A complete brief
We put the decision, the constraints, and the missing context in one place so the models are not answering a thinner question than the one you have.
Service
AI-Assisted Research
High-stakes questions fail when the context is broken and the prompt points the model at the answer you already wanted.
What this is
Useful research needs complete context, more than one model, and a check on bias.
Companies want AI to help inform large decisions about a pivot, a new market, or an investment. The usual attempt feeds a chatbot a partial brief and gets a confident, narrow answer. We run a research system across Claude, ChatGPT, and Perplexity, gather the sources, and return findings that can actually change the approach. The same system can run on a schedule for competitive research, new markets, and investment questions, and it improves when people mark what was useful.
What we build
The visible experience and the system underneath it.
Several models, one finding
Claude, ChatGPT, and Perplexity run in parallel. We aggregate the sources and surface where they agree, disagree, or skip something important.
A recurring system
The same pass can watch competitors, new markets, and investment questions on a schedule, and tighten itself from human feedback.
How the engagement moves
A real sequence, from ambiguity to something the team can run.
- 01
Define
Write the decision, the context that must travel with it, and what a useful answer would let you do next.
- 02
Implement
Run the research system, compare sources, and return a finding with the disagreements still visible.
- 03
Improve
Mark what helped. The next run starts from that correction.
A good fit when
- A decision is large enough that a thin prompt is a risk
- Competitive or market research needs to run on a cadence
- Leaders have used AI for research and not trusted the result
What changes
- A brief the models can actually use
- Findings that have been checked against more than one source
- A research pass you can run again when the question changes
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