Move more metrics in a quarterthan most teams do in a year.
Pavo helps your Product, ML and Data Science team run more experiments, find what works faster, and ship wins without growing headcount.Pavo helps your Product, ML and Data Science team run more experiments, find what works faster, and ship wins across Search, Recommendations, Notifications, Pricing, and Ads — without growing headcount.
Built by ML engineers from:
Vintage Moto Jacket
$240
Racerback Tank
$25
Distressed Leather
$210
The Racer (Book)
$15
What's leaking
Results match keywords but miss intent — a plumber search returns articles, not bookable pros.
Unlock
Rerank via hybrid retrieval + contextual embeddings.
Built by ML engineers from:
Snowflake
Databricks
Statsig
BigQuery
Feast
MLflowWhat if you could
try every idea to improve your metrics
Pavo owns the entire discovery loop — hypothesis → data → features → models → offline evals → A/B tests. And runs it again and again, to move the needle.
Most teams try 3 things a quarter. The best ML orgs try 300 — different features, weights, architectures, cohorts. Give Pavo a metric, and it explores the full space, drawing on what's already proven at the world's best companies.
Most teams try 3 things a quarter. The best ML orgs try 300 — different features, weights, architectures, cohorts. Give Pavo a metric, and it explores the full space, drawing on what's already proven at the world's best companies.
Capabilities that compound.
Not point solutions — a system that closes the loop across your entire ML stack.
01 — Outcome Focused
Closes the loop across the whole stack
From revenue metrics down to raw code — Pavo operates across every layer of abstraction. It doesn't just suggest changes. It connects metric movement to decisions, experiments, systems, models, features, data, and code.
02 — ML Org in a Box
A swarm of agents, not a single tool
Pavo replaces the coordination overhead between Data Engineers, Data Scientists, ML Engineers, and MLOps. One system that thinks like your whole ML org — from data pipelines to production deployments.
03 — 100x Explorations
Explore every branch, not just the obvious ones
Your team picks 2–3 bets and hopes for the best. Pavo explores hundreds of branches simultaneously — iterating on candidate generators, adding new signals, testing ranker variants — all before you commit to a single A/B test.
04 — Offline Evals
10–100x more iterations before going live
Traditional teams run f experiments per month. Pavo inserts an offline evaluation layer — counterfactual simulation, holdout backtests, multi-task learning — so you test 10–100f variants before burning live traffic.
Capabilities that compound.
When exploration compounds,
everything moves.
Broader Discovery
+14%
lift from exploring 40+ strategies in a week
Smarter Bets
60%
of weak hypotheses filtered before launch
Faster Learning
3x faster
from hypothesis to statistically significant result
Everything you need to move metrics, automated
Search Ranking Optimization (Learning to Rank)
Get our LTR retraining from quarterly to weekly and automate feature engineering. Our search ranking can't keep up with competitors who iterate faster.
Product / Content Recommendations
Each rec model iteration (CF → two-tower) takes 4–6 months. Help us test more product recommendation architectures faster with rigorous offline eval.
Send-Time Optimization
Move send-time optimization from 5 segments to per-user — 10M+ daily predictions, <100ms latency, updating with each interaction. Productionize it.
Churn Prediction Modeling
Our churn prediction model is a point-in-time snapshot. Rebuild it to understand behavioral trajectories — distinguish slow decliners from sudden drop-offs.
Proactive Retention Interventions
Retention interventions fire on a rule (score > 0.7 → email). Optimize timing, intervention type, and dosage — before we annoy users instead of saving them.
Dynamic Pricing
Replace rules-based pricing (if inventory > X, drop Y%) with ML-driven elasticity at item-segment level. Optimize revenue, margin, and turnover.
SKU-Level Demand Forecasting
Our ML forecasting degrades on long-tail SKUs, new products, and external events. Combine time-series with external signals and handle the tail better.
Incrementality Testing
Geo-experiments for incrementality are slow (6–8 weeks) and we only run 2–3/quarter. Build a faster, cheaper framework to measure every major campaign.
Here's how you start. It takes days, not months.
Deploy on your cloud — or use ours
Run Pavo inside your VPC with read-only access to your warehouse, repos, and experiment platform. Or skip the infra and use Pavo Cloud. Your call.
Onboard onto your systems
Pavo reads your codebase, past experiments, and metric definitions. It builds tribal knowledge — like onboarding a senior hire, but in hours.
Pavo starts working
Within days, Pavo raises its first PR. It proposes experiments, runs offline evals, and flags what to test next. Review it like you'd review a teammate's work.
Got a metric that won't move?
Tell us where you're stuck — we'll show you exactly where you're leaving money on the table, and hand you a 30-day plan to move the needle.