01
Connect the context.
Connect analytics or your warehouse, add your product goals, and link your GitHub repo when you want code changes.
A shared picture of your product YOUR AI DECISION SCIENTIST.
Build less.
Build what matters.
Tamda investigates your product data, recommends
what to change, and measures what worked.
From a question to a better next move.
INSIDE A TAMDA INVESTIGATIONILLUSTRATIVE EXAMPLE
01 / 04
A small signal. A bigger story.
Checkout conversion is down. Tamda finds the change worth your attention, then starts looking beneath the average.
Your AI decision scientist.
From evidence to action.
From evidence to action.
Your product Connected context
SIGNAL DETECTEDThis week
Checkout conversion
5.8%MONTUEWEDTHUFRISATSUN
Something changed at checkout.
Conversion fell from 8.5% to 5.8% this week. Let’s find out where.
Start with the signal. Follow the evidence.
One continuous loop. A better-informed next move.
THE WHOLE PICTURE CHANGES THE ANSWER.Your data has context.
Your data has context.
Your decisions should, too.
What customers do. How the business is doing. What your code actually makes possible. Bring it into the same conversation.
Product & business data Customer behavior Your codebase
A clearer
next move.
next move.
HOW THE DOTS CONNECT.A scientist’s process.
A scientist’s process.
Your product’s next chapter.
Start with the data you already have. Tamda brings the investigation, the experiment plan, and the follow-through.
02
Ask a better question.
Tamda examines changes in your metrics and compares customer segments to find where to investigate next.
An opportunity backed by evidence03
Make a testable move.
Define a hypothesis, success metric, and guardrails. Review the experiment and any proposed code before it ships.
An experiment your team can review04
Let the outcome teach you.
Compare outcomes, watch for regressions, and keep a record of the result. When evidence is thin, keep learning.
A more informed next decisionGOOD JUDGMENT, BUILT IN.
More signals.
Less noise.
Better next moves.
01 / EVIDENCE
A reason behind the recommendation.
Trace a proposed change back to the signals and context that informed it.
02 / CONTROL
Your product. Your call.
Review proposed changes and define the guardrails before an experiment runs.
03 / MEMORY
Learning that stays with you.
Keep a history of decisions and outcomes to inform what you try next.
What’s your product
trying to tell you?
Get a free teardown A fresh look at your product. A concrete place to start.