Research Machine
Produces market and competitor intelligence in which every claim carries a source, or it does not ship.
The problem
AI research output is fluent and frequently wrong, which is the worst possible combination for a document someone is about to make a decision on. A confident unsourced paragraph is more dangerous than having no research at all.
The solution
Research Machine gathers, reads and synthesizes — then checks itself. Every factual claim has to resolve to a retrievable source. An unsourced claim blocks the report and is either sourced or cut. That gate is exactly what a research process usually lacks.
What's inside
Features
Every claim carries a source
A factual statement without a retrievable citation does not make it into the document. The bibliography is the gate, not the appendix.
Adversarial second read
A separate agent argues against the findings and flags what the first one assumed rather than established. Agreement is not evidence.
Competitors read, not guessed
Positioning, pricing and messaging pulled from current public sources, with the retrieval date recorded against every finding.
Says plainly what it does not know
Gaps and low-confidence areas are stated outright, because a decision made on an invented certainty is the expensive kind of mistake.
Anything can generate. A machine earns the name when something can automatically reject its work and make it try again.
- Goal
- A decision-ready brief in which every factual claim resolves to a retrievable source.
- Verify
- Each claim checked against its citation, and an adversarial reviewer agent attempts to refute the findings; unsourced claims block the report.
- Stop when
- Every claim is sourced and survives the adversarial pass, or the open questions are reported as open.
In Development · no client results published yet
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