L7 Informatics’ position is that generative AI has been deployed in regulated science with only half the architecture it needs, and that the missing half is an active knowledge graph generated at the point of execution.
AUSTIN, Texas, July 23, 2026—The life sciences industry has spent the past three years trying to make AI more trustworthy by reaching for better models. L7 Informatics sees the problem differently. A large language model is a formidable pattern recognizer that carries no inherent understanding of an organization's validated workflows, permissions, or data lineage. Making it larger does not give it what it never had, which is one reason MIT's 2025 State of AI in Business report found 95% of organizations seeing no measurable return on their generative AI investments.
The alternative L7 points to is neuro-symbolic architecture, which pairs neural networks with symbolic systems. The neural component behaves like an analytical, pattern-driven left brain. The symbolic component, an ontology-driven knowledge graph, behaves like the contextual, relational right brain, holding the entities, relationships, and rules that give reasoning its boundaries. Deployed without that second half, AI in a GxP environment produces fluent output that cannot be traced, defended in an audit, or trusted to act.
Most organizations attempt this bolt-on by curating a knowledge graph from public databases and published literature. In L7's view, that supplies a right brain full of someone else's memories. Such graphs are powerful for discovery research and effectively blind to operations, carrying no instrument calibrations, no reagent lot numbers, no operator training records, and no execution parameters. The model can recite what the field has published. It cannot account for what happened on the floor last night.
"For years the industry has treated the knowledge graph as something you assemble after the fact, a reference library built from data that has already gone cold," said Vasu Rangadass, Ph.D., Founder and CEO of L7 Informatics. "In regulated science, that is exactly why AI stalls. The graph has to be generated by the work itself, at the point of execution, or the AI reasoning on top of it can never be trusted to act."
That is the shift L7 describes as the active knowledge graph. Rather than being compiled as a separate initiative, it forms as a byproduct of running the work. As operators execute workflows across research, development, and manufacturing, the people, equipment, materials, process parameters, and results are captured as connected data the moment they occur, with ontology labels applied at the point of capture. The symbolic half of the brain, in this model, is written by the science itself.
A new white paper from L7 Informatics lays out the full architecture, including the deterministic harness that allows probabilistic models to operate under mandates such as 21 CFR Part 11 and GAMP 5, and the operational results customers have documented. Active Knowledge Graphs: Why AI in Regulated Life Sciences Stalls Without Them is available to download at https://l7informatics.com/white-paper/active-knowledge-graphs-why-pharma-ai-stalls-without-them/
About
L7 Informatics L7
Informatics is on a mission to
accelerate the pace of science. L7|ESP®, our Enterprise Science Platform, is
the operational backbone of modern life sciences organizations, orchestrating
people, instruments, data, and AI agents that drive research, development, and
manufacturing. Built for regulated environments, L7|ESP unifies laboratory,
manufacturing, and operational workflows into a single execution layer, giving
science the infrastructure it needs to move faster and comply by design.
L7|SYNAPSE™, L7|ESP's agentic AI layer, brings reasoning and autonomous
execution to that environment while preserving the compliance and auditability
that regulated operations require.
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