Build your
Frontend factory.

Delivery infrastructure we install on your codebase so an idea becomes a merged production PR without a senior Frontend engineer hand-carrying every one. In practice: architecture and context committed to the repo (AGENTS.md, patterns, design tokens), skills we write and maintain per repo, guards that decide whether output is mergeable (design system, accessibility, tests, Storybook, senior review), telemetry and evals on every run, and a learning loop where each failure becomes a new guard or a sharper skill. Our forward-deployed engineers install it, operate it with your team inside it, and hand it over. Ownership is the deliverable, not a permanent retainer.

People who could not merge Frontend before. Backend engineers, PMs, designers, marketing and CS open real PRs on the real codebase, and the Frontend team stops being the queue for every small UI ask. Teams usually come to us through one of three doors. Capacity: people outside R&D ship. Velocity: the demo that impressed everyone reaches production instead of aging in a branch. Force multiplication: your one great senior runs many parallel tasks instead of one. Same machine behind all three, so pick the door that matches the pain you have this quarter.

Because a coding agent writes code, and your bottleneck is merging it. In-house rollouts stall for the same reasons every time: the rules live in senior engineers’ heads, nothing enforces them, and nobody measures whether output actually got better. The factory makes the rules physical. Gates run on every agent run, deterministic checks first and LLM evals second, and the PR is not opened until they pass. When the same failure repeats, we fix the factory rather than the code, so the guard or skill that catches it is inherited by every run after it. That is the part you cannot buy off the shelf, and it is what decides whether the tools you already pay for turn into shipped product.

The bottleneck is structural. AI-generated PRs are longer, more scattered, and less trusted, so reviewers wait longer to pick them up and spend more time inside them. The factory attacks it from four sides at once. Structured specs constrain what agents produce, so PRs land smaller and more focused. Generated tests and quality gates run before the PR exists, not after a human opens it. A review agent runs first, so senior reviewers only see what needs human judgement. And shared AGENTS.md context in the repo keeps agents from re-litigating the same review comments across sessions. The result: AI-generated PRs re-enter the same lane as human-written ones, and your senior reviewers stop being the single-threaded merge queue.

Four stops: audit, pilot, run, own. The audit reads your repo, workflow and team to find where the hours actually go and what an agent can safely own. The pilot runs inside one team, never org-wide on day one, against a single KPI written down before we start (a named non-Frontend person merges a production PR, demo-to-production cycle time against your own baseline, or throughput through the line). Then we operate the factory with your team inside it while we tune the gates, and hand it over with playbooks, configs and people who can run it without us. Phased fixed price. The first real output is a merged PR from your pilot team, not a slide.

Yes. We work inside your current codebase, design system, routing and CI, not a greenfield rewrite. Same stack, same conventions, same review process, and the security envelope is agreed before the first agent run, including restricted and on-prem environments. Everything the factory adds is committed to your repo: AGENTS.md context, the skills library, the guards, the review agent and the eval set. It stays when we do not.

All of them. The coding agent is the engine, and the engine is the part anyone can buy. We install the harness on top of whichever one your team already runs: Claude Code, the Claude Agent SDK, Cursor, Codex or GitHub Copilot. The harness is engine agnostic, so the same context structure (AGENTS.md, CLAUDE.md, Cursor rules), the same guards, the same review agent and the same evals apply either way. Teams frequently run more than one agent in the same repo, and we tune the setup so they compose instead of colliding.

A model release is a drill, not a crisis. We re-run the eval set against the new model and ship updated skills to every client within days, so you learn what changed from us and not from a bad merge. Dependency is designed out from the start: the factory is your repo, your gates, your playbooks, operated in the open with your team in the loop from the pilot onward, and handed over at the end. Some clients keep us on an operation retainer to stand up new lines and keep the gates sharp. That is a choice they make after handover, not the shape of the deal.

Completely. Embedded senior Frontend engineers are a delivery model we run alongside the factory, and plenty of engagements are simply strong React and React Native engineers shipping product on your team. They are mid-senior to senior (3+ years, typically 5+), already AI-native with Claude Code, Cursor and MCP, and they usually start within days of a signed scope and ship real code in the first week. We do not place juniors who learn on your time. They also arrive carrying factory tooling, so the factory itself is an upgrade you can choose later, never a requirement.

That is our other service line, Frontend AI: the factory is how your org ships, Frontend AI is what your product does. An MCP app puts your product inside AI assistants like ChatGPT and Claude as a real, usable interface, not just a text mention. When someone asks an assistant to book, buy or look something up, your product can show up as live UI and complete the task right there in the chat. If your customers start their journey in an assistant, you want to be the app it calls, not the website it skips. We build to the current MCP and MCP-UI protocol stack, in three layers: read-only data, personalized signed-in data, and full transactions.