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In Regulated Domains, AI Without Evidence is Untrustworthy.

I help life sciences and biotech companies move from “AI that sounds smart” to AI that provides scientific proof with verification-first architectures, benchmarked accuracy, and transparent validation pipelines. No black boxes.

I Build and Consult for Verification-First AI Systems for Life Sciences

I’ve built my own AI system for antibody discovery with 93.6% validation accuracy and 0% hallucinations across 139 biological entities 1. Using my first hand experience, I can help you build, improve, or evaluate your idea.

Who I Work With

I help life sciences and biotech companies that are:

  • Running AI pilots that aren’t reaching production or are optimizing production results
  • Struggling to validate AI outputs against scientific ground truth
  • Building AI systems where hallucinations aren’t acceptable
  • Bridging communication gaps between scientists and engineers
  • Architecting multi-agent systems for context-aware reasoning over specialized biological databases

Roles I work with: VP Research Informatics, Director of Bioinformatics, Chief Data Officers, CTOs at biotech startups, VCs evaluating bio-AI investments.

Services

AI Multi-Agent Architecture & Implementation

For organizations building multi-agent systems or evaluating agentic AI approaches

I design and implement specialized AI multi-agent systems that coordinate complex workflows without hallucinations. This includes planning agents, domain-specific execution agents, and synthesis agents that triangulate evidence across multiple data sources.

What you get:

  • Architecture design with pattern routing strategy
  • Proof-of-concept implementation using modern frameworks
  • Validation framework to prevent hallucinations
  • Observability and telemetry setup for agent governance

Example outcome: 8-agent system handling 12 distinct query patterns with 100% routing accuracy 2.

Verification & Validation Frameworks

For organizations deploying AI in regulated or scientific contexts where accuracy is non-negotiable

I build systems that validate AI outputs against ground truth. This includes multi-metric filtering, cross-validation against authoritative APIs, and evidence-based evaluation rubrics.

What you get:

  • Ground truth validation architecture
  • Automated cross-checking against reference databases
  • Hallucination detection and prevention mechanisms
  • Benchmark suite for ongoing accuracy monitoring

Example outcome: 93.6% validation accuracy with 0% hallucination rate across production testing 1.

Context Engineering for Domain-Specific AI

For organizations whose AI systems struggle with domain-specific knowledge, terminology, or validation standards

I structure domain knowledge so AI systems produce scientifically accurate results. This includes designing system prompts, defining tool interfaces, setting context-aware thresholds, and creating validation logic that matches how experts actually think.

What you get:

  • System prompt architecture optimized for your domain
  • Tool design that matches domain workflows
  • Context-aware threshold tuning (when to be strict vs. permissive)
  • Vocabulary mapping between user language and system operations

Example outcome: AI agents that understand “liver-specific” requires tau score ≥10 and fold-enrichment ≥1.5×, but “highly liver-specific” requires tau ≥100—without hardcoding every permutation 3.

AI Strategy & Audits (Quick Start)

For organizations exploring AI adoption or diagnosing why current AI initiatives aren’t succeeding

I investigate what’s preventing your AI pilot from reaching production. Simple RAG app or multi-agent orchestration platform? Single database or data fusion architecture? Scope adjusts to match your system’s complexity.

What you get:

  • Assessment report with specific recommendations
  • Gap analysis (architecture, data, validation, governance)
  • Prioritized roadmap for next steps

Timeline scoped during initial consultation—you’ll know the cost before we start

Example outcome: Production readiness assessment identifying why your AI pilot isn’t meeting your expectations.

Why Work With Me

Working Proof

I built a multi-agent system from scratch. You can see it work in a live demo. You can verify my claims against the Human Protein Atlas API yourself. While it’s far from perfect and answering every question the Human Protein Atlas can be capable of, I’ve struggled and learned from failure to engineer and experiment with solutions to make my AI system work.

The liver-specific protein query I documented in my blog post took 2 months to get right 4. I’m showing you the solution AND the variance calculations because that’s the level of rigor biological data demands.

Verification-First Mindset

I’m not a scientist, which made me hyper-aware of validation importance. Every claim in my system traces to ground truth. Zero hallucinations across 139 entities tested 1. I bring that rigor to your projects.

Building without formal academic training in biology has made me respect the importance of rigorous validation. I’ve approached HPA’s data with deep respect for scientific standards, and I want to demonstrate that same rigor in the systems I build for you.

Transparent Methodology

Everything I build is documented, reproducible, and auditable. You’ll understand exactly how the system works, what the limitations are, and how to verify accuracy. No black boxes.

The AHSG (liver protein) validation example in my blog post? I wanted to demonstrate what transparency looks like in the system displaying metrics, the HPA ground truth, the 0.2% variance calculation 5, and the exact API endpoint to verify it yourself. That’s the transparency I bring to every project.

How We Work Together

1. Initial Consultation (30 min, free)

We discuss your challenges, what you’ve tried, and whether there’s a fit. I’ll be honest if I think there’s a simpler solution than AI, or if your problem is outside my domain expertise.

2. Scoping & Proposal (1 week)

I assess your needs, define deliverables, and provide a fixed-scope proposal. You’ll know the cost upfront—no hourly surprises.

3. Engagement Kickoff

Clear milestones, regular check-ins, transparent progress tracking. If I hit a blocker (like the 2 months debugging that liver query), you’ll know about it immediately, not at the end of the project.

4. Delivery & Documentation

You receive working systems plus documentation to maintain and extend them. I document limitations as clearly as capabilities—you need to know what the system can’t do as much as what it can.

5. Optional: Ongoing Advisory

Monthly retainers available for continued support and strategic guidance.

Frequently Asked Questions

Q: Do you only work with life sciences companies?

A: Life sciences is my primary focus due to domain expertise, but the verification-first architecture applies to any regulated or scientific domain where hallucinations aren’t acceptable. I’m open to other industries and domains.

If you’re in finance, legal, healthcare, or any field where “good enough” AI outputs aren’t acceptable, the same validation principles apply.

Q: How do you price engagements?

A: Fixed-scope projects with clear deliverables. No hourly surprises. You know the cost upfront.

For the AI Strategy Audit, we’ll scope the assessment during the initial consultation based on your system’s complexity, then I’ll provide a fixed price. For implementation projects, I’ll scope the work, estimate complexity, and give you a fixed price for defined deliverables.

Q: Can you build production-ready systems or just prototypes?

A: I build production-ready systems when the problem is scoped. My HPA system is different—it’s a minimum viable prototype for exploring an unbounded problem space.

HPA data covers innumerable query possibilities. I built a prototype that handles 12 distinct patterns successfully, but I’m not claiming comprehensive coverage. It’s a learning vehicle, not a commercial product (yet).

Client projects are scoped: “validate antibody data against three databases” or “build biomarker discovery for cancer research.” Bounded problems get production solutions. My HPA prototype taught me how to build those solutions by tackling the hardest possible case—open-ended biological research.

Q: What if we’re not sure AI is the right solution?

A: Start with an AI Strategy Audit. I’ll tell you honestly if AI makes sense for your use case or if there’s a simpler solution.

I’ve learned that sometimes the best answer is “don’t use AI here—use a database query.” Verification-first thinking means being honest about when AI adds value and when it’s overkill.

Let’s Talk

If you’re facing challenges with AI validation, multi-agent coordination, or translating scientific requirements into technical systems, let’s discuss whether I can help.

Reach out:

Include in your message:

  1. What you’re trying to build or fix
  2. What you’ve tried so far
  3. What’s blocking you

I’ll respond within 48 hours and let you know if I think I can help.

Fair Warning (Setting Realistic Expectations)

Before you reach out, you should know:

What I’m good at:

  • Building verification architectures for biological data
  • Multi-agent system design and coordination
  • Context engineering for domain-specific AI
  • Debugging complex AI LLM multi-agent systems

What I won’t do:

  • Guarantee delivery timelines
  • Build systems for clinical/diagnostic use (RUO only)
  • Pretend I can solve problems outside my expertise
  • Deliver “good enough” accuracy when you need rigorous validation

If you need someone who will promise fast timelines and perfect solutions, I’m not your consultant. If you need someone who will show you variance calculations and tell you when debugging takes 2 months, let’s talk.

References

Footnotes

  1. Validated against Human Protein Atlas ground truth data. 44 of 47 biological claims verified (93.6% accuracy) with zero hallucinations across 139 biological entities tested in 12-test benchmark suite. See Validation Methodology: Quality Assurance for detailed cross-validation protocols and 18-Month Journey: Validation Against HPA Ground Truth. ↩ ↩2 ↩3

  2. System architecture uses 8 specialized AI agents (Tissue Biomarker, Brain Biomarker, Cell Type Marker, Serum Biomarker, Blood Biomarker, Biomarker Validation, Vendor Specialization, Generic Exploratory) coordinated by a Planning Agent. Achieved 100% pattern routing accuracy across 12 distinct biological query patterns. See 18-Month Journey: Test Results Summary and Validation Methodology: AI Agent Architecture. ↩

  3. Context-aware threshold optimization enables AI agents to interpret biological language nuances and apply appropriate HPA validation metrics automatically. Tau score thresholds range from ≥10 (tissue-enriched) to ≥100 (highly tissue-specific), and fold-enrichment thresholds range from ≥1.5× (tissue-enriched) to ≥4× (high confidence). See Validation Methodology: Metrics Definitions for tau score and fold-enrichment interpretation ranges. ↩

  4. Stage 4 prototype development (December 2025) focused on accuracy improvements through iterative debugging, threshold optimization, and architectural enhancements across a 12-test benchmark suite. Development involved repeated query execution for blocker removal, tool optimization, data fusion, agent prompt development, and observability integration. See 18-Month Journey: Stage 4. ↩

  5. Cross-validated liver protein AHSG against HPA JSON API showing system tau score 4,328 vs HPA 4,319 (0.2% variance), system liver nTPM 5,638.7 vs HPA 5,439.8 (3.5% variance), with reliability classification “Enhanced” confirmed. See 18-Month Journey: Validation Against HPA Ground Truth and Validation Methodology: AHSG Cross-Check Protocol. ↩