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General

Semilattice tests how your users would react to product decisions before you ship them. It uses real human data to predict how specific audiences would answer questions they’ve never been asked.
An audience model represents a specific group of people: UK adults interested in consumer finance, software buyers, developers. It’s built from real human data and predicts how that group would answer new questions. You can use our 50+ ready-made audiences or work with us to create custom ones from your own data. In the API documentation, audience models are referred to as “populations.”
Every audience model is tested using leave-one-out cross-validation: each question in the seed data is temporarily removed and predicted as if it were new, then compared to the real answer. Our UK Consumer Finance audiences score 90% accuracy on this measure. You can see the accuracy score for every audience in the dashboard.
Yes. We’d love to work with you to build a custom audience model for your users, tested for accuracy using cross-validation. All your data and the model itself remains yours. Get in touch →
Currently single-choice questions, including scales. Anything you’d put in a survey: feature preferences, pricing sensitivity, competitive perception, behavioural intent. The model works best with questions that have distinct answer options.
Traditional research takes weeks and costs thousands. Semilattice returns predictions in seconds. It doesn’t replace deep qualitative research. It fills the gap between gut instinct and a full study, giving you directional signal before you’ve committed to a direction.
Semilattice is free to use right now. No credit card, no contracts. We’re building in public and want early users shaping what gets built.
Book a call and we’ll walk you through it. Or get in touch if you’d like API access.

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Developers

Populations are what the API calls audience models. A population is a model that represents a specific group of people. When you call the API with a population_id, you’re telling Semilattice which group to predict for. Populations are built from real human data (seed data) that determines prediction accuracy and subject coverage. You can browse available populations in the dashboard or list them via the API. Learn more →
Predictions are how you ask your audience a question via the API. You send a question with answer options to a population, and get back a predicted distribution showing how the group would respond. Predictions return population-level outputs (aggregated percentages), not individual responses. You can run single predictions or group them into batches for a project. Create a prediction →
Tests measure how accurately a population model predicts answers to questions where you already know the real answer. You provide ground truth data (real survey responses) and Semilattice compares the prediction against reality. This is different from the built-in cross-validation, which tests the model against its own seed data. Tests tell you how the model performs on your specific questions. Create a test →
The simulation engine takes a population model, a question, and answer options. It uses large language models to predict how the population would distribute their answers. The engine produces stable results: the same question returns similar distributions across runs. Watch the explainer →
Choose a population, send a question with answer options, and poll for results. Predictions typically return in ~20 seconds. Available via Python SDK, Node.js SDK, or REST API. Python quickstart →
The Semilattice MCP server lets AI assistants make predictions directly. Connect it to Claude, ChatGPT, Cursor, or any MCP-compatible tool. Your AI can ask your audience a question mid-conversation. Set it up →