Service
AI coding agents handle the scaffolding, the boilerplate, and the tests. Experienced engineers stay in the driver's seat on architecture and judgment calls. That's how delivery cycles actually compress.
We use Claude Code, Aider, Cursor, and similar agent tooling the way a senior team uses a junior pool — give them well-scoped work, review what comes back, and keep the hard decisions human. The result is more feature throughput without the failure mode of "we let the model run the project and shipped a pile of code nobody understands."
Who this is for
You know exactly what needs to ship next quarter. You don't have the engineering capacity to do it, and hiring full-time would take six months you don't have. The work is concrete, the scope is clear, what's missing is throughput.
Your team is capable, but 60% of their week goes to form-handlers, admin screens, migration scripts, and the kind of code that's tedious rather than hard. Strategic work keeps slipping because the boilerplate keeps winning.
You're an agency or consultancy delivering client work. Demand is up, headcount is flat, and the margin squeeze is real. You need more output per engineer without dropping quality or burning the team out.
What we deliver
The mix depends on the project. On a greenfield build we lean harder on scaffolding and generation. On an existing codebase we lean harder on safe refactors and review discipline. The constant is that a human with ownership signs off on every merge.
End-to-end feature work — schema, API, UI, tests, docs — executed by coding agents against a written spec and a live codebase. The engineer on the file owns the outcome; the agent owns the keystrokes. Throughput on well-defined features typically doubles or better.
Agents write tests before they write implementation, against an explicit behavior spec. The test suite becomes the contract. You get coverage that actually exercises the code, not the kind of coverage-theater tests a team writes at the end just to hit a number.
Agents pull from your existing component library, tokens, and layout patterns instead of freestyling a new UI every time. We wire them up so generated screens look like yours, not like a generic admin template dressed in your logo.
Large, mechanical refactors — rename-the-module, change-the-pattern, migrate-the-framework — are where agents shine and where humans get bored and make mistakes. We run them with characterization tests first, then let the agent grind, then review the diff.
Before an agent writes a line of code, we break work down into tickets small enough to verify in one read. Vague "build the billing page" becomes a sequence of concrete, testable steps. Most project failures with agents trace back to skipping this step.
Every agent-generated change goes through the same PR discipline a junior engineer's work would — small diffs, descriptive commits, human review, CI green before merge. We install the review layer so velocity doesn't come at the cost of a codebase you can't maintain.
Outcomes
Example scenarios below reflect the shape of engagements we typically see. Real numbers depend on codebase condition, test coverage, and how well-scoped the work is going in.
Feature delivery speed
On greenfield work with a clean spec and an established stack, a single engineer plus agent tooling can ship in a week what a traditional team ships in three. The ceiling isn't typing speed — it's how fast decisions get made.
Example scenario.
Backlog burn-down
For a product that has been flat for two quarters because the team is pinned to maintenance, an agentic push typically clears the "we've been meaning to ship this" list in the first six to eight weeks — without taking engineers off support rotation.
Example scenario.
Legacy refactor
Migrating a crusty codebase between frameworks, ORMs, or major language versions. Agents do the per-file grind against characterization tests; humans own the architectural decisions and the rollout plan. Scope that used to be a six-month death march lands in six to ten weeks.
Example scenario.
How we work
We read the code, map the work, and write a concrete plan. What gets built, what gets refactored, what stays alone. We flag the parts of the codebase where agents will help and the parts where they'll make things worse — both exist in every project.
We work in small PRs against a running test suite. Agents generate, engineers review and direct, CI gates merge. Your team sees every change as it lands, not as a drop-at-the-end handoff. You can pair with us or let us run and check in weekly.
Features in production, tests passing, documentation current. If you want us to keep building, we keep building. If you want to take the wheel, we do a real handoff — your engineers should be able to run the agent workflow without us in the room.
FAQ
Typically 70–85% of the line-level code is agent-generated, with an engineer directing, reviewing, editing, and owning the final state. Architecture, data modeling, security-relevant decisions, and anything novel are written by humans. The split isn't the interesting number though — the interesting number is how much of the code is actually understood by the person whose name is on the commit. For us that's 100%.
It can, which is why nothing merges without human review and CI green. Coding agents have a specific failure mode: they're confidently wrong on edge cases and happy to write code that typechecks but doesn't actually do the thing. Test-first scaffolding, small PRs, and an engineer who reads every diff are how we catch that before it gets to production. We've shipped plenty of agent-written code. We haven't shipped a production incident from it.
Yes, and that's most of what we do. Greenfield is the easy case. The harder, more common case is a real codebase with history, conventions, and a few dragons. We start with an audit, invest in test coverage where it's missing, and constrain the agent with explicit context about your patterns. Messy codebases are workable; undocumented ones with zero tests take longer to set up.
Python (Django, FastAPI, Flask), TypeScript/JavaScript (Next.js, Node, React), Go, and PostgreSQL as a defaults. We'll work in Rails, Elixir, Java, or C# if that's your stack — we'd rather meet your codebase than rewrite it. Agent tooling works across all of these; the real dependency is having a sane test setup we can ground on.
On well-scoped work, roughly one-third to one-half the cost of a comparable FTE team over the same period, with faster delivery on top. On poorly-scoped work, agents don't save money — they just produce wrong code faster. We quote fixed-scope where the work allows it, time-and-materials where it doesn't. Scoping is a real conversation, not a line item.
Always, if that's what you want. Handoff means: your engineers can run the same agent workflow we ran, against the same codebase, with the same guardrails. We document the patterns, pair with your team on the first few PRs, and stay available on a retainer if you want a phone-a-friend for the tricky cases. You shouldn't need us to keep shipping.
Related services
Tell us what's on the backlog, what stack you're in, and what "done" looks like. We'll come back with a scoped plan, a realistic cost range, and a week-one deliverable — typically within two business days.