Compliant AI adoption · Maryland & Virginia

Adopt AI where "we cannot explain it" is not an option

Practical, governed AI for regulated teams in pharma, healthcare and government. Get real value from AI without betting your compliance record on a black box.

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6x Claude / Anthropic Certified Live 'Ask Hamad' demo Regulated-AI author Human-in-the-loop by design

Everyone wants AI. Regulated teams cannot use "trust me".

In pharma, healthcare and government, an answer you cannot explain, audit or defend is not an answer. That is why so much AI adoption in these sectors stalls: the demos are exciting, the governance is missing.

You do not need more hype. You need AI that is grounded in sources you trust, gated so nothing irreversible happens without a named human, and logged so any decision can be reconstructed.

AI you can put in front of a regulator

I help regulated teams adopt AI responsibly: choosing where it fits, grounding it in approved data, wrapping it in governance and audit trails, and keeping a human in the loop where it counts.

For the DMV's regulated and public-sector teams

Pharma and biotech across the BioHealth Capital Region, next to NIH and the FDA, where "validated and auditable" is the baseline.
Health systems and academic medicine around Johns Hopkins and VCU adopting generative AI under HIPAA and BAA constraints.
Northern Virginia contractors and agencies under pressure to do more with less as federal budgets tighten, turning to AI and automation and needing it done safely.

I build this, I do not just talk about it

Six Anthropic / Claude certifications, from Claude 101 and Building with the Claude API to MCP, Agent Skills and Claude Code.
The assistant on my own site, Ask Hamad, is a live, grounded, guard-railed demo of exactly this approach.
Author of eBooks and articles on AI in regulated industries, prompt caching, and setting an organisation's PII policy for AI tools.

How we work together

Step 1

Find the right use

Where AI genuinely helps, and where the cost of error is low enough to start.

Step 2

Ground and govern

Grounding in approved sources, guardrails, and an audit trail from day one.

Step 3

Human in the loop

Typed, gated tools so nothing irreversible happens without a named person.

Step 4

Prove and scale

Measure, document, and widen autonomy only as trust is earned.

Start where the risk is zero

We begin with a low-risk, high-value use case and a scoped assessment. You see governed AI working on your own data before you commit to anything bigger.

Questions, answered

Can AI be used under HIPAA or a BAA?

Yes, with the right architecture, vendor terms and controls. The point is to design for it from the start, not retrofit compliance later.

How do you make AI auditable?

By grounding responses in known sources, logging inputs and outputs, and gating actions behind human approval, so any decision can be reconstructed.

Is this just for healthcare?

No. The same governance approach fits pharma, government contractors, and any team where "we cannot explain it" is not acceptable.

Do you build, or only advise?

Both. I can advise on strategy and governance, and build the grounded, guard-railed tooling itself.

Where I work

Regulated and public-sector teams across Maryland, Northern Virginia, Baltimore and the DC metro, remotely.

Let's talk about your build

A straightforward conversation about your product, your constraints, and how I can help. No sales pitch.