That meaning lives in your ontology: the structure that decides whether an AI agent reasons correctly across biological and clinical data, or just sounds confident while getting it wrong.
The Challenge
Feed a biomedical AI messy, ambiguous data and it doesn't just stumble.
AI hallucinates, confidently,
at rates reaching
~25%
in clinical benchmarks.
Curated, ontology-aligned data cut hallucination rates by
40%
for GPT-4 and Llama2.
We break down how that works, plus two real biomedical use cases where it paid off.
Download the whitepaperthe solution
For more than a decade, we've been the team pharma and biotech companies bring in to actually untangle their data. That's the curation and harmonization work that sets any AI initiative up to succeed.
We align your data to 50+ established ontology standards, plus any custom ontologies you're already running, through our Terminology Management Solution
We use AI-assisted mapping we've spent eight years refining, so the heavy lifting doesn't fall on your team
We help you build and maintain the structured knowledge that keeps your AI accurate enough to actually trust, whatever stage you're at with agentic AI
The research behind the conversation
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One 2026 study found ontology-grounded AI hit 98% accuracy, versus 37% for an ungrounded baseline. Jane Lomax's Pistoia Alliance talk shows where agentic AI can accelerate ontology work, and where it can't.
'Ontology' used to be the word that made meetings go quiet. Now Gartner calls it critical infrastructure. Here's what changed in 2026.
A new hire doesn't understand your business on day one. Neither does an AI agent, until it's been onboarded into the meaning underneath your data.
Most of what your scientists know was never written down. Curation is how that tacit, institutional knowledge becomes something an AI agent can actually use.
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