# Axon Agentic

> Verification-first AI consulting for regulated life sciences. I design AI systems that provide scientific proof - benchmarked accuracy, transparent validation, and evidence you can verify.

## Company Information

- Name: Axon Agentic
- Domain: axonagentic.ai
- Industry: AI Consulting, Life Sciences, Biological Research
- Focus: Verification-First AI Systems for Regulated Domains
- Principal: Jonathan Agoot

## Website Structure

- Homepage: https://axonagentic.ai/
- NeuroAutomata: https://axonagentic.ai/neuroautomata
- Research: https://axonagentic.ai/research
  - Research and Disclosure: https://axonagentic.ai/research
    - Methodology: https://axonagentic.ai/research/methodology
  - Benchmark reports: https://axonagentic.ai/verification
    - ESM-C vs ESM-2: https://axonagentic.ai/verification/esmc-v-esm
    - TP53: https://axonagentic.ai/verification/tp53-benchmark
    - Methodology: https://axonagentic.ai/verification/methodology
    - INSR: https://axonagentic.ai/verification/insr-esm2-mavedb-joint-coverage
    - CYP2C9: https://axonagentic.ai/verification/cyp2c9-esm2-benchmark
    - See all reports: https://axonagentic.ai/verification
  - Results: https://axonagentic.ai/research/validation
    - Cohort results: https://axonagentic.ai/research/validation
- Services: https://axonagentic.ai/services
- Blog: https://axonagentic.ai/blog
- About: https://axonagentic.ai/about
- Glossary: https://axonagentic.ai/glossary
- Contact: https://axonagentic.ai/contact

## Services

### AI Multi-Agent Architecture & Implementation

Design and implementation of specialized AI multi-agent systems that coordinate complex workflows without hallucinations. Includes planning agents, domain-specific execution agents, and synthesis agents that triangulate evidence across multiple data sources.

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

### Verification & Validation Frameworks

Build systems that validate AI outputs against ground truth. Includes multi-metric filtering, cross-validation against authoritative APIs, and evidence-based evaluation rubrics.

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

### Context Engineering for Domain-Specific AI

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

**Example outcome**: AI agents that understand "liver-specific" requires tau score >=10 and fold-enrichment >=1.5x, but "highly liver-specific" requires tau >=100 - without hardcoding every permutation.

### AI Strategy & Audits

Investigate what's preventing AI pilots from reaching production. Scope adjusts to match system's complexity (simple RAG app to multi-agent orchestration platform).

**Example outcome**: Production readiness assessment identifying why AI pilots aren't meeting expectations.

## Working Proof

Built an AI system for antibody discovery with:
- 93.6% validation accuracy (44 of 47 biological claims verified)
- 0% hallucinations across 139 biological entities tested
- Cross-validated against Human Protein Atlas (HPA) ground truth data
- 8 specialized AI agents coordinated by planning agent
- 12-test benchmark suite covering fundamental HPA queries

See blog post: https://axonagentic.ai/blog/ai-natural-language-human-protein-atlas-18-month-journey

## Target Audience

- VP Research Informatics
- Director of Bioinformatics
- Chief Data Officers
- CTOs at biotech startups
- VCs evaluating bio-AI investments

## Who I Work With

Life sciences and biotech companies that are:
- Running AI pilots that aren't reaching production or 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

## Background

12+ years of digital transformation experience in life sciences industry:
- Thermo Fisher Scientific (machine learning product recommender 2016, AR/VR experiences)
- EMD MilliporeSigma / Merck KGaA (established ABM practice, turned around 2-year sales decline)
- Deep experience in biotech digital marketing and innovation

Independent consultant and engineer since 2022, focused on building LLM-enabled applications for life sciences and biological research.

## Engagement Model

- Fixed-scope projects with clear deliverables
- No hourly surprises - cost known upfront
- Initial consultation: 30 minutes, free
- Scoping & proposal: 1 week
- Delivery includes working systems plus documentation

## Frequently Asked Questions

**Q: Do you only work with life sciences companies?**
A: Life sciences is primary focus due to domain expertise, but verification-first architecture applies to any regulated or scientific domain where hallucinations aren't acceptable (finance, legal, healthcare).

**Q: How do you price engagements?**
A: Fixed-scope projects with clear deliverables. You know the cost upfront.

**Q: Can you build production-ready systems or just prototypes?**
A: Build production-ready systems when the problem is scoped. HPA system is a minimum viable prototype for exploring unbounded problem space - a learning vehicle that handles 12 distinct patterns successfully.

**Q: What if we're not sure AI is the right solution?**
A: Start with an AI Strategy Audit. Will tell you honestly if AI makes sense for your use case or if there's a simpler solution.

## Contact

Use the contact form at https://axonagentic.ai/contact or reach out via:
- LinkedIn: https://linkedin.com/in/jonathanagoot
- Bluesky: https://bsky.app/profile/jonagoot.bsky.social

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

Response within 48 hours.

## Sitemap

For a complete sitemap, visit: https://axonagentic.ai/sitemap-index.xml

---

This file is provided to help LLMs, AI agents, and web scrapers understand our website structure and content.
Version: 1.1.0 (llms-txt.org)
Last updated: 2026-07-18