Services
AI-Assisted Software Development with Human Review
We use AI tools across planning, coding, reviewing and testing — under written engineering standards and mandatory human review — to shorten delivery without lowering the quality bar.
Overview
Why AI-Assisted Development matters
AI coding tools can produce a lot of code quickly, but without guardrails they also produce inconsistent patterns, hidden security gaps and tests that prove nothing. Our AI-assisted software development approach puts those tools inside a disciplined workflow. Engineers use AI to explore requirements, draft plans, write boilerplate, suggest tests and pre-review changes, while our written standards define exactly how code must be structured, validated and secured. Every change is still read, understood and approved by a developer before it merges, and CI gates must pass. The result is faster delivery with the same predictable quality you would expect from a careful senior team.
What we build
AI-Assisted Development: capabilities
Every feature is built on our standardized stack, so it is secure, tested and maintainable by any engineer in our pool.
AI-supported discovery and planning
Requirements notes, process descriptions and existing code are summarized with AI help so engineers can propose a scope and plan sooner.
Accelerated implementation
Repetitive work such as DTOs, Zod schemas, CRUD modules and form wiring is drafted with AI and refined by the engineer.
AI pre-review of every change
Pull requests get an automated first pass for missed validation, inconsistent naming and risky patterns before a human reviewer looks.
Test generation and gap analysis
AI suggests unit and API test cases, including edge cases and unauthorized-access scenarios, which engineers verify and keep.
Documentation that keeps pace
Module overviews, API descriptions and handover notes are drafted alongside the code so documentation does not fall behind.
Legacy code understanding
AI helps map unfamiliar older codebases, trace data flows and outline a safe migration path before changes begin.
Standards encoded for AI tools
Our engineering rules are written in a form AI assistants follow, so generated code matches the same architecture every time.
Key benefits
Outcomes your business can count on
Shorter time-to-market
Less time on boilerplate and searching means more engineering time spent on the parts of your product that are genuinely unique.
The same quality bar
Linting, type checks, automated tests and human review apply to AI-drafted code exactly as they do to hand-written code.
Consistent code across the team
AI tools follow the same written standards as our developers, which reduces stylistic drift between contributors.
Better test coverage
Generating candidate tests is cheap, so engineers can cover more edge cases and failure paths than schedules usually allow.
Accountable engineers
A named developer owns every change. AI is a tool in their hands, not an unsupervised author of your software.
Technology used
Built on one proven stack
AI assistance is applied on top of our standard delivery stack — Next.js, NestJS, PostgreSQL, Prisma and a strict TypeScript toolchain — with CI gates for lint, typecheck, tests and build that every change must pass.
- AI coding assistants
- TypeScript (strict)
- ESLint + Prettier
- Vitest, Supertest, Playwright
- CI gates: lint, typecheck, test, build
- OpenAPI-generated clients
Our process
From first call to confident launch
- 01
Discovery
Engineers use AI to digest documents, interviews and existing systems quickly, then validate the summary with you and confirm business rules directly rather than guessing.
- 02
Design
A short written plan lists the files to change, the approach and the risks. AI helps draft it; a senior engineer reviews and approves it before coding starts.
- 03
Agile Build
Developers implement with AI support, run lint, typecheck and tests locally, and open small pull requests that receive AI pre-review followed by human review.
- 04
Production + 90-day hypercare
Releases follow the normal CI pipeline. During 90 days of hypercare, AI helps triage logs and error reports while engineers decide and apply the fixes.
How AI-assisted software development works in our team
We treat AI as a capable junior collaborator that never gets tired and never gets the final say. Our engineering standards — the same rules that govern our web and backend stack — are written so both people and AI tools can follow them: which framework owns which responsibility, how inputs are validated, how permissions are checked, how database changes are made and what must be tested. When an AI assistant drafts code, it is drafting against those rules.
The engineer remains responsible for the outcome. For anything that touches more than a few files, they first produce a short plan describing the approach and risks, and get it approved. They state assumptions explicitly, ask when a business rule is unclear, and make the smallest change that solves the problem. AI speeds up each of those steps; it does not replace the judgement behind them.
Guardrails that protect quality and security
Speed is only useful if the result is safe to run in production. Every change, regardless of who or what wrote it, must pass linting, strict TypeScript checks, unit tests, API tests and a production build in CI. Our standards forbid shortcuts that AI tools sometimes reach for: no `any` types, no disabled lint rules, no suppressed type errors and no empty catch blocks that hide failures.
Security rules are equally firm. Server-side Zod validation, role-based guards with record-level checks and parameterized database queries are mandatory, and reviewers specifically look for places where generated code might skip them. Secrets are never pasted into prompts, and client code or data is handled according to the confidentiality expectations agreed for your project.
- Human approval required on every pull request
- Bug fixes include a test that fails without the fix
- No new dependency without a written justification
Where AI-assisted delivery makes the biggest difference
The gains are largest in projects with a lot of structured, repeatable work: admin panels, CRUD-heavy modules, integrations with well-documented APIs and test suites for existing code. They are also significant when we inherit an unfamiliar codebase, because AI tools help engineers read and map it faster. Novel algorithms, sensitive business rules and architectural decisions still get the same careful human design time they always have.
This way of working applies to all of our delivery, from custom software development to a dedicated development team. If you want AI inside your product rather than in our workflow, see our AI services instead.
Industries served
Proven across industries
FAQ
AI-Assisted Development: frequently asked questions
Want faster delivery without cutting corners?
Talk to us about your roadmap. We will explain how AI-assisted delivery would apply to your project and where it helps most.