Blok provides a platform for simulating product experiments using synthetic user profiles modeled after real behavioral data. This allows product teams to validate assumptions and compare design variants predictively before committing development resources. The service helps de-risk product decisions by offering directional insights on user response across different personas.
Funding
$7.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Product teams often rely on gut feel or lengthy A/B tests that require significant traffic and expose real users to potentially flawed changes, leading to delayed decisions and wasted engineering effort.
Solution
Blok offers an AI‑driven simulation platform that generates synthetic user agents based on a company’s actual usage data and established behavioral science models. These agents emulate distinct user personas—such as high‑intent shoppers, skimmers, or risk‑averse users—and predict how each would interact with proposed product changes. By running virtual experiments on landing pages, onboarding flows, pricing structures, feature placements, and copy variants, teams receive directional, explainable insights before any code is shipped. The platform delivers results in hours, allowing rapid prioritization of ideas, reduction of live‑test risk, and more efficient allocation of engineering resources.
Target Audience
Primary users are product managers, UX researchers, growth and design leads who need to validate onboarding, conversion, activation, and feature‑adoption hypotheses; marketing teams also use the platform to pre‑test copy and messaging.
Features
- AI agents trained on proprietary behavioral models and real product interaction logs, producing persona‑specific simulation outcomes
- Predictive experiment engine that evaluates design variants across multiple user clusters without live traffic
- Dashboard for side‑by‑side comparison of simulated metrics (conversion, activation, retention) with drill‑down explanations of driver factors
- Seamless data ingestion via CSV upload or API integration, supporting continuous model fine‑tuning through backtesting
- Scenario library covering landing pages, signup flows, pricing pages, feature placement, CTA copy, and prototype usability tests
- Exportable reports and API endpoints for embedding insights into existing product analytics or road‑mapping tools
- Explainable AI outputs that highlight which heuristics or biases influence simulated user decisions, aiding hypothesis generation
- Enterprise controls for permission management, versioned simulations, and audit trails to ensure reproducibility