Verify the source, not the story.

The document is the easy part; an intern, ChatGPT, and a word processor make a compelling document. The value is the thinking behind it -- analyzing, re-analyzing, testing a claim until the load-bearing answer is clear. I do that thinking and return it in whatever form the decision needs: a diligence read, a target product profile, a regulatory package, a data-room articulation. Bespoke to your indication. Days, not months.

What comes back is not a pile of pages. It is the handful of facts the decision rests on, the logic that connects them to the conclusion, and my name on the verdict.

Breadth alone is table stakes. The edge is the overlay: I lay the biomedical evidence over the money, the patents, the people, the corporate record, the regulators, and the funding -- assembled into one view of a company, an asset, a team, a grant. No single database connects them all, so the cross-domain landmine stays buried until it costs you the wire. The overlay surfaces it before you act -- the view a CEO needs to move, and an investor needs before the wire.

The machinery, kept brief: thousands of verified data tools across hundreds of categories, specialized agents that draft the literature, custom Python scripts, and frontier models for reasoning -- local-only where the data cannot leave, HIPAA and SOC-grade where it can. One operator directing the output of a research organization, and every pull lands as a source-traced fact, never a screenshot.

1. 01

   Sit down and scope

   Before any machine runs, a person takes in your question and your documents -- and scopes it to your decision, not a generic checklist.

   However you prefer to work -- a call, a meeting, a secure upload -- I understand the question first. The depth is set and named here: a fast pass over twenty thousand pages surfaces what no one person could read in time; a deep read over weeks is a different commitment, and which one you are buying is stated plainly, never oversold. So the read is aimed at your decision, and you know exactly what you are paying for.

2. 02

   Build the environment

   A bespoke, compliant environment stands up for your question -- and lays the science over the money, the patents, the team, and the record to assemble one true view.

   The thing no single-domain report can do, and what capital actually pays for: I overlay the biomedical data on the finance, the patents, the team and its social graph, the funding history, and the regulatory record -- SEC filings, USPTO, state corporate registries down to the Delaware incorporation, NIH RePORTER, FDA enforcement and adverse events, and thousands of verified data tools besides -- so the cross-domain landmine surfaces before you wire. The patent that lapses in fourteen months. The lead inventor who just left for a rival. The grant that quietly went unrenewed. The thing no single-lane report would ever catch.

3. 03

   Run the engine

   The engine traces every claim to its primary source and grades it. A person reads and reasons at each gate.

   Source: every figure traced to its filing, trial registry, or paper -- verbatim, with a durable identifier and a timestamp. Break: a model of a different lineage audits each claim and is allowed only to veto it, never to admit it, so a hallucinating judge raises false alarms, never injects a lie. Pre-register: a forward call is written down and dated before the readout, so the record shows I cannot edit it after. The result: every digit is re-checkable, the weak claim dies in private, and the track record is auditable, not asserted.

4. 04

   Review and iterate

   A human reviews the work before anything ships. I iterate with you until it is right.

   What comes back is whatever the decision needs -- a Verified Read before you wire, a target product profile, a regulatory check, a data-room articulation. Dialed in, sourced, defended. A document you can carry into a committee and defend line by line.

Verification is worth nothing if it can lie to you. So I do not assert trust here; I derive it. Every fact is a small, frozen record -- a proposition, where it came from, the exact quote, and when -- and everything trust-bearing is computed from the source's identity, never typed by an author. A mislabeled source fails to resolve; it cannot quietly pass.

Every number, date, and negation must appear verbatim in the cited quote or the claim fails -- a deterministic check, no model in the loop. A second model, of a different lineage from the one that wrote the analysis, audits each claim and is allowed only to veto, never to admit -- so a hallucinating judge raises false alarms, never injects a falsehood. And source tiers are computed from independence and accountability, not declared. Predictions, where they appear, ride above the facts carrying their full chain of premises -- labeled as inference, never as verified truth.

The whole AI industry races to generate. In 2025 the regulators moved the other way: the FDA's draft guidance on AI in regulatory decision-making put provenance, traceability, and documented context of use at the center, and in January 2026 the FDA and EMA published joint guiding principles naming data governance outright. Generic AI output is fluent and gets turned away at exactly that gate, because it cannot show where its claims came from.

This engine is provenance by construction -- and the FDA's own data is wired in directly, so a regulatory claim resolves to the agency's record, not a scrape. I do not just claim that bar; I have already cleared it under the regimes that exist:

I build FDA-grade work: documents built to, and defensible under, the standard the guidance calls for. Built to the bar, not blessed by the agency -- I say so plainly, and this is not legal advice.

"Order in the chaos" is not a slogan I chose; it is training. I was schooled in complex systems in the Santa Fe Institute tradition of Stuart Kauffman and Murray Gell-Mann, and co-founded a biocomplexity institute under Kauffman himself. Complexity science is the study of the load-bearing order in apparently chaotic systems -- the attractor a biology or a market settles toward. That is why a found pattern recurs, and why it can be templated.

It is also why the agents are not hype. Directing many of them to surface the emergent structure of a system at machine speed is precisely what a complex-systems thinker is built to do -- wings for a mind that already knows the shape it is hunting. The bottleneck was never ideas; it was hands and hours. That is why a one-person shop returns what a team would, and why the output is not slop.

I point out the trends the data supports. Where I commit, I write down before a readout what the data would have to show for me to be right -- then, after it prints, I show whether it did. What I hand someone with a real decision to make is the ground truth, not a guess dressed as one: dated, checkable, out in the open. You turn being right early into money; I make sure you have the facts to be right.