Premium evidence, met by an agent that knows biopharma.
Neither half is enough alone. The deliverable is only as good as both together.
The answer is only as good as what it reads.
Fourteen classes normalized to one entity model — company, asset, target, indication, trial — so a query about one molecule reaches every source that discusses it, in any language.
The four highlighted are licensed and closed. They are the reason the answers differ from a general assistant’s.
Connected read-only, private to your tenant, never used for training.
It knows the deliverable before it reads a word.
The shape of the work, which source answers what, and which numbers cannot be compared. A chat model improvises all of that per prompt.
Knows what a landscape contains.
- §1 Standard of care and unmet need 2L, post-IO
- §2 Competitive set, by mechanism and line by line
- §3 Pivotal data, normalised by population ORR · PFS · OS
- §4 Catalysts and readouts, next 18 months dated
- §5 Where the position is still open white space
Sends each section to the sources that can answer it.
Refuses to compare apples to oranges.
Writes the file, every number sourced.
Before the first search.
- What a TPP, a funnel and a landscape must contain.
- Which evidence class answers which question.
- That 1L and 2L response rates are different numbers.
- That consensus is a dated forecast, not an outcome.
Files your team can actually work in.
A chat answer still has to be turned into the deliverable. Mindgram writes the deliverable — in the format the next person in the chain expects.