Retention and engagement problems, caught early.
Bellwether is an agentic copilot for growth teams. It reads your product analytics, surfaces the insight with the statistics to back it, designs the research, experiments, and solutions, and compounds every result into memory your team keeps.
The gap
The data has the answer. The loop to reach it is broken.
Product and growth teams sit on rich behavioral data in Mixpanel, Amplitude, and Segment. But the path from pattern to validated learning is slow and manual: analysts are backlogged, experiments run under-powered or get read wrong, and what the team learns evaporates when someone moves on.
Most teams run a fraction of the experiments their data and product launches warrant, and act on a fraction of the insight it holds. Bellwether runs that loop continuously and keeps the receipts.
The loop
One continuous loop, run for you.
Scan
A daily sweep of your data surfaces ranked, evidence-linked insights, each with the query that produced it and the statistics behind it.
Interrogate
Ask any insight a question in plain language. It re-runs the query, checks the obvious confounders, and pulls adjacent slices, grounded in your live data, not a guess.
Enrich
Behavioral data shows what users do, not why. Bellwether runs surveys and short interviews to fill in the gap, adding each user's motivations, values, and intent to their profile so the next pass sees the whole person.
Design
It turns the finding into an experiment worth running, and sizes the test before launch, so you never run one too small to tell you anything.
Experiment
Once you approve it, the experiment goes live with a metric to hit and guardrails to watch. Bellwether follows it the whole way and gives you a straight answer when it's done, without dressing up a flat result as a win.
Learn
Every insight, test, and result compounds into a searchable repository: the institutional memory that makes the next cycle smarter and survives the team that built it.
Then the loop consults its own memory and begins again
It acts, not just analyzes
Understand each cohort, then act on it.
Finding the problem is half the job. When Bellwether can't explain why new cohorts drift off, it asks them, and a quick survey could learn their values, intents, and motivations. Then it writes the win-back that matches the answer and waits for your approval before a single user sees it.
Audience: new users, first week
Why did you download the app?
Screened for leading & double-barreled questions
Audience: dormant for 14 days and inspired by an influencer
Voice matched from past campaigns · sends via Customer.io on approval
Proof
What it found on a live deployment.
With enriched cohort data to personalize against, the growth team shipped 12x more copy variants in a quarter, testing angles they never had the data to justify before.
Two cohorts both repeated the key action 3+ times, so the team counted them equally activated. But retention still diverged by the first category they browsed, a cut the data team had never run.
Why it's different
Statistically honest by construction.
Analytics vendors are bolting AI onto dashboards. The risk is a confident-sounding wrong answer. Bellwether is built so it can't make one.
The model narrates. It never does the math.
Every number (effect size, significance, required sample) comes from a deterministic statistics engine, not a language model. The AI writes the sentence around the number; it cannot invent one.
It tells you when the answer is no.
Power analysis runs before any experiment. If a cohort is too small to detect a real effect, Bellwether says so and suggests alternatives instead of greenlighting a test that can't conclude.
Correlation is labeled correlation.
Findings flag what's correlational and what could confound them. One false "winner" destroys credibility with a data team, so the system is built not to make that call for you.
Who it's for
See it on your data.
Consumer and product-led SaaS teams, Series A to C, already instrumented on Mixpanel, Amplitude, or Segment, with more experiments worth running than the team has bandwidth for.