We deliver governed AI into regulated healthcare and life-sciences environments, in four-week cycles that each end in production.
Every system we build follows the same architecture and the same rules of engagement. This is how we work, and why it holds up when a regulator, an auditor or your own clinicians start asking questions.
Grounded in your data. Sound in its reasoning. Traceable in its output. Built on top of platforms your team already trusts.
Every output traces to a real source. Provenance is attached at ingestion and carried through every transformation, rule and answer.
Every inference is checked against the logic that produced it, not just the citation. Two correct facts combined into a wrong conclusion is a failure we design out.
The full chain (data → rule → reasoning step → output) is inspectable after the fact, timestamped and immutable.
Most AI implementations collapse two jobs into one. We keep them apart, and that separation is what makes a system someone can sign off on.
A governed data pipeline and a rules engine produce validated, auditable outcomes. Rules are versioned: given the same event and the same rule version, the system returns the same outcome every time. No probability involved.
The AI explains what the system already determined, in plain language, with a citation for every answer. It never interprets regulation or decides what is compliant, and when grounding is weak it says so instead of guessing.
Let deterministic logic own the determination. Let AI own the explanation. Never let the second job quietly absorb the first.
Each cycle is scoped to one workflow: narrow enough to finish completely, real enough to matter operationally.
Map the real requirements with your team, connect to source systems and set the governance model.
Build the engine at the heart of the cycle and validate it against your real operational records.
Add audit logging, access controls and the explanation layer, then run a structured evaluation set.
Deploy into your environment, walk your team through it and document everything.
Each cycle is the first in a series, not a one-time project. The first proves the model; the next ones expand scope with more workflows, more data sources and more regulatory domains. Most clients run three to six cycles over six to twelve months. How we work →
Most of our work touches regulated data, so the same controls apply on every engagement. Each service page answers the PHI question for its own context; this is the full picture.
We execute a Business Associate Agreement before any protected data is accessed. Not after the kickoff, not in parallel: before.
We work in isolated environments deployed into your cloud account, not ours, with role-based access enforced from day one.
Protected values are tokenized at the ingestion boundary, so PHI never reaches the AI layer directly and downstream consumers that do not need it never see it.
Every evaluation is timestamped and immutable. When an auditor asks how a decision was made, the answer is already assembled.
The code, the infrastructure definitions and the documentation are yours at the end of every cycle, deployed into your cloud account.
We build on established open-source and platform tooling (Databricks or BigQuery, Airbyte, dbt Core, Soda Core) so your team can extend it without us. See the stack →
We have deep relationships in the Southern California life-sciences ecosystem and deliver remotely for engagements across the country.
Every engagement starts with a 30-minute scoping call. If it fits a four-week cycle, we tell you exactly what it includes and what it costs.
If we cannot scope a bounded problem we can genuinely solve in four weeks, we say so rather than manufacture one.
We build the deterministic layer first and put AI on top of it. That is the difference between an AI demo and a system someone can sign off on.
If we can scope it into a four-week cycle, we’ll tell you exactly what it includes and what it costs. If we can’t, we’ll tell you that too.
Book a 30-minute scoping call →Hi. Ask me about our services, the four-week cycle, PHI and security, or the Luveo case study. Every answer links to where it comes from.