An AI-powered commercialization intelligence platform for evidence-based customer discovery.

What Custard is, and what it isn't

A focused platform for evidence-based customer discovery.

What it is

• An AI-powered commercialization intelligence platform.
• A project-centric hub for customer discovery, evidence, and hypotheses.
• A living knowledge base built from interviews, documents, and outreach.
• Designed for researchers, TTO teams, and student commercialization teams.

What it isn't

• Not a generic chatbot.
• Not a CRM built to close deals.
• Not a black-box tool that fabricates market evidence.
• Not a substitute for talking to real customers.

The problem

Customer discovery evidence stacks up fast.

Every interview adds insight, but it also adds more material to organize, connect, and revisit. The bend starts around 40 to 50 calls, well before the 100-interview milestone.

• Volume overload. Notes, transcripts, hypotheses, and follow-ups start to pile up.
• Memory strain. It becomes harder to remember who said what, and why it mattered.
• Pattern blindness. Important patterns can hide inside repeated conversations.
• Coordination drag. Teams lose time stitching together evidence across scattered tools.

From gut feel to evidence

Repeated interview themes should not live as gut feel. They should become evidence.

Without a system, you start saying "I keep hearing this", patterns live in memory rather than evidence, and confidence rests on instinct and recall. That is gut feel.

A good system tracks repeated themes across interviews, links evidence back to transcripts and notes, makes contradictions and weak signals visible, and lets patterns be reviewed, shared, and tested. That is evidence.

What Custard does

Custard builds a knowledge base that grows with you.

• End-to-end workflows. Custard connects discovery, analysis, outreach, and decision-making in one continuous workflow.
• A knowledge base that keeps growing. Custard captures transcripts, documents, hypotheses, and outreach inside one project-centric hub.
• Connections you might miss. AI helps connect similar themes, contradictions, and weak signals across interviews.
• The next best question. Custard nudges the team toward the next question to test and the highest-leverage next step.

Hypothesis, discovery, analysis, knowledge base, next action: from research workflow to cumulative commercialization intelligence.

The loop

Discovery works as a loop.

New evidence refreshes the system, reveals what is still unknown, and points the team to the next question.

1. New evidence. A transcript, focus group, or note enters the system.
2. System refreshes. The knowledge base and strategic analysis update.
3. Unknowns surface. Gaps, assumptions, and next questions become visible.
4. Next research. The next interview or focus group generates new evidence.

The value is not just storing research. It is creating an AI learning loop that gets smarter with every round.

Design principles

What a strong discovery system should be built to do.

• Collaborative. Built for teams, not solo note-taking.
• Always learning. The system continuously updates as new evidence arrives.
• Evidence-grounded. Insights tie back to transcripts, notes, and source material.
• Transparent. You can see how conclusions were formed and what supports them.
• Pattern-seeking. The system surfaces repeated themes, contradictions, and weak signals.
• Next-question oriented. The goal is helping teams decide what to test, ask, or learn next.