When code generation is instant, the bottleneck moves from writing code by hand to direction and review. We changed our delivery to match: AI generates the code, senior engineers direct it, judge it, and ship it. The output is finished software you own.
A senior engineer maps what you need and writes the spec the AI builds against.
All the code is AI-generated against that spec, across the full stack.
Every change is reviewed by a separate adversarial agent and then approved by a senior engineer. Generation is fast; the gate is human.
Finished, documented, tested, and yours, repo and IP included.
The difference between AI that underperforms and AI that delivers is not a cleverer prompt, it is context. We give the AI two things before it writes a line: how the system is today, and what it should do. Without them the AI improvises; with them it builds against reality.
The engineering brain: a live knowledge graph of your code and infrastructure, refreshed with each commit.
The specification: your intent and the project's rules, written down and reviewable.
We use Spec Kit (open source, built to work with Claude Code and the major AI coding agents), so the spec generates the implementation, it does not just guide it. The workflow is a set of versioned artifacts that live in the repo and are reviewed in the PR like any code. One spec feeds implementation, tests and QA from the same acceptance criteria, so there is no second, hand-written test plan to drift.
constitution generated from your real codebase patterns · clarify & analyze optional
Same AI-generated, senior-judged delivery, in whatever shape fits your team.
We take the build end to end and hand you finished, owned software.
Our full-code-generation engineers join your team and ship inside your sprints. Staff augmentation, evolved: AI-generated, expert-directed and reviewed.
We embed specialists who turn the manual and repetitive parts of your SDLC (testing, CI/CD, integrations, data plumbing, releases, internal tools) into AI-driven automation, and they ship application code too.
A senior engineer embeds, runs discovery, owns the spec, directs and judges the AI, and operates the result. One accountable owner, end to end.
From a ticket to a running regression test, with one human gate.
Across roughly ten repositories, test coverage moved from single digits to the high nineties, with thousands of generated tests merged and passing in CI. Regression cycles that took days now take minutes, and the automation surfaced real bugs and secrets manual review had missed.
A requirement becomes a spec, the AI implements it and generates the tests, a developer approves (the one required gate), it deploys on your CI/CD, and a QA card is generated for whatever needs a real environment.
An AI coding agent with full repo access reads the code, decides what to test, writes and runs the tests, verifies the result, and hands off what it cannot cover.
Unit, integration, contract in CI on every PR; end-to-end against the deployed environment in a real browser.
An agent drives a real browser through the full journey. You give it the goal, it discovers the steps, and leaves evidence. Selectors self-heal when a third-party UI changes.
The automation handles execution; the QA engineer reviews the spec for coverage, audits the evidence, and approves the self-heal diffs.
The methodology behind every claim on this site runs today in real client engagements, not in a lab.
Judgment, architecture, and accountability stay with people who have shipped for years.
Code, IP, repo. No licensing back, no platform lock-in.
Engineering teams across several countries, with years shipping production software.
Finished software you own on its own, and every project is built on an engineering brain: a live knowledge graph of your code and how it connects, decisions, rules and tests included. It keeps the software maintainable and extensible, evolving with your company, and it becomes another module your Company Brain inherits.
Learn about the Brain →Powered by the Neocortex. The intelligence layer that ships with every brain and agent we build. It arrives knowing the job and keeps getting smarter with every deployment.
Meet the Neocortex →We work in your stack when you have one.
Claude (API, Code, Agent SDK), MCP servers, multi-agent orchestration, with OpenAI and Gemini where they fit.
TypeScript (Node, Hono, Express) and Python (FastAPI). Next.js and React.
Postgres and pgvector, Redis, vector DBs, Neo4j for graph.
Vapi, Retell, ElevenLabs, vision and document pipelines.
Eval suites and holdout testing, Langfuse and Helicone, Sentry and Datadog.
AWS and GCP, Firebase, Docker, Playwright for end-to-end tests.
A leading SaaS in edtech (client): an entire 66-repo codebase moved to a Claude-native, spec-driven standard in a 2-day sprint, with an engineering constitution generated from the real code, an automated security audit, full database documentation, and a QA automation suite started.
A leading SaaS in association management (long-running client): started as classic staff augmentation, their developers now on a Claude Code standard with Spec Kit, led by our embedded engineers, with custom skills merged into the client's product and full code generation for QA test automation expanding across their products.
One proven methodology across both: the same standard (constitution, Spec Kit, skill library, adoption tracking). Most of the code we deliver today is AI-generated.
Scope a project in one working session. Scope: defined per project, in the working session.