An analyst that can’t make things up.
Sarah is InCite’s built-in AI advisor. Because she works inside a financial and compliance platform, a made-up number isn’t just unhelpful — it’s harmful. So we built her to surface only what’s actually in your data, to cite where every figure comes from, and to say “I don’t have that” instead of guessing.
She answers from your records — not the internet.
Every answer is assembled, in the moment, from your organization’s own uploaded documents and validated records — scoped to your tenant and no one else’s. Sarah is explicitly instructed to answer only from that context, never to fill a gap with a benchmark, an industry average, or a plausible-sounding estimate.
- Your data only. The context is built from your authenticated organization’s files — another tenant’s data can never enter it.
- Verified snapshots, not live guesses. Documents are read and summarized once at upload; Sarah works from that stable, parsed text — not a fresh re-interpretation each time.
- No web for your numbers. Sarah is barred from using web search for your organization’s own figures — web access is reserved for public, regulatory facts only.
Every number is pulled, checked, and kept with its source.
Key figures — revenue, expenses, headcount, funding, contract values — aren’t paraphrased from prose. They’re extracted into typed fields, validated against a schema the moment they’re ingested, and stored with a record of where they came from. A value that doesn’t pass validation is flagged, not saved.
And when two documents disagree, InCite doesn’t average them or pick at random — it defers to the more authoritative source.
If she can’t show you the source, she doesn’t claim it.
Sarah’s structured outputs require evidence. Every assessment must name the specific document or data point that supports it; if it can’t, the supporting evidence stays empty and the confidence is set to zero. Nothing is asserted without a traceable basis.
Your committed revenue drops about 68% by mid-2027 if nothing renews — driven mainly by two federal contracts ending in 2026.
- Evidence required. Each claim carries the source behind it — surfaced as supporting-evidence boxes you can open.
- Honest confidence. Every assessment carries a 0–100 confidence score, set high only with strong, specific evidence; low-confidence items show their gaps instead of inflating.
- Structured, not freeform. The model fills a validated schema — a field with no evidence stays empty, never invented.
- Charts can’t lie. Visuals are drawn from canonical records by exact ID; incomplete inputs show a gap label rather than a fabricated shape.
“I don’t have that yet” is a valid answer.
Where the data is thin, Sarah tells you exactly what’s missing and why — a named list of the documents or data points she’d need. You always see the gap; you never see a confident answer built on nothing.
Sarah flags. People decide.
When Sarah spots an inconsistency across your data, she surfaces it for review — open, visible, and dismissible. She never silently acts on a finding or edits your records on her own. The judgment stays with you.
The rules she runs under.
These instructions ship in Sarah’s system prompt on every message — non-negotiable, and applied before she ever sees your question:
- Web research is off by default. A platform operator must enable it per organization, and even then it’s limited to public, regulatory information.
- No memory across sessions. Each conversation starts fresh from your current data — so answers can’t drift or go stale on yesterday’s numbers.
- Your question is kept separate. What you type is never folded into Sarah’s instructions, so a cleverly-worded prompt can’t talk her out of her guardrails.
What Sarah will not do.
When the data isn’t there, she says so — and tells you exactly what would fill the gap.