Production AI, shipped this week. Fixed price.

Senior ML engineer who builds AI features for early-stage startups — Claude integration, eval harness, monitoring, the full production stack — for a price you can read on the page. Same engineer also audits AI deployments for companies that need an outside read.

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What you get

Pillar I

Data Architecture

We trace your data from raw source to live model. You learn which link breaks first under production load — and how to fix it before it does.

Pillar II

Access Control

We map who can touch your model, weights, training data, and inference endpoints. You get the list of gaps that turn into incidents — ranked by what regulators and attackers find first.

Pillar III

Process Documentation

We pressure-test your runbooks, on-call rotations, and kill switches against real failure modes. If they would fail at 2 AM, you find out now — not then.

How the audit runs

01

Intake Week 1

We sign the NDA, agree on what is in scope, and send the document request. You name the stakeholders.
02

Evidence Review Weeks 2–3

We read everything. Architecture docs, access policies, pipelines, runbooks. We trace what actually happens, not what the docs claim.
03

Pillar Scoring Week 4

Each pillar gets a score from 1 to 4 with the evidence cited line by line. Disagree with a score and you can challenge it on the merits.
04

Report Week 5

A ~30-page written report you can hand to your board. Findings, risks, and a ranked list of what to fix first.
05

Debrief Week 6

A 60-minute walkthrough with your stakeholders. Then a 30-day check-in to see what moved.

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Tool

The Knowledge Base

A live, browsable snapshot of an AI research knowledge base — continuously crawled across arXiv, the major frontier labs, Hugging Face, and Hacker News. Embedded, clustered, and rendered as a navigable semantic map.