
Generic AI code assistants generate plausible code — but not code that follows your conventions. Custom AI coding agents are trained on your repository, coding standards, and architectural patterns. They write code that looks like your senior developers wrote it.
Copilot and ChatGPT generate code. But it uses libraries you don't use, follows conventions you don't follow, and structures that don't match your architecture. Developers spend as much time fixing AI code as they saved generating it.
The problem isn't the model — it's the lack of context. A generic assistant doesn't know your API patterns, error handling, state management, or deployment constraints.

Code generation agents write features following your standards, using your patterns and libraries. Code review agents check PRs against your guidelines for naming, security, performance, and architecture. Test generation agents create tests matching your framework and assertion style. Refactoring agents identify improvements with before/after diffs.
All agents operate through your Git workflow — creating branches, opening PRs, and responding to feedback.
Analyze repository structure, patterns, conventions, and CI/CD pipeline. Identify highest-value areas.
Configure agents with your codebase context, directory structure, conventions, and constraints.
Validate output against your standards on real PRs and features. Calibrate until output matches quality bar.
Deploy into IDE extensions, PR automation, and team channels. Train team on effective usage.
No commitments. Tell us what you need and we'll tell you how we'd solve it.
Challenge: 4-person team needed to ship faster but couldn't hire — code review was the bottleneck
Solution: PR review agent + code generation agent handling boilerplate from Jira tickets
Result: PR review time from 4 hours to 30 minutes; feature velocity increased 35%; 12 security issues caught in first month
Challenge: 500K+ line legacy codebase needed modernization but devs avoided refactoring
Solution: Refactoring agent identifying safe modernization opportunities with behavioral equivalence tests
Result: Refactoring velocity 5x; tech debt decreased 22% in 3 months; zero regressions
Challenge: Component library inconsistencies across 8 developers — same patterns implemented 5 ways
Solution: Code generation agent creating components per established patterns, review agent flagging deviations
Result: Consistency improved from 62% to 94%; new component development time reduced 40%
Challenge: Test coverage 23% on data pipelines — writing tests was tedious
Solution: Test generation agent creating comprehensive tests: schema validation, transformations, edge cases
We build agents on Next.js 16 + Payload CMS 3 + PostgreSQL — the same stack our own production AI systems run on. Server Actions handle tool orchestration, PostgreSQL stores agent memory and state, and Payload manages configuration through an admin UI your team can use without touching code.
Claude and GPT-4o aren't services we resell — they're tools we use every day to build software, generate content, and run internal operations. Our AI coding agents write production code. Our content pipeline generates and publishes articles autonomously. We build AI agents because we are an AI-native team.
Self-hosted infrastructure means your data stays where you control it. No vendor lock-in to SaaS platforms that can change pricing or terms. Full PostgreSQL audit trails, your own backups, and GDPR compliance built into the architecture.
Strategy, architecture, development, deployment, and ongoing support — all from one team. No handoffs between consultants, designers, and developers. The engineers who build your system are the same ones who maintain it.
Our own operations are automated end-to-end: CI/CD pipelines, infrastructure monitoring with Telegram alerts, daily database backups, automated content publishing, and AI-assisted development workflows. We build automation for clients because automation is how we run our own business.
Single-function agents start at $20,000-$30,000. Multi-function suites range from $35,000-$60,000. Enterprise deployments cost $60,000-$100,000+. LLM API costs run $500-$3,000/month per team.
Agents are configured with your security policies and check for common vulnerability patterns. We recommend AI as a first pass, with security-critical code reviewed by a human.
Enterprise AI API agreements with data handling protections. For maximum security, we can deploy self-hosted models on your infrastructure.
Custom agents achieve 70-85% daily usage because output matches team standards. Generic assistants see only 30-40%.
Tell us about your needs and we'll design a custom ai coding agents solution for your business.
Free consultation · Custom solutions · Expert team
Result: Coverage from 23% to 71% in 6 weeks; 8 latent bugs discovered; pipeline failures reduced 55%
Fixed-price engagements with defined deliverables at each milestone. AI projects have inherent uncertainty, so we scope with explicit prototyping phases — you see working results before committing to the full build. No open-ended hourly billing that punishes you for complexity.
Review and test agents show immediate savings. Code generation reaches full productivity in 2-3 weeks. Measurable velocity improvements appear within the first sprint.