AI agents that behavelike your customers.
Predict A/B test outcomes from your own user data — direction, effect size, and calibrated confidence. In minutes, not weeks.
Predictions locked before you reveal the answers.
Learning is rationed by traffic.
Four numbers set the ceiling on how much any team can learn in a quarter — and what it costs.
experiments that succeed at Google, Bing, and Booking.com1
of live traffic for one answer
experiments per quarter, ceiling, at 10% allocation
lost a year to failed variants on live traffic, for a $15–50M business (illustrative)
The failed ones aren’t free. For weeks, real users live inside the failed variant — slower checkouts, worse rankings, prices that feel wrong. That’s not just spent traffic; it’s spent trust.
An average test needs four weeks and a 10% slice of traffic. That’s a hard ceiling of ~30 experiments a quarter — before holdouts, exclusion groups, and re-runs bring it closer to 20. Your backlog is ten times that. Ideas were never the bottleneck. Traffic is.
1 Kohavi & Thomke, Harvard Business Review, 2017; Thomke, HBR, 2020.
From an idea to a decision, in hours
You connect
Link the analytics and experiment tools you already use. No code changes.
We learn
We learn how your users behave and build behavior models of them.
Describe
Describe the change in plain words, or set a grid: fee ₹19 / ₹29 / ₹39 / ₹49.
We simulate
Variants ranked by predicted lift, with confidence bands and segments.
Decide
A/B test the top 2, drop the idea, or ship once your Trust Score backs it.
Plain language: the single-page variant is predicted to win, driven almost entirely by new users; returning users are near flat.
Every real readout is scored against our locked prediction, so the model gets sharper onyour business with each test.
Connect the tools you already use
We read past behavior, orders, and experiment assignment and results. No code changes on your side.
- Shopify
- Amplitude / Mixpanel, GA4
- Segment / RudderStack
- Your experimentation platform
Our models never read your hypothesis. They can’t be talked into a winner.
Achieved high-fidelity outcome predictions on our data.
We built a proof of concept on a food-delivery simulation: a dataset derived from open-source data, a behavioral model trained on it, counterfactual tests, and release gates around it. It reproduces browsing and persona behavior within simulation noise, and responds to tested price changes at full size. It under-responds to new fee types — we’ve found the root cause, and the fix is in the new build. It even responds to new, never-seen fees.
What you can simulate, and where it fits
Change types
Available now
- Price and promotions
- Fees and membership
- Discounts
Coming next
- Inventory and availability
- Navigation and path changes
- UI changes
- Personalization and recommendations
- Design alternatives before build
Where it helps most
In your workflow
- Experiment design:which variants are worth testing
- Prioritization:your backlog ranked by predicted lift
- Readout:surprising results explained by segment
Built by engineers who’ve lived the queue.
Years running experimentation-heavy product teams at Meta, Uber, Intuit, and PayPal — where the backlog was always ten times the traffic.
We’re early — and looking for partners to build this with.
Simpirical is early. The product is built on synthetic data created from published research — and we’re looking for partners to grow with, teams willing to point it at their own event history and hold us to the answer.
Partners help set the roadmap and get early access as the product hardens, a direct line to the team building it, and pricing locked from day one.
Our design partner deck covers what partners get, how the pilot works, and how your data stays under your control.
View the design partner deck (opens in a new tab)