For VPs of Engineering at mid-market enterprises
Two-week audit. Three highest-ROI AI integrations identified, scoped, and ranked. One working pilot for the top pick. $15K flat, credited toward implementation if we build any of them.
No slide decks. No workshops. No junior handoffs. A senior engineer, two weeks, one shippable outcome.
If this sounds familiar
If two of those resonate, you are exactly who this audit is for.
How the two weeks run
Week 1
I get read access to your repo and Slack. I interview your engineering leads, your product owners, and one or two front-line engineers who actually live in the code. I read the system: data flows, integration points, technical debt, the places where AI would compound and the places where it would just be expensive theater.
Week 2
I deliver three integration specs, ranked by ROI. Each spec covers data dependencies, model approach (LLM, classical ML, or hybrid), build estimate, risk profile, and a go/no-go recommendation. For the top-ranked integration, I build a working pilot — actual code, in your stack, runnable on your data — and walk your team through it.
Deliverables
A 20–30 page audit document, three implementation specs, one working pilot, a build estimate for each spec, and a one-hour walk-through with your team. Plus a clear go/no-go on whether to extend into a build, and what it costs if you do.
Why this beats the alternatives
Why I can do this
Three live AI systems built and running in production, all public, all self-tuning. They are the receipt that the audit and the build are the same person.
Live, interactive walkthrough of how I use AI day-to-day. Cost meter on, $4/day budget cap, 100+ term glossary. The audit's methodology, but as a public artifact.
MLB prediction engine. Self-tunes overnight from yesterday's accuracy. The "ML guided by AI" pattern in production: Claude reads the validation curves, rewrites the pipeline.
Self-tuning market analysis. Three-model ensemble (momentum, mean-reversion, sentiment) with adaptive weights and Claude-driven insight commentary.
Questions you're probably about to ask
You don't pay. Every codebase I have audited has surfaced at least three high-ROI integrations, but the guarantee is real: if I cannot find three that pencil out, the audit is on me.
I build them. The audit is the wedge. Most engagements turn into a follow-on build for the highest-ROI pick, scoped at the end of week two. Typical builds run $40K to $250K depending on complexity, and the $15K audit fee credits in full toward whatever we build.
Mid-market enterprises with 50 to 500 engineers and a codebase that has been in production for at least three years. Below or above that, contact me anyway — Fortune 500 work goes through Champlin Enterprises, and smaller-team work I scope individually.
Modern stacks (TypeScript, Python, .NET, Swift, Kotlin) and legacy stacks (Visual Basic, Classic ASP, .NET Framework, aging PHP) are equally welcome. Legacy is often where the highest-ROI AI integrations live, because nobody else wants to touch them.
Remote-first. I read code in your repo, talk to your engineering leads on Zoom, and embed in your Slack for two weeks. One onsite week is available when the project genuinely needs it.
Consultancies deliver slide decks. I deliver a working pilot, three implementation specs, and a build estimate. The deliverable is shippable code and engineering decisions, not a strategy roadmap. I am a senior engineer first, an advisor second.
Usually inside two weeks of a signed engagement letter. I run one audit at a time so the engagement gets full attention. Calendar fills up, so the answer to "when" is partly "when did you reach out."
Next step
A 30-minute call to talk through the codebase, the AI mandate, and whether the audit is a fit. No pitch, no deck, no follow-up sequence. Either it's a yes after thirty minutes or it isn't.
