EMET can now work directly with your internal documents, ELNs, reports, and experimental databases — connected to the public and paywalled evidence it already draws on, as well as to private data accessible only to your organization. Your answers stop being merely correct and starts being correct for your company.
The most valuable evidence is the evidence you already own — and can't reach
Every drug discovery organization sits on years of hard-won knowledge. The assay that finally worked, the target you walked away from, and why? Almost none of it lives in a public database. It lives in ELNs, study reports, slide decks, and structured systems like Benchling, Veeva, D360, and GDB none of which were built to be searched together, let alone connected back to the public literature.
So programs repeat dead ends. Decisions get made without the one internal result that would have changed them. "Has anyone here looked at this before?" takes days to answer, if it gets answered at all.
The memory exists. It just isn't reachable when you need it. Starting now, it is.
EMET meets your data where it lives
You don't have to move your data to get value from it. EMET connects to it the way that fits each source:
Connect live to structured systems and databases: — Benchling, Veeva, ELNs, and internal databases like D360 and GDB — with no ingestion required. This is where a lot of your key experimental data actually lives, and it has been the hardest to search and the hardest to connect to anything else.
Bring in documents: — an ELN entry, a study report, a slide, a memo — so a file that used to be impossible to find becomes something EMET can search, reason over, and cite.
Build a private knowledge graph for the deepest reasoning. For your most valuable, multi-source data, BenchSci structures it into a knowledge graph built for your organization and used only by your organization.
That last tier is what makes EMET different. It is not keyword search bolted onto your files, and it is not retrieval over a pile of documents. A private knowledge graph captures not just the text, but the entities and the relationships between them — which target, which assay, which result, and how they connect to each other and to the public evidence. Retrieving structured relationships is far more accurate than retrieving passages of text, so answers reflect what your data actually means, not just which words it contains.
Ask about a file or what was done before, and EMET uses your internal evidence as context alongside the 100+ public data sources it already draws on. Point it at a specific document, or ask a broad question and let it surface the relevant prior work on its own. Every answer stays traceable to where it came from.
Your data stays yours
Bringing internal data into any system raises a fair question: is it safe? For regulated biopharma, that is not a footnote — so we treat it as a first principle.
Your data is isolated to your organization. It is never pooled with another customer's, never added to a shared graph, and never used to train shared models. Access follows your existing permissions. Every answer that draws on internal evidence shows its source. EMET was built for enterprise use in regulated environments, and your internal data holds to that same standard.
Why it matters
For R&D leaders, scattered institutional knowledge becomes a durable asset. Your teams decide with everything your organization has already learned — including the negative results and internal know-how competitors will never see.
For scientists, "has anyone here looked at this before?" takes seconds instead of days, with the evidence attached.
Your answers are no longer just correct. They are correct for your company.
If you'd like to see EMET work with your own data, we'd love to show you.
