The Situation
A marketing company came to me because research was eating their time, and the AI tools meant to speed it up were creating a new problem:
- AI answers sounded confident, even when they were wrong
- Sources were sometimes made up entirely, and checking them by hand took longer than the research
- No way to tell which findings were proven and which were just someone's claim
- Every new project started from scratch, because past research was scattered or lost
- The AI would quietly drift from the question they actually asked to an easier one
They did not need faster answers. They needed answers they could put in front of a client.
What I Built
An AI research team, not a single chatbot. Five specialists work on every project in turn, each with one job, and each checking the work of the one before.
The Researcher
Searches widely for everything relevant to the question: approaches, tools, examples and what the experts are saying. Everything it brings back is treated as a claim to be checked, never as a fact.
The Tester
Tries out the most promising ideas before anyone relies on them, in a safe, separate space where nothing can touch the company's real accounts or data.
The Observer
Checks what really happened in each test against what was reported, and keeps watch that the project is still answering the question that was actually asked.
The Judge
Deliberately tough. Its starting position on every claim is "prove it". Important claims go to a vote, and only the ones that survive are marked as verified. It catches made-up sources before they reach a report, and it will not let the research be steered away from what the team said mattered most without their say-so.
The Writer
Turns only what the Judge approved into a clear report, with every finding linked to the evidence behind it, then files it all in the company's research library.
The Infrastructure
Every finding carries a label: verified, reported but unproven, or disproven. Nothing is quietly deleted. What turned out to be wrong is kept too, so nobody wastes time on it again. Everything lands in one research library the team can search, and each new project checks it first, so the company never pays to research the same thing twice. Nothing is saved, published or spent without a person's go-ahead, and if an approach fails twice, the system stops and rethinks instead of trying a third time blindly.
The Results
What Comes Next
The research library gets more valuable with every project the team runs. The same team can take on new kinds of questions, from campaign strategy to new channels and tools, and every answer starts from everything they already know.