Synthetic UXRGet started today
A behavioral model of your customers, built from your own research and product data. Run thousands of digital twins through what you are about to ship and see what they do, why, and what to change.
Start with a question. Sentient brings your research into the study.
Compare our new guest checkout with the current Nike experience. Use our customer research to build an audience and find what helps shoppers finish their purchase.
With support from
User research for the AI era
Recruiting, scheduling, and synthesizing a study takes weeks, so most decisions ship untested. Sentia runs agent swarms that represent your real users through the decision in hours, so every release gets researched.
Send an agent swarm that represents your real users through the complete journey, from first visit to confirmation. See where they go, where they backtrack, and where they give up, with the reasons in their own words.
Browse to confirmation · 100 twins · US footwear shoppers
Find the moments that stop customers and measure who pushes through. Friction is ranked by abandonment and persistence for every segment, so you know which fix recovers the most people.
Before a change ships, predict what customers will worry about and how strongly: price, trust, effort, risk. Each concern is pinned to the screen that raises it, so the fix is scoped before the first support ticket.
Put headlines, value propositions and microcopy in front of agents that read like your users. They report what they noticed, what they understood, and the exact phrase that lost their trust.
Sentia is trusted by UX researchers at
Sentia connects to the research, analytics, and delivery stack you already have, so twins are built from live sources and findings land where decisions get made.
Decks, transcripts, and survey exports become goals, frustrations, and behaviors, each traced to the sentence or row it came from. Open the citation behind any trait.
Shopper interviews and checkout research.
Every study adds to a living graph of your audiences, surfaces, and the behaviors connecting them. Ask how a segment reacted to checkout last quarter and the answer is already there.
Delivery cost uncertainty appears in 23 of 100 synthetic walkthroughs.
Point a panel at a prototype or a live URL and the twins click through it as users would. See where they hesitate, what they misread, and where they give up, on the screen itself.
Interview findings, survey responses, and checkout events inform each persona.
A verdict, the recommendations behind it, pins on the exact screens, usability scores, and the twins in their own words. Built for the meeting where the roadmap gets decided.
68 of 100 synthetic shoppers preferred the new checkout. A clearer guest path helped more participants finish, but delivery timing remained a source of hesitation.
Shoppers can continue without deciding whether to create an account.
68 of 100 preferred the new checkoutParticipants still look for a delivery estimate before committing to the order.
23 of 100 asked about delivery timingUnder the hood
Model, simulate, and calibrate on one integrated stack. Built from your data, measured against what your customers actually do.
Turn your interviews, surveys, analytics, and support data into a model of how each segment decides: goals, frustrations, habits, price sensitivity, trust, patience. Every value cites its source.
Query your segments, your product surfaces, and the behaviors connecting them. Every edge carries a predicted outcome, its confidence, its evidence, and its record against real releases.
Forecast pricing, flow, copy, and roadmap decisions per segment, with the reasons behind each and how often the model has been right on that surface. Ship the changes that move the number.
Measure every forecast against real behavior once you ship and fold the result back into the model. When your customers change, the model changes with them.
One model per workspace, isolated at the database layer and encrypted in transit and at rest. Your data trains your model and no one else’s.
Sentia MCP
Through the Sentia MCP, Claude Code, Cursor, or Codex can run a study against the branch you are working on and fix the friction before the pull request opens.
01Capture the local checkout flow.
02Use the audience grounded in customer research.
03Run the study and return usability findings.
Model the capacity you could free for research. Adjust these example inputs to reflect your team and the workflow you want to evaluate.
Digital twins are simulated users built from real data about your customers. In Sentia, each twin is spawned from your own interviews, research documents, and product behavior, then run through decision scenarios such as a new flow, design, or price. The result is synthetic user research grounded in your data: a read on how your users are likely to respond before anything ships.
Most synthetic research starts with a stock persona and asks a model to improvise. Sentia starts with your first-party data. Every twin is grounded in what your customers have actually said and done, and every finding links back to that evidence, so the simulation does not hallucinate a user you do not have.
Twins behave according to principles that psychology and behavioral economics have relied on for decades: personality traits, loss aversion and prospect theory, habit formation, social proof, status quo bias, anchoring, and cognitive load. Those models, combined with your data, decide how a twin reacts, so behavior is explainable rather than improvised.
Figma prototypes, live websites and products, pricing changes, messaging, onboarding flows, and roadmap options. Set the research question and the audience, then review observations, screen evidence, and recommendations.
Every simulation is a forecast of how each customer segment will behave, with a confidence range rather than a single number. When the change ships, the Sentia SDK or your analytics tools report what real customers did, and every forecast is graded against it: how right it was, and whether it was as confident as it should have been. The model updates from each result. Forecasts that held get tighter, surprises widen the range, and when your customers start behaving differently, the model notices. Each workspace owns its own model, isolated and encrypted, trained on its data and no one else’s.
No. Sentia is where your team tests early and often, so the studies you run with real customers start from sharper questions. Twins are simulated users, and their findings are directional evidence to act on and to validate with real people. We recommend running both.
In the web app, or from Claude Code, Cursor, or Codex through the Sentia MCP, where you can run studies and test local prototypes without leaving your terminal.
Let’s talk
Show us the design, flow, or price you need to decide on. We’ll walk through how digital twins of your users could tell you what to change before you ship.
