A researcher that shows its work.
Describe an area of the world you need tracked — no list required. DeepSieve designs the database for it, researches the public web to fill it, and tells you what it could and couldn’t verify. Then, while monitoring is on, it keeps the whole thing current.
“Track the vendors in clinical-trial software — who owns them, what they charge, what they just shipped.”
You describe the domain. It drafts the schema.
Write what you want tracked as a paragraph of plain English. An inference pass turns it into a Blueprint — the entities, the columns and their types, the freshness window on each one, and the prompts the research will run. You edit it, and nothing is built until you approve it. The Blueprint is the source of truth for everything downstream: the tables, the extraction schema, the prompts, the lifecycle. Change it later and safe changes apply immediately, risky ones ask you to confirm, and destructive ones are refused rather than silently applied.
“Track the vendors in clinical-trial software — who owns them, what they charge, and what they just shipped. Keep pricing current.”
the cyan value is each column’s freshness window — edit any of it before a table exists
You don't have to know the list first.
A brief on its own is a valid run: the agent maps the area and finds the entities, which is both the cheaper shape and usually the more complete one — in a captured run, 35 of the 38 rows were entities it discovered rather than names it was handed. Where you do already know some, paste URLs or just type names; anything you paste is read before research starts, and a classifier files each one under the entity types your own Blueprint defines rather than somebody else's.
It reads the public web — properly.
For each entity, an autonomous research agent plans its own investigation: what to ask, where to look, what to read next. It works deep into the public web — official sites, press coverage, filings where they are public — with entities researched in parallel, and writes a long-form, citation-rich dossier as it goes. If a research stream goes quiet the orchestrator reconnects and presses on; it never quietly falls back to a thinner model.
Findings become your table, not an essay.
A second pass reads each dossier and sieves the findings into structured records — against the schema you approved, whatever that schema is. Wide schemas are split into shards so the extraction stays strictly enforced rather than degrading silently. Empty fields aren't shrugged off: targeted follow-up searches go out to fill the gaps, field by field, until the table is as complete as the public record allows.
Every value carries a confidence verdict.
Nothing lands as a bare claim. Each value is written alongside an evidence row carrying a confidence score, and the sources behind it where sources were found. A separate QA agent — one that played no part in the research — then re-reads those sources against the claim and records a verdict on each cell it checks: supported, contradicted, corrected, or not verifiable. Where it couldn't check, it says so rather than passing the cell as verified. You are told which is which, in the table.
And then it keeps going.
This is the part that makes a dataset worth more than a search result. Every column carries a freshness window — ours by default, yours to change. While monitoring is on, a poller watches for values whose window has elapsed and schedules a re-verification run for them; what comes back is either confirmed, or replaced with the old value preserved in a supersede chain you can audit. Nothing is quietly overwritten and nothing is silently trusted: while monitoring is on, a value that can't be re-checked is flagged stale rather than left looking current. Every paid run includes 30 days of monitoring, and after that your plan keeps a set number of monitored runs fresh on the schedules you chose.
superseded 2026-07-21 · previous value kept in the audit trail
Don’t trust it. Inspect it.
A real brief, the schema it produced, and the rows it returned. Hover or tap any cell for its evidence — the links go to the live web, and two values are marked uncertain rather than guessed.
“Track the major US EHR platform vendors — founded, HQ, ownership, flagship product, estimated revenue. Keep ownership and product facts current.”
| Vendor | Founded | HQ | Ownership | Flagship product | Est. revenue |
|---|---|---|---|---|---|
| Epic Systems | 1979 | Verona, WI | Private — founder-led | Epic EHR | uncertain |
| Oracle Health | 1979 | Kansas City, MO | Oracle — acquired 2022 | Millennium EHR | Within Oracle segment |
| athenahealth | 1997 | Boston, MA | Bain Capital & H&F — 2022 | athenaOne | uncertain |
And for the AI-native in the room…
Research you can audit.
The output isn’t a chat answer — it’s a table you can defend in a meeting, with the sources to prove it.