Observant talks to each of your users one-on-one and keeps learning automatically — following up in the moment, relaying your team's questions live, and pipes user truth straight into the agentic loop.
Agents write, design, test, and ship. What they can't do on their own is reach a real person and ask how they actually use it, or what they think. Learning from users is still done manually.
01Across the ~20 founders and operators we've talked to building with AI, the same gaps came up:
The unit of learning shifts — from the study to the individual. Instead of standing up a study when you have a question, Observant learns continuously from each user, and insight rises on its own.
| The old way | The new way — on autopilot |
|---|---|
| inquiry-driven, ad-hoc studies | bottom-up, always-on product discovery |
| relies on a fixed sampling frame | 1:1 at scale |
| research plan → alignment → recruitment → data collection → insights buried in Notion | runs itself |
| decoupled from behavioral data; effort to combine sources | all data plugged into your agentic workflow |
Connect your data (PostHog) or invite your users directly — bring your own users in. They become your standing panel of consented users.
No users yet? Observant can start you on a matched research panel — but it shines when you already have users to learn from.
04Each user chooses where the 1:1s happen — in context, where they already are.
| Category | What it really is | Who operates it | Cadence |
|---|---|---|---|
| Survey tools Typeform · Qualtrics | a narrow execution tool — one methodology | you + a research team + ops | weeks per study · snapshot |
| AI interview platforms Listen Labs · Outset · Synthetic Users | a narrow execution tool — one methodology | you + a research team + ops | hours per study · snapshot |
| Traditional research SaaS Dovetail · UserTesting · Maze · dscout | a narrow execution tool — one methodology | you + a research team + ops | weeks per study · snapshot |
| Observant | a methodology-agnostic system | runs itself | continuous |
Agents now write, design, test, and ship continuously — but they can't generate human judgment on their own, and there's no direct way for an agent to reach a person, get a useful answer, and keep going. The Human API is that missing infrastructure: agents ask → real humans answer → structured signal returns → the agent acts, at agent speed.
Many companies will be built in this category. Our entry point is the most acute pain — automated user learning — and from there the panel and methodology compound into the layer every agent queries for human truth.
07Xuan has lived the early-startup grind from the inside, and learning from users has been her life's work since — the problem she cares about most. So much is still out of reach: scalable, automated user learning most teams can't do, for lack of awareness, access, know-how, and capacity (until now). She's building Observant for the builders she's been one of — to change all of that.
One of the first 60 employees at Robinhood, where she built and led user research through Robinhood's highest-growth years — driving 0→1 research for flagship products like Options, Cash Management, Banking, and more. She went on to lead monetization research at pre-IPO Airbnb and Instagram, and stood up research from scratch at SmartNews and Wyze. PhD from the University of Michigan.
09