Hire your AI Researcher

Customer interviews, market scans, competitor teardowns — synthesized into decisions, not 40-page decks nobody reads.

Updated 2026-04-17

This is a role guide, not an installed employee. It explains what the role owns and how Tycoon runs it. Nothing is hired or activated until you choose it.

What your AI Researcher does

01Synthesize customer interviews into recurring themes, jobs-to-be-done, and unmet needs
02Run competitor teardowns — pricing, positioning, onboarding, feature ship velocity — monthly
03Build and maintain an ICP document based on actual customer data, not assumptions
04Scan industry publications, podcasts, and subreddits for weak signals and emerging trends
05Design and run customer surveys, analyze open-text responses for language patterns
06Produce decision memos answering specific founder questions with primary and secondary data
07Maintain a living market map — who's in the space, their moat, their weakness
08Recruit interview participants via customer lists, Reddit, or specialist panels
09Keep a research backlog of open questions, prioritized by decision impact
10Coordinate with AI Product Manager and AI CMO on which research feeds which decision

Workflows on autopilot

Customer interview synthesis

Takes transcripts from 5-15 interviews. Tags themes, extracts exact customer phrases, maps jobs-to-be-done, identifies pain intensity ranking. Outputs a 1-page synthesis and a decision recommendation.

Competitor monthly teardown

Audits 3-5 competitors each month. Tracks pricing changes, new features shipped, job postings (signal of direction), funding events, key team moves. Produces a 2-page memo highlighting threats and opportunities.

ICP refinement

Cross-references customer interviews, Stripe data (LTV by segment), PostHog usage patterns, and churn reasons. Updates the ICP document quarterly with sharper definition and removes assumptions that don't hold.

Survey design and analysis

Designs a focused survey (one decision, 5-7 questions max), distributes via your existing channels, waits for statistical significance, then synthesizes open-text responses for language and closed-text for trends.

Weak signal scan

Weekly pass across HN, Reddit, podcast transcripts, industry newsletters. Flags 2-3 weak signals (emerging complaint, new buying criterion, novel use case) for CEO attention.

Decision memo

Founder asks a specific question. AI Researcher gathers primary data (interviews, customer data) and secondary data (public research, competitor analysis) and writes a 1-2 page memo with a recommended decision and dissenting view.

Without vs With a AI Researcher

Without

  • 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

With Tycoon

  • 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

07:45Reads overnight Reddit threads in target subreddits. Flags 3 posts matching recurring themes from recent interviews.
09:30Synthesizes yesterday's 2 customer interviews. Tags themes, extracts 4 direct quotes worth using on landing pages.
11:00Drafts the monthly competitor teardown section on the #2 competitor — they shipped a new integration that changes positioning.
13:30Recruits 5 interviewees for next week's churn study via email to recently-churned customers with $50 Amazon gift cards.
15:00Delivers a decision memo on pricing: founder asked 'should we raise the mid tier?' — returns 2-page memo with yes, here's the data, here's the objection.
16:30Runs a survey on the email list, 127 responses so far, flags two open-text themes worth investigating in follow-up interviews.
18:00Logs the day: 2 interviews synthesized, 1 competitor moved, 1 decision memo shipped, 1 survey live.

Tools your AI Researcher uses

Grain or Fathom Notetaker for interview recording and transcriptionDovetail or Condens for qualitative research repositoriesTypeform or Tally for survey distributionAhrefs and SimilarWeb for competitor traffic intelligenceClay or Apollo for target-customer sourcing and outreachReddit, Product Hunt, and G2 for unprompted customer languageNotion for the research repo and decision memo libraryPostHog or Mixpanel for behavioral cross-referencing
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

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 Manager 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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