Hire your AI Researcher
Customer interviews, market scans, competitor teardowns — synthesized into decisions, not 40-page decks nobody reads.
Your AI Researcher answers the questions that drive decisions: Who is our actual ICP? What do customers say unprompted? What are competitors shipping this quarter? Where is the market moving? It runs interviews, scans public data, synthesizes transcripts, and produces decision memos — not slide decks.
What your AI Researcher does
Workflows on autopilot
Without vs With a AI Researcher
- —You run 2 customer interviews a year when desperate
- —Competitor research happens when you see a Twitter thread about them
- —ICP doc is 18 months old and describes customers you no longer serve
- —Decisions made on vibes and 2 Slack conversations
- —You hire a research agency for $40K per project
- ✓AI runs structured interviews monthly and synthesizes them
- ✓Monthly teardowns land in your inbox before you'd have noticed
- ✓Living document updated every quarter against actual revenue data
- ✓Decision memos with primary data and a dissenting view
- ✓Researcher runs continuously for a fraction of that cost
A day in the life of your AI Researcher
Tools your AI Researcher uses
Frequently asked questions
Can an AI actually conduct customer interviews, or does that still need a human?
The right model: AI recruits, schedules, takes notes, and synthesizes. A human conducts. Tycoon's AI Researcher handles 80% of the workflow automatically — identifying who to talk to from your customer list, writing outreach emails with incentives, booking calendar slots, sending transcripts into the research repo, and producing the synthesis. The founder runs the 30-minute call itself — because this is the highest-leverage founder activity and shouldn't be automated. Pieter Levels and Matthew Gallagher (Medvi) both cite customer interviews as the thing they refuse to delegate; the AI frees up hours around each call so the founder can do more of them.
What's the difference between an AI Researcher and an AI Data Analyst?
The AI Data Analyst works with quantitative data — SQL, dashboards, PostHog events, cohort analysis. The AI Researcher works with qualitative data — interviews, surveys with open text, competitor moves, market narratives. They're complementary. When you ask 'why is retention dropping?', the Data Analyst tells you which cohort and when; the Researcher tells you why by running 10 interviews with churned users. In Tycoon, the AI CEO knows which role to assign which question and coordinates handoffs automatically.
How does it handle ambiguity in research findings?
Every synthesis includes a confidence level and lists the dissenting data points. If 4 customers say they want feature X and 1 says X would ruin the product, the memo names the 1. AI Researcher is instructed to over-weight disconfirming evidence — a mistake human researchers often make (telling founders what they want to hear) is one the AI is prompted against. If findings are genuinely inconclusive, the memo says so and recommends what additional data would resolve the ambiguity rather than forcing a decision.
Can it replace a Product Marketing Manager or ICP consultant?
For a one-person company, yes — the AI Researcher handles what a PMM or ICP consultant produces: interviews, market landscape, positioning, ICP. For a growing team with PMs, designers, and marketers who need shared research infrastructure, you'll eventually hire humans. The inflection point tends to be around $5M ARR or 20+ customer-facing teammates. Below that, the AI Researcher gives you the outputs without the coordination overhead. Polsia and Medvi both ran research entirely AI-driven until well past $1M ARR.
Where does it get market data — is it making things up?
Primary data comes from your own interviews, surveys, and customer database — always cited with source. Secondary data comes from public sources (G2, Capterra, industry reports, Ahrefs/SimilarWeb for competitor traffic) with links in every memo. The AI Researcher is prompted to cite every non-primary claim and to flag when a claim is inferred vs. sourced. If you ask a question where no public data exists and you haven't run primary research, it will tell you that and recommend what data would answer the question — instead of hallucinating a number.
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