We help engineering organizations adopt AI-assisted development practices without the quality regression most teams experience — and we do the underlying systems work that modern AI applications actually require.
Moving an engineering organization from traditional development to AI-assisted workflows sounds straightforward until your first pull request lands with 400 lines of code no one can maintain. The tools are the easy part. The hard part is the practice around them — the context files, the skill libraries, the review discipline, the quality gates, the handoff patterns between human and AI work.
We've built this system for ourselves. Our code consistently passes A-rated SonarQube quality gates. Our AI-assisted output meets the same standards as anything written by hand. We teach your team to work the same way.
Who this is for CTOs, VPs of Engineering, and Engineering Managers leading AI adoption at organizations where code quality and maintainability aren't negotiable.
What an engagement includes
Assessment of your current development workflow and where AI fits (and where it doesn't)
Setup of context files, skill libraries, and agent patterns specific to your codebase
Quality gate implementation — SonarQube, CodeRabbit, or your existing tooling
Team training on effective prompting, review patterns, and handoff discipline
Ongoing advisory as your team scales the practice
The AI consulting engagement is where most clients start. These are the adjacent capabilities we bring — and take on as standalone projects when the fit is right.
Model Context Protocol servers that connect AI agents to your internal systems, data, and tooling. We default to Go for the binary distribution story and concurrency model — but we'll build in whatever fits your stack.
REST and GraphQL APIs built for both human developers and AI agents. Clear contracts, thorough documentation, and integration patterns that hold up when your system doubles in complexity.
Production data migrations where downtime and data integrity aren't negotiable. We've handled complex migrations in high-stakes domains — billing systems, provisioning platforms, systems of record.
Native and cross-platform mobile applications with the same quality standards we apply to everything else. React Native and Expo for most engagements; native when the use case demands it.
Architecture review and implementation support for teams that need to get security right the first time. Pragmatic, not theatrical — we focus on the threats that actually matter for your system.
We've made these decisions enough times to have opinions worth sharing. These are our defaults — your mileage may vary.
Postgres for transactional systems, full stop. Document stores for specific cases, not defaults. Read replicas before distributed complexity.
Go, unless there's a compelling reason otherwise. Binary distribution, concurrency model, and operational simplicity outweigh ecosystem tradeoffs.
React Native with Expo for most applications. Native when hardware or performance requirements demand it. We don't do hybrid web wrappers.
SonarQube as quantitative baseline, CodeRabbit in the review loop. AI-assisted code is only as good as the gates it has to pass.
Claude Code for agent-based development work, with custom skills and MCP servers bridging to client systems. Tool-agnostic in principle.
GitOps by default. Rancher + K3s + Fleet for hybrid SaaS environments. Standard cloud deployments when that's the whole picture.
These are the languages, frameworks, and tools we reach for on real projects.
Most of our engagements start with a conversation about what your team is trying to accomplish, what you've tried, and where the quality or velocity is breaking down. No readiness assessment funnel, no prepared pitch — just a conversation.
Get in Touch