough and we'll show you the pipeline, the consensus review, and the agent team on a real pull request.