Scientists in different roles ask the same question and need different answers. Ask about KRAS and a medicinal chemist wants crystal structures and warhead chemistry; a pathologist wants mutation frequencies and IHC localization by tumor type; a computational biologist wants ADC constructs, linkers, and DAR comparisons; a cell biologist wants mechanism and cell-line selectivity. Generic search averages all four into one page, so each person re-digs through material irrelevant to their work. FACET conditions retrieval on the asker's role at three points: planning (which connectors run, and how), extraction (which findings survive a discipline relevance filter), and synthesis (how the dossier is structured and voiced). Role changes the evidence, not just the wording. On the same KRAS query, the four personas draw on different connector sets. Two decisions make it trustworthy. Retrieval runs through deterministic connectors (ChEMBL, PDB, PubChem, UniProt, ClinVar, PubMed, STRING, Reactome, GTEx, AlphaFold, Open Targets, and more), not LLM tool-calling — keeping sources credible and cost near-zero. And retrieved values are sacrosanct: each is tied to a numbered evidence ID, and when a connector returns nothing, FACET says so rather than inventing it. In our demo the medicinal chemist refuses to cite PDB accession codes the evidence block didn't surface, flagging them as established knowledge instead. That refusal-to-fabricate is the feature: hallucination has almost no surface to occur on. FACET isn't a search replacement — it's a lens on top of one, plugging into the retrieval stack biopharma teams already run so the same corpus answers four people correctly instead of one approximately. Future roadmap: per-persona learned rerankers (evidence ordered by what each discipline actually cites), click-through citation traceability to every connector call and timestamp for a regulated-grade audit trail, and cross-persona collaboration on one shared query. FACET conditions the search stack a lab already runs rather than replacing it.
More from Built with Claude: Life Sciences
What I built. Lazarus is an agent that takes a dead research repository that is open, cited, and no longer runnable, and turns it back into a callable…

Diffuse midline glioma is a paediatric brainstem cancer that is almost uniformly fatal: about ninety percent of children die within two years, and the first…

Hippocampus AI is the memory and reasoning engine for scientific discovery. Every experiment generates valuable knowledge, but that knowledge becomes…

I used Claude to train a protein language model that can generate an extremophilic version of any enzyme, maintaining its function, while allowing it to…

Many primates are sexually dichromatic — males and females are different colors. I wanted to know how that trait is built genetically, and how it evolved…
About Trialign is a patient-facing clinical trial matcher for oncology. A patient pastes a clinical note, uploads a record, or connects their EHR via SMART on…
