Tamda watches your product, catches the change before you do, finds the segment behind it, and drafts the next experiment as a PR you review like any other. Then it tells you ship, kill, or iterate.
Minutes
From signal to drafted PR
Stats built-in
Guardrails checked automatically
No backlog
Hypotheses generated, not requested
Every outcome
Remembered for the next cycle
Diagnosis
A dashboard stops at −31.8%. Tamda breaks the change down against your real per-user events and names the segment that caused it.
Your dashboard
−31.8%
8.50% → 5.80% · that's all it says
Tamda
Decomposed by customer type · real per-user events
Your checkout didn't break. Your first-purchase flow did. New customers fell 60% while returning held flat. Start with whatever changed for first-time buyers this week, and Tamda will draft the experiment to fix it.
Illustrative example
Computed, not estimated
Each segment’s exact share of the change, from event counts, not a noisy rate.
Won’t invent an explanation
Nothing real to show? Tamda says so, instead of naming a segment it wasn’t shown.
The experiment loop
Tamda runs the full loop and remembers every outcome, so each cycle is smarter than the last.
checkout_completed
3.84%
−2.1% · mobile
activation_rate
41.2%
+0.3% · all
booking_started
18.6%
−0.2% · all
Tamda detected an opportunity
Checkout completion dropped 2.1% on mobile over 7 days. Pattern matches past payment-friction cases. Generating hypothesis…
Connect your product data and Tamda continuously watches for drops, friction, and untapped gains, surfacing them before your team notices.
Context
Your event data says what happened. Your team already knows why, in a Slack thread, a Notion doc, an email that never got logged anywhere. Connect them once, and Tamda searches that real context every time it proposes an experiment or explains a metric, instead of guessing.
Slack
Threads where your team already said why something broke, or what to try next.
Notion
Specs, postmortems, and planning docs, searched, not re-typed into a prompt.
Gmail
Customer feedback and bug reports that never made it into an analytics event.
#growth · 9 days ago
"a few people on the call mentioned the OTP step feels like it stalls on mobile, might be worth looking at"
The drop is concentrated in mobile checkout, and matches a Slack thread from #growth flagging OTP friction on mobile nine days before this signal fired.
Attribution
A visitor clicks your ad, browses anonymously, and buys three days later as a logged-in user. Other platforms count two strangers. Tamda counts one person, and the ad gets its credit.
Anonymous → identified
A persistent device ID tracks users before login. One identify call links it to their account forever.
First-touch attribution
UTM source, medium, and campaign captured at first touch and carried through to conversion.
Cross-session outcomes
Exposed anonymously, converted after login? Tamda connects the dots instead of counting two users.
Why Tamda
Your analytics tool
Mixpanel · Amplitude · PostHog
AI chat + MCP
connected to that same data
Tamda
Connect your metrics, ask why anything moved, and let Tamda design the experiment, open the PR, and hand you a real statistical verdict. No PM ticket required, just a review before it ships.