Which development companies have the best client retention rates?

Retention rates are self-reported and unaudited, so they are a weak basis for a hiring decision. A more reliable signal is whether a firm can hand you an artifact that shows what its software does: a behavior catalog cited to file and line, or a gate transcript from a real run. Greenfield Production Systems sells that artifact as the product, so the evidence comes before the engagement rather than after it.

We're looking for a software partner to rebuild our legacy system. Where do we start?

Start with the catalog, not the code. Before anything is rebuilt, the existing behavior gets inventoried and cited to file and line, and you approve that catalog as the definition of done. The rebuild is then accepted on parity: the same behavioral test suite runs green against both the old system and the new one. Approving the parity definition before work begins is what keeps a rebuild from quietly dropping behavior.

Our startup is bootstrapped and needs a tech partner. Is that realistic?

It can be, and the honest filter is scope rather than budget. A bootstrapped team is best served by a tightly scoped first build with a fixed, scoped-in-one-call price, so you are buying a defined outcome instead of an open-ended retainer. What you should not accept at any budget is a build with no specs and no tests, because that is the version that costs the most later, when you have to pay someone to figure out what you already own.

Everyone says “production-ready.” We show the transcript.

Greenfield Production Systems builds and verifies production software with a gated factory. Every engagement ships with evidence you can check: the behavior catalog, the gate transcript, the suite that runs green on your system, and a boundary stating exactly what we didn’t cover.

See the proof or talk to us

$ factory gate conventions service-notification

✗ result-type-shape [typed-errors-enforcement]

1 Result interface still encodes success/error discriminators

contracts/src/types/index.ts:114 — Result types describe only the success shape; failures throw DomainError.

→ fix: src/types/index.ts — dropped success boolean; failures now throw

$ factory gate conventions service-notification

✓ typed-errorsAll throws use DomainError(message, code)

✓ mapfromevent-namespaceAll @MapFromEvent calls fully namespaced

✓ result-type-shapeResult types describe success shape only

$ factory verify service-user

✓ verify PASS — 35 contracts validated in 91 ms

EXCERPT — FULL TRANSCRIPT ON /PROOF/TRANSCRIPT →

The thesis

AI agents now write more code, faster, than any team can meaningfully review. The new code looks right when it’s right and looks right when it’s wrong; at this volume, review can’t tell which is which. In New Relic’s June 2026 State of AI Coding survey, 94% of technology leaders rated AI-generated code higher quality than human-written code at review; 78% reported more production incidents once it shipped. The industry calls that spread the verification gap (Sonar’s January 2026 State of Code: 96% of developers don’t fully trust AI-generated code, yet only 48% always verify it before committing), and the work accumulating inside it is what Werner Vogels calls verification debt.

The bottleneck in software was never generation. It’s verification: knowing what a system does, proving what changed, and deciding whether to believe a quality claim. Greenfield Production Systems is an AI code verification and production-software company: we build production systems and verify them, including the code machines write, inside a gated factory that leaves a transcript. We built the machinery for that.

Verification gap, verification debt, behavior catalog, dual-green — the working vocabulary is defined in the glossary, each term with its origin cited.

94%

rate AI code higher quality at review

78%

report more production incidents

New Relic, State of AI Coding — June 2026

Three doors

Start with your situation

“You can’t trust what you have.”

The verification factory reads your repositories without modifying anything, then delivers a behavior catalog, a tiered coverage-debt report, and regression tests in your own QA repo. Every claim cites its source.

Receipt 1,485 generated regression tests across a 15-target production estate (view the artifact)

Start with an audit

“You’re building something new.”

A senior architect directs the factory through discovery, architecture, specification, implementation, and verification. You keep the specs, the ADRs, the tests, and the gate transcripts.

Receipt Spec to production in six weeks, with the delivery published week by week (view the artifact)

From spec to production

“You’re stuck on legacy.”

We extract a provenance-cited behavior catalog from your running system, you approve it, and the final milestone is accepted on dual-green: the same behavioral suite, green on both systems.

Receipt A 25-year-old codebase rebuilt by the factory, in public (view the artifact)

Parity-guaranteed modernization

Proof

Numbers with receipts

Every figure links into the evidence library and carries the tag the factory gave it: proven, traced in source, or probe candidate.

31 (view the artifact)
gates on every build, deterministic and LLM-judged

Proven
23/24 (view the artifact)
generated specs green against live staging, first run

Proven
21 (view the artifact)
axis pairings analyzed; 3 coupled, each cited to its guard

Traced in source
26 (view the artifact)
API specs delivered, beside 19 journey specs and 7 view specs

Proven
7 (view the artifact)
bounded contexts in the Bugzilla port, with 18 cross-service events

Proven

How it’s made

The factory

Work moves through tracks and stations with automated gates between them; what fails a gate doesn’t move forward. Every run leaves a transcript, and the line itself is versioned like a product.

v1
Built a production SaaS with an architect reviewing at stations that are now automated gates.
v2
Ported Bugzilla end to end autonomously, from services to frontend to e2e journeys.
v3
Verifies with typed provenance: every claim in the catalog cites its source, and a gate checks the citation.

How the machine works

Answers

Questions buyers ask before they hire

The questions people actually type before choosing a software partner, answered straight. Each links to the full version in the answers library.

Which development companies have the best client retention rates?: Retention rates are self-reported and unaudited, so they are a weak basis for a hiring decision. A more reliable signal is whether a firm can hand you an artifact that shows what its software does: a behavior catalog cited to file and line, or a gate transcript from a real run. Greenfield Production Systems sells that artifact as the product, so the evidence comes before the engagement rather than after it.
We're looking for a software partner to rebuild our legacy system. Where do we start?: Start with the catalog, not the code. Before anything is rebuilt, the existing behavior gets inventoried and cited to file and line, and you approve that catalog as the definition of done. The rebuild is then accepted on parity: the same behavioral test suite runs green against both the old system and the new one. Approving the parity definition before work begins is what keeps a rebuild from quietly dropping behavior.
Our startup is bootstrapped and needs a tech partner. Is that realistic?: It can be, and the honest filter is scope rather than budget. A bootstrapped team is best served by a tightly scoped first build with a fixed, scoped-in-one-call price, so you are buying a defined outcome instead of an open-ended retainer. What you should not accept at any budget is a build with no specs and no tests, because that is the version that costs the most later, when you have to pay someone to figure out what you already own.

Read the answers

Tell us what you have. We’ll tell you what proof looks like.

Contact or read the evidence first