About Axon Agentic
Hi, I’m Jonathan Agoot
Since April 2024, I’ve invested my time and resources to experiment and build something I never imagined I’d create—a comprehensive AI system for antibody discovery that helps scientists navigate one of the most complex databases in biological research. It started as curiosity and then steadily materialized into something real.
I’m not a scientist. I don’t have a PhD in biology or a background in academic research. What I do have is 12+ years of digital transformation experience in the life sciences industry, a relentless drive to experiment, and a genuine passion for making effective tools accessible to people who need them.
The Journey That Got Me Here
For over a decade, I worked with some of the biggest names in life sciences and biotech—companies driving innovation in research tools, diagnostics, and pharmaceutical development. I led digital transformation initiatives, built account-based marketing strategies, and turned massive datasets into actionable insights that drove millions in revenue.
At Thermo Fisher Scientific, I developed a machine learning-based product recommender in 2016, built AR/VR experiences for scientists, and helped grow eCommerce operations to scale. At EMD MilliporeSigma (Merck KGaA) during the COVID-19 era, I established their account-based marketing practice from scratch to enable 250+ stakeholders to digitally reach their customers in a disrupted marketplace, and helped turn around a 2-year sales decline by making data fully actionable.
I thrived on solving complex problems—breaking down silos, automating manual processes, and proving that data-driven approaches could transform how businesses engage with their customers. But somewhere along the way, I realized I wanted to build, not just optimize.
From Marketing to Building AI Systems
In 2022, I made the leap from corporate roles to independent consulting. I worked with life sciences companies to transform their marketing from volume-based lead generation to highly targeted, personalized strategies. I integrated early LLM platforms like ChatGPT and Jasper AI into content production workflows, and I started exploring what these tools could really do.
That’s when I discovered the Human Protein Atlas (HPA)—a treasure trove of experimental data about where proteins are found in the human body, how they function, and what makes them interesting for research. Scientists use this data to discover biomarkers, validate drug targets, and understand disease mechanisms. But navigating it is incredibly complex.
I thought: what if an AI system could do this work intelligently? What if it could understand natural language queries, navigate multiple databases, validate antibody candidates, and deliver automated synthesized insights?
So I built it. Curiosity and experimentation drove me to increase my commitment to making this real.
What I’ve Learned
Over 18 months and four development stages, I’ve built a multi-service platform that combines open source software and GPU-accelerated vector search. It orchestrates a multi-agent system to process biological queries from natural language, runs fusion queries across 3 types of databases, and validates antibody candidates using experimental evidence from the Human Protein Atlas.
I’ve learned more about protein biology, antibody validation, and biomarker discovery than I ever thought possible. I’ve learned to work with massive biological datasets, optimize an AI-driven query processing workflow, and design systems that are universally adaptable—meaning they work across any biological domain without hardcoded assumptions.
But more importantly, I’ve learned that I don’t need a traditional academic background to contribute meaningfully to the scientific community. I just need curiosity, persistence, a willingness to ask questions and keep learning. However, it would be better to work alongside scientists and researchers as I’ve done when I worked at Thermo Fisher and MilliporeSigma.
What I Do Now
Today, I work as an independent consultant and engineer focused on building LLM-enabled applications for biological research. I specialize in:
- AI Application Development: Building intelligent systems that leverage large language models, vector databases, and multi-agent orchestration to solve complex research problems
- Digital Transformation: Helping biotech and life sciences companies adopt data-driven, AI-enhanced strategies for marketing, operations, and research workflows
- ABM & Demand Generation: Designing targeted account-based marketing strategies that move beyond volume-based approaches to deliver qualified, high-value engagement
I bring together cross-functional expertise in data analytics, emerging technologies, B2B life sciences digital marketing domain knowledge, and more.
Why I’m Inviting
I believe the best work happens through collaboration. Whether you’re a scientist frustrated with manual data workflows, a biotech company exploring AI applications, or a marketing leader looking to transform how you engage customers—I’d love to talk.
I’m passionate about teaching and mentoring. I love sharing what I’ve learned and helping others adopt new technologies effectively.
I’m also still learning every day. I stay current with emerging AI capabilities, explore new tools and platforms, and continuously refine my approach based on what works.
Let’s Connect
If you’re curious about what AI can do for biological research-share with me your problem or challenge, interested in seeing the HPA discovery system in action, or just want to talk about digital transformation in life sciences—reach out. I’m always open to conversations, demos, and exploring how we can work together.
I may not have come from academia, but I’ve proven that with the right mindset, I want to build tools that make a real difference for scientists. And I’m just getting started.
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