A persistent, queryable graph of papers, trials, targets, drugs, and SEC filings — wired into your AI through MCP. Ask in natural language. Gyri executes the multi-hop traversals that take a postdoc a week, returns the answer in seconds, and remembers what it learned for next time.
Conversations end. Their reasoning doesn't. Each assertion your AI makes is recorded as a structured object with its evidence chain intact — addressable across sessions, models, and providers. Tomorrow's session inherits today's work.
MCP-native. Claude today, GPT tomorrow, the next thing after that — all read from the same substrate.
Targets to trials to sponsors to SEC filings. Diseases to targets to drugs to adverse events. One GraphQL query. One round trip. Provenance preserved at every edge.
Not a footnote — a typed reference the next model can re-fetch and re-evaluate. Disagreement gets localized to the exact binding that broke, not lost in prose.
No customer-data ingestion. Gyri queries against your sources at runtime, with your trust roots, your epistemology.
Each model invocation against your workspace deepens the graph: emits new claims, hydrates old ones, surfaces contradictions, accrues citations. The substrate is non-decreasing in value as your team uses it.
MONDO:0007739 (IPF) and pull associated targets.
maxScoreDirect ≥ 0.7 — strong genetic evidence.
indicationDrugs with phase + sponsor.
A six-source join that would take a research associate the better part of a week. Returns in 412ms with provenance intact.
drug:<slug> with mechanism + sponsor.
faersAdverseEvents rollup: maxLlr, totalReports, top events.
The query an analyst runs the morning before the earnings call. The graph crosses signal databases no chat tool has access to in one shot.
depmapEssentiality, geneBurden, geneticConstraint for druggability scoring.
googlePatentsSearch by gene symbol + mechanism class.
sponsor:<slug>; cross-check pipeline + trials.
Surfaces the lab that filed an IP claim but hasn't filed an IND yet. Cannot be reached by trial registries alone.
chemblTargets + proteinDrugs for primary and off-target hits.
faersAdverseEvents filter + patent-expiry cross-check.
The BD scout query. Three weeks of manual work returned in a single round trip with the failed-prior-attempts column already filled in.
sponsor:<slug> — entity with aliases + ticker.
A diligence pass that connects the science to the disclosure. The asset doing the most work in the deck is usually not the one doing the most work in the pipeline.
The query that turns "interesting paper" into "what does this mean for the field" without a week of literature work.
Hand a frontier model the same question. It will spend an afternoon scrolling the open web, burn hundreds of thousands of tokens making the same lookup over and over, hand you a wall of paraphrase, and then forget every word of it the moment you close the tab. A typed graph and a one-shot query do something completely different.
"Models are only going to get smarter — but whether they preserve what you taught them is entirely on you."
Gyri's biomedical workspace is in private access for research groups, biotech teams, and infrastructure builders working at the frontier. Tell us what you're researching.