Get the path right before you build.

We read the system as it is, not as the diagram claims. Where are the bottlenecks, the brittle seams, the decisions that will cost you in six months? You get a clear write-up of what is sound, what is risk, and what to change first, in priority order, with the reasoning behind each call so your team can defend it without us in the room.

Before a build starts, the expensive mistakes are already baked in: the wrong data model, the integration nobody costed, the feature that quietly needs a queue. We scope the work honestly, surface the unknowns early, and hand you an implementation plan your engineers or ours can execute against. Smaller, sharper scopes beat ambitious ones that stall.

A demo that works once and a system people depend on are different animals. We take the promising prototype and do the unglamorous engineering that makes it dependable: real data models, error handling, retries, observability, and the tests that let you change it later without fear. The clever part stays; the fragility goes.

Plenty of AI projects look impressive and quietly fail in ways nobody is measuring. We audit what you have built or plan to build: where retrieval is wrong, where a model is doing a job deterministic code should own, where the evals are missing. You leave knowing what to trust, what to fix, and what to cut. The house position is levers, measured, never black-box mystery.

Old code is not the enemy; code nobody can safely change is. We modernize incrementally: type safety, server-side rendering migrations, dependency upgrades, and structure that lets your team ship faster without a risky full rewrite. We have moved real applications, including a server-side rendering migration across more than twenty production tools, so the path is mapped, not theoretical.

The difference between a codebase your team trusts and one they dread is mostly tests and guardrails. We build the coverage that matters, unit, integration, and end-to-end, wire it into CI quality gates, and set up the observability that tells you a system is degrading before a customer does. Reliability is engineering, not luck.

Senior time gets eaten by the same manual work week after week: context gathering, repetitive lookups, copy and paste across tools. We build the internal tooling that takes it back, MCP servers, scripts, and automations wired into the systems your team already uses, with deterministic code where reliability matters and a model only where judgment does.