Empower your agents with structured, always-fresh research data on any topic.
Your agent declares the schema it needs. DeepSieve fills it from the public web, cites the sources it found, and keeps it current on its own schedule while monitoring is on — then serves it over MCP, CLI and REST. Research becomes a dependency you declare, not an errand your agent runs at request time.
Most agents know things in one of two ways.
Fast and free, and frozen at a cutoff it can't tell you. Your agent answers confidently about a world that has moved on, and nothing in the response says so.
Current, but slow, non-deterministic and un-auditable — re-derived on every call, priced on every call, and different each time. Nobody can tell you what it knew last Tuesday.
Researched once against the shape you specify, cited where sources were found, kept current on its own schedule while monitoring is on, and read like any other table. Same answer for every caller, with its evidence attached.
A dependency, not a tool call.
Describe the entities and columns your product needs — in plain language or through the API — and approve the schema before anything is built. It's versioned, it's yours, and the shape you get back is the shape you asked for.
A deep-research run populates it, recording the sources it found, a confidence score read against a published rubric where 50% means no signal, and the verdict of an independent QA pass over the cells it checks.
Every column carries a freshness window — ours by default, yours to change. While monitoring is on, a value whose window expires is re-checked, or flagged stale — never silently trusted — and the value it replaced stays in the audit trail.
Reads are ordinary API reads. The 15–60 minutes of a Standard run is what builds the table, not the query against it, so this can sit in a serving path rather than beside one.
Signed run and dataset.updated webhooks from Pro up, with cursor sync via updated_since — so when a re-verification supersedes a value, your system is told rather than left to poll.
Agent-first, not agent-compatible.
An MCP server with browser OAuth, a CLI, and installable agent skills — plus REST, on every plan including the entry tier. Point a coding agent at the setup file and it configures the lot itself. Then build the whole loop against dry_run, which is free and unlimited: real response shapes, sample cited rows, nothing billed and nothing persisted. You spend only when you ask for real research.
Set up DeepSieve by following the instructions here: /setup.md
It connects the MCP server, installs the skill, and appends its own section to your rules file. Browser login over MCP needs no API key at all.
Software is starting to configure itself. It still can’t check its facts.
The next generation of software assembles itself around a goal — it picks its own steps, writes its own glue, and decides what to do next. What it cannot do is know, on its own, whether the world still looks the way it did when it was trained.
So the industry has been treating knowledge as something to retrieve in the moment: a search call here, a scrape there, re-derived on every request, at latency, with no memory and no accountability. That is a workable trick for one answer and a terrible foundation for a system that has to act.
We think the right primitive is a declared, maintained dataset: you state the shape of what you need to know, the research runs once, and while monitoring is on a schedule keeps it true. The result is queryable like any other table, carries its evidence, and can be audited when it changes. That is what we mean by Research as Code — and we think autonomous systems are only as trustworthy as the facts they stand on.