When we first built Om, we needed a way to prioritize ~17K proteins for screening. That internal tool—automated literature review, opportunity scoring, and multiplexed referencing—became Diligence. Today it’s the same system we give customers: the UI provides instant summaries, and the API exposes the exact endpoints we use in production.
Search across the proteome
Start at /diligence/search to look up any emerging target, browse our growing library of protein profiles, and open the Diligence cards that bundle the core sections researchers ask for—clinical trials, therapeutic strategy, disease association, biology, pathway context, and more. Each section links directly to the cited publications so you can verify the evidence without reading hundreds of PDFs.
Request fresh runs when something is missing
If a target hasn’t been synthesized yet, use the in-product “Request Free Diligence Run” flow to notify our team. We’ll run the pipeline, add the target to the shared catalog, and let everyone searching that protein benefit from the new analysis.
Automate with the API and credits you already have
Everything in the UI is mirrored by the API. Call POST /v2/diligence/generateClaims (300 credits, i.e., $3.00) when you need validated evidence snippets, POST /v2/diligence/synthesizeReport (500 credits) for a structured brief, or POST /v2/diligence/deep-research (500 credits) for bespoke prompts. GET /v2/diligence/gene-keys stays free so you can enumerate what we already believe to be true. Jobs settle through the same credit ledger the rest of the platform uses—1 credit still maps to $0.01 USD—and you can poll /v2/jobs/* to track status.
Ship diligence faster
Grab an API key, fund your wallet, search the UI to orient on a target, and kick off programmatic workflows when you need datasets you can plug into downstream tooling. Because Diligence runs on the same infra that powers our data-access products, every result is citation-backed, auditable, and ready for the next meeting.
