Service Compliance & Governance

Compliance built in from day one.

Most AI programs bolt compliance on after the pilot works — then spend three months unwinding decisions that were never logged. I build the audit trail, the deployment pattern, and the access controls into the system from the first sprint: append-only logging, on-prem inference where the data can't leave the building, and HIPAA-, SOX-, and GDPR-aware deployment with SOC 2-style controls, not a compliance binder written after the fact.

Append-only Every decision logged, nothing overwritten
HIPAA·SOX·GDPR Aware deployment patterns, not an afterthought
Audit-ready On day one, not after the first incident

Most teams build the AI system first and ask the compliance question second. That order works right up until legal, a customer's security questionnaire, or an actual regulator asks who approved a decision, what data the model touched, and where the record is. If the honest answer is "we didn't log that," the system doesn't ship — or worse, it ships and becomes the finding in next year's audit.

I build the other way around. Before a line of production code is written, I know what has to be logged, where the data is allowed to live, and who's allowed to see what. A system I can't audit doesn't go live, regardless of how good the demo looked.

What "built in" actually means

Three things have to be decided at design time, not retrofitted after launch. Retrofitting compliance is where projects go over budget and past deadline.

  • Audit trails and append-only logs — every prompt, retrieval, decision, and tool call recorded to a log that can't be edited after the fact, structured so an auditor can reconstruct exactly what happened and why.
  • Regulatory-aware deployment — the architecture matches the regulation that actually applies to your data: HIPAA for PHI, SOX for financial controls, GDPR for EU personal data — decided before the stack is chosen, not patched in afterward.
  • On-prem inference and access controls — for data that legally or contractually can't leave your environment, inference runs inside your perimeter, with role-based access enforced at every layer, not just the front door.

Where this pays for itself

The return isn't abstract. It's the security questionnaire you pass without a scramble, the audit that closes in a day instead of a quarter, the incident review where you can show exactly what the system saw and did. I look for the process where a compliance gap is currently blocking a deal or an audit, and I close that gap first.

A system you can't audit is a liability wearing the costume of a productivity gain. I build systems that do the work and can prove it, in writing, to anyone who asks.

The four controls of a compliant deployment.

Designed in from the first sprint — not written into a policy doc after the system already ships.
01 / Audit trails
Append-only, end to end
Every prompt, retrieval, decision, and tool call recorded to a log you can't edit after the fact — structured so it reconstructs, not just records.
02 / Deployment
Matched to the regulation
HIPAA-, SOX-, and GDPR-aware architecture decided before the stack is chosen — the pattern fits the data, not the other way around.
03 / Inference
On-prem where it has to be
For data that can't leave your environment, inference runs inside your perimeter — no silent round-trip to a third-party API.
04 / Access
Controls at every layer
Role-based access enforced at the data layer, the retrieval layer, and the action layer — not just at the login screen.
SOC 2HIPAASOXGDPRAppend-only logsOn-prem

Compliance designed in, not bolted on.

Fixed scope, fixed price, the controls reviewed before the build — not after.
01Map

Map the data to the regulation

We identify what data the system touches, which rules actually apply to it — HIPAA, SOX, GDPR, contractual terms — and where it's allowed to live.

02Design

Architect the audit trail and access model

Append-only logging, on-prem or hosted inference, and role-based access are designed together — fixed scope and price so compliance isn't a change order later.

03Build

Ship with the controls already live

We build in sprints with weekly demos, and the audit trail and access controls are working from the first deployment — not added at the end.

04Verify

Prove it holds up under review

We walk the logs, the access model, and the deployment pattern against whatever's coming — an audit, a questionnaire, an incident review — before you need to.

Related work.

Compliance rarely stands alone. These usually come with it.
Service / 03
AI infrastructure
Local model deployment and hardware builds for organizations that need their data to stay inside their walls. Nothing leaves the perimeter.
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Service / 01
Enterprise AI agents
Agentic workflows designed around your actual processes, not a demo — built, deployed, and tuned until they hold up in production.
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Service / 02
RAG systems
Retrieval pipelines that put your institutional knowledge to work. Grounded, source-cited answers on your own data — without leaking it.
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Service / 05
Knowledge systems
Your documents, wikis, tickets, and contracts turned into one queryable, source-cited system that finally answers back — with the receipts.
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Industry / 01
Financial Services
Banks, hedge funds, asset managers, fintech, and capital markets. Fast where the market demands it, provable where the regulator does.
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Industry / 02
Healthcare
Health systems, payers, providers, and pharma. HIPAA-aware, grounded, and auditable — PHI stays protected.
Explore

Let's build the system you can actually defend.

If an audit, a security questionnaire, or a regulator is the thing keeping your AI project from shipping, that's where we start. I'll tell you what the compliance gap costs before we close it.

Let's talk

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