The ladder from audit to a custom agent in production — context layer, SDLC rollout, custom agents, MCP, and RAG. Vendor-neutral, USD-priced on every page. Start with a $3.5K audit that credits 100% into any engagement within 30 days.
Three steps, fixed scope, and a starting price on the page. Most teams begin with the audit and move down the path; the exact number gets scoped on a 30-minute call, never guessed at.
Five days inside your codebase, docs, tests, and agent workflow. You leave with a scorecard, the gaps that matter, and a 90-day roadmap. It credits 100% into whatever you build next.
The context layer and SDLC rollout that make coding agents reliable, or a custom AI agent that runs a real workflow in production. Fixed scope, vendor-neutral, and you own all of it.
Models change, your codebase changes, and a system that worked at launch drifts. We keep agents and their context layer current, as managed ops, an embedded engineer, or a fractional lead.
Every audit, system, build, and retainer, each with a starting price and a full brief. Filter by what you need.
A 5-day audit of your codebase, docs, tests, and workflow — and the roadmap to make coding agents reliable inside it.
The context layer that makes your repo legible to any coding agent — AGENTS.md, CLAUDE.md, DESIGN.md, repo skills, and guardrails.
The operating layer — MCP, evals, CI gates, multi-agent workflows, and the team training to run them.
A custom AI agent that runs a real workflow in production — customer-service, voice, or internal — owned by you, not rented from a platform.
An MCP server that lets any agent safely use your tools, data, and APIs.
Retrieval that cites sources, respects permissions, and improves over time — not chat-with-docs.
Keep your agents and context layer current — new skills, model upgrades, evals, and quality monitoring, every month.
A senior AI engineer embedded in your team, shipping agentic systems in your repo.
Senior leadership for your AI engineering — strategy, architecture, and team mentoring.
Build logs, agentic engineering decisions, agent failures, evals, and what survives real users. Sent weekly, never more.