01
AI Build & Strategy
From a blank repo or a stalled prototype to a system in production. Agents, retrieval, evaluation, and the architecture decisions that decide whether an AI feature is a demo or a product.
AI engineering & security
Verified,
Verified,
not assumed.
We build AI systems, and prove they hold.
Blacklite Labs is an AI and security engineering studio. We design and ship AI systems for teams moving fast, then verify the things that fail quietly — tenant isolation, fail-open paths, the gap between what the docs claim and what is actually deployed. Every finding comes with a reproduction, not a checkbox.
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01 What we do
Build with AI. Prove it holds.
Two halves of the same job. We help teams ship AI quickly, and we make sure speed did not quietly cost them correctness or security.
Build — AI engineering
Ship AI that works in production
Design and build LLM and agent systems that survive contact with real users: retrieval, tool-use, evaluation harnesses, guardrails, and the unglamorous plumbing that makes them dependable.
- Agents & tool-use
- RAG & retrieval
- Evals & guardrails
- AI-native workflows
Verify — Security
Confirm it holds under scrutiny
First-hand security baselines on what you have already shipped. We read the running system, probe tenant isolation, and trace every weakness to the exact line it lives on.
- Security baselines
- Tenant isolation
- AI-build review
- Remediation
02
AI-Build Review
For products written largely by AI agents. We find the gaps the model left: fail-open windows, missing owner-scoping, raw primitives called past the safe helper, and drift between the docs and the deploy.
03
Security Baseline & Tenant Isolation
An evidence-based read on your real posture. Live database enumeration, both isolation walls verified with an empirical cross-tenant probe, auth and billing integrity, backups, and build health. A report you can hand to a customer or an investor.
04
Embedded Partnering
Fractional senior engineering that stays through the work. Build alongside your team, prioritize remediation, govern migrations and releases, and re-verify once it lands.
02 Method
Not a doc review.
Most reviews read the docs and call it done. The docs lie. Whether we are building or verifying, the work is hands-on against the running system — we gather evidence first-hand, then trace every finding to the exact line it lives on.
The deployed posture can be sound while the process that maintains it is not. We test both.
Live enumeration
We read the deployed state directly: policies, indexes, grants, and functions, across staging and production. Not a snapshot from a README.
Empirical probes
A throwaway tenant that owns nothing, run against real data, to prove isolation holds in practice rather than on paper.
Full route audit
Every service-role path checked for owner-scoping. The whole surface, not a sample, because the one unscoped route is the one that matters.
Evals over vibes
For AI work, behavior is held to evaluation harnesses and guardrails, so a change is measured, not just felt.
Build health
Typecheck and the full test suite re-run by hand. Green is something we observe, not something we are told.
Proof-of-concept analysis
Findings traced to exact payloads, reproduction steps, and the controls that bound the blast radius. You can reproduce every one.
03 Field notes
How we think.
Writing from the work, on building real systems with AI.
How to run an engineering project staffed entirely by amnesiacs
AI coding agents forget everything between sessions. Treat that as a design constraint, not a flaw: organize project state across surfaces that each update at their own cadence, so a cold-start agent always recovers the thread. One fact lives in exactly one home.
By Diego Netto · read on engineering.boosterusa.com ↗
Start here
Bring us something real.
Tell us what you are building and who it serves. If it's a fit, you'll know inside a call — and if it isn't, we'll tell you that too.
Email diego@blacklitelabs.ai Engagements led by Diego Netto · Blacklite Labs, LP