Observant.

Put user learning
on autopilot.

Observant (observanthq.com) is the world's first agent that automatically learns from your users. It knows what your teams care about, watches your user data, talks to the right users on its own — and the why lands on your desk before you knew there was a question.

The problem

Building software is automatic now. The feedback loop isn't.

Traditionally, "talking to customers" takes a lot — study design, alignment, separate tooling for data, recruiting, and feedback collection. It's fragmented, ad-hoc, and ops-heavy.

We're changing this. The better way: your agent executes all the background work, and you simply get proactive user truth, ready to act on.

Customer research

Learning from users is the new bottleneck.

Across the ~20 founders and operators we've talked to building with AI, the same gaps came up:

Research is a chore — finding, recruiting, and scheduling the right people.
Random, not systematic — builders rely on signals from social, Discord, or Slack with customers; they're sporadic and unstructured.
They lack the research know-how — to ask the right questions and read the answers.
They want their behavioral data connected, not siloed — the truth sits unread in their analytics.

"I just wish every Tuesday between 1pm and 3pm I could talk to customers — and there's just customers there for me to talk to."Chris, Founder

"All the questions come to me… Some PM says 'I want the result by end of this month' — and we can't cover it. So those questions are in the air. No one answers them."Kai, Researcher

The reframe

Observant completely reimagined how companies learn from users.

The unit of learning shifts — from the study to the individual. And the layer shifts: every tool out there automates the execution layer. Observant owns the proactive layer — it generates the inquiry itself.

The old way	The new way — on autopilot
built for research teams & insights buyers — the existing org chart	built for the developer workflow
inquiry-driven, ad-hoc studies	bottom-up, always-on product discovery
waits for a human to hand it a question	generates the inquiry itself
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

How it runs

Install once. Learning starts.

Install a code snippet — an in-product agent with reserved real estate in your product (guardrailed touchpoints, freeze policy) runs short surveys and 10-minute voice interviews, or invites users into your feedback program — activating email and IM for anything deeper.
Turn on autopilot programs — customized user sentiment, product discovery, and user journey health start learning from your users from Day 1.
Dispatch your own line of inquiry — from the dashboard or an MCP client. The agent decides the modality, light or deep, reaches the right people in-product or through the feedback program, collects the data, and returns insights.

Landscape

No one is tackling the full loop.

Category	What it covers	Built for	Who operates it	Cadence
Data analytics platforms Amplitude · Mixpanel	dashboards that show what users do — but not why	data & product teams	you + instrumentation + analysts	continuous · behavior only
Survey tools Typeform · Qualtrics	a narrow execution tool — one methodology	research teams & insights buyers — the existing org chart	you + a research team + ops	weeks per study · snapshot
AI interview platforms Listen Labs · Outset · Synthetic Users	one phase of execution — ad-hoc; still ops-heavy for study design, internal sampling, recruitment	research teams & insights buyers — the existing org chart	you + a research team + ops	hours per study · snapshot
Traditional research SaaS Dovetail · UserTesting · Maze · dscout	one phase per tool — still ops-heavy, ad-hoc, and you stitch the pipeline yourself	research teams & insights buyers — the existing org chart	you + a research team + ops	weeks per study · snapshot
Observant	the whole pipeline — it generates inquiries and executes on them	the developer workflow	runs itself	continuous · what + why

The vision — our big bet

The next generation of user intelligence.

We're creating the category that cuts across the User Intelligence market: for the first time, the thing doing the learning has full awareness, full visibility, and full initiative.

Human signal is the scarce primitive of the agentic era. Our work is to build this critical infrastructure that can pump high-quality, actionable user truth into your build.

Team

Xuan Zhao — co-founder & CEO. One of the first 60 employees at Robinhood, where she built and led user research through Robinhood's highest-growth years — directly responsible for the 0→1 research effort for most of its flagship products, including Options ($222M, Q4 2024), Cash Management ($296M, Q4 2024), Banking, Web, Passive Investing, and Robinhood Learn. 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. She believes the way companies stay user-centric is due for its biggest reinvention in decades. She founded Observant to accelerate it.

Zhifei (Jeffrey) Song — co-founder & CTO. Fifteen years building large-scale backend, ML, and AI systems at startups and public tech companies. At LinkedIn, he built and led engineering teams behind recommendation systems, LLMs, and AI platforms serving a billion members. The lesson that brought him here: the hard part isn't model capability — it's building systems that continuously learn from users and improve over time.

Contact: xuanzhao630@gmail.com · jeffrey.listening@gmail.com