Ship on evidence, not vibes.
Change a prompt, swap a model, modify a tool call — then see what actually happens in production. three.dev runs controlled experiments on live traffic so you can compare quality, cost, latency, and business impact before you roll anything out broadly.
You changed the system. Did it actually get better?
For most teams, the honest answer is still: we don't know.
Shipping on vibes
You tweak a prompt, swap a model, push to prod — and hope. If something breaks, you find out from user complaints, not data.
Evals don’t close the loop
Offline benchmarks catch obvious regressions. They can’t tell you whether a change actually moved a business metric with real users on real conversations.
Every change is a tradeoff
The question isn’t only ‘did quality improve?’ It’s whether the extra quality is worth the cost and latency, or whether a faster, cheaper option is the better choice for you.
How it works
Define variants
Define the AI configurations you want to compare — model, prompt, retrieval, routing, inference, or anything that affects output.
Route traffic
Route a slice of real user traffic through the experiment. three.dev keeps each conversation on one configuration, tracks the result from start to finish, and can send less traffic to weaker options as it learns.
Ship the winner
Traffic shifts toward the best performer as data accumulates. When it’s time to roll out, the decision is data-driven.
Built for how AI actually works. Not how you wish it did.
Test on real users, measure real outcomes
Run A/B/n experiments on production traffic. Measure what matters — conversion, task success, resolution rate — not just eval scores.
Bad variants get starved automatically
Our experimentation framework continuously rebalances traffic toward higher-performing variants, helping improve user outcomes while still exploring alternatives.
Run any change as an experiment
Model swaps, prompt engineering, inference, tool settings, or full pipeline changes. If it affects behavior, you can compare it.
Keep each conversation on one configuration
Users get a consistent experience from first message to last, and you get cleaner attribution than request-by-request sampling.
The best config today won’t be the best config tomorrow
New models ship weekly. Costs change. Your traffic shifts. Run experiments to find the optimal configuration for each use case — and re-test when the landscape moves.
Your next prompt change deserves better than “LGTM, ship it.”
Run it on live traffic first. See the impact on outcomes, latency, and cost before you roll it out.
Request early access →