Pavo Platform
The platform forcontinual improvement.
The applied science factory to continually improveproduction systems and agents.
Purpose-built for moving metrics: find opportunities, build benchmarks, and ship interventions.
demo.pavoai.com
The Vision
The future belongs to production systems that learn from experience, improve continuously, and remain steerable.
The platform
Purpose-built for engineers and leaders owning metric outcomes. Pavo sets a new standard for finding opportunities and shipping changes.
Build deep system understanding
Pavo self-onboards across your code, data, and experiments, then distils a benchmarked model of how your production system actually works.
Run parallel explorations
Designed to run multiple approaches, reducing median time to validated improvement from months to days. From finding gaps, to pushing PRs.
Move more metrics per quarter
Pavo runs opinionated projects end to end and ships the ones that hold up offline, so more validated improvements land every quarter.
Knowledge Systems
Pavo self-onboards where production knowledge lives, distils a benchmarked understanding of the product, and compounds it with every finding.
Learn moreapp.pavoai.com/knowledge
Source knowledge4,869 docs
Ware
house846
house846
Code
files2,840
files2,840
Pull
requests413
requests413
Saved
queries392
queries392
Dash
boards118
boards118
Experi
ments74
ments74
Docu
ments186
ments186
Derived knowledge54,696 docs
Facts54,380from every base
Data tasks192from saved queries
Key tables86from query traffic
Playbooks38from investigations
Synthesized knowledge1,264 docs
Entities1,264Canonical listenerStore decision+1,262 more
Applied science factory
From a plan to a swarm of agents across gap discovery, offline evaluation, feature development and A/B tests, with checkpointed autonomy.
Learn moreStart with a metric goal. Pavo spins up an applied science program, grounded in your system and split into modules.
Frames the problem, formulates hypotheses. Drawn from your code, data, and past experiments, for your sign-off.
Explores approaches in parallel. Long-running agents build features and candidate models per hypothesis.
Picks the winner offline, ships a PR. Pavo executes the work and raises the pull request for you to approve.
Offline eval suite
Build an offline eval suite that takes every parallel approach and predicts which one moves the metric most — across your user cohorts, before a single user is touched.
Learn morePredictingRevenue per sessionreplaying production…
Cohort
Predicted winner
Segment-adaptive
+7.2%
predicted revenue per session
CandidateNDCG@10Predicted rev / sessionPredicted
Production ranker0.412BASELINE
Semantic retrieval0.438−0.4%
Multi-task ranker0.467+4.8%
Segment-adaptivePredicted winner0.481+7.2%
Offline prediction ↔ online outcome · ρ 0.8148,200 sessions · illustrative
Case studies
All case studies
How Seekho replaced broadcast blasts with category-level personalization
Pavo mapped the notification system, fixed the signal driving it, and ran a phased program from four interest groups to sixteen category segments.
Read the story+43%
Notification click-through rate
+94%
Second-video initiation
- Dashverse
How Dashverse lifted Shortfree Autoplay activation with an offline-eval loop
Pavo built an offline-eval engine to screen ~50 candidate policies without live traffic, then shipped the one variant that moved activation in production.
Read the story+3.1pp
Autoplay activation, live in production
~50
Policies screened offline before one reached users
Work with us
Bring continual improvement into production.
Pavo Enterprise
The loop, embedded.Run with you by our applied scientists.
The library
Notes from production.
Research, teardowns, and field notes on making production systems measurably better.
Talk
Compounding systems intelligenceWhy +1% on a production system is worth millions, and what it takes for an agent to own the improvement loop.Watch the talk
Compounding systems intelligenceWhy +1% on a production system is worth millions, and what it takes for an agent to own the improvement loop.Watch the talkResearch
System Ground Truth: What Agents Must Know About Production SystemsAgents can be right about every file and still be wrong about the system. We define the ground truth production agents need.Read more
System Ground Truth: What Agents Must Know About Production SystemsAgents can be right about every file and still be wrong about the system. We define the ground truth production agents need.Read moreSystems
Building an Enterprise Sandbox for AI AgentsWe tried to buy the sandbox. We ended up building one. This is the story of why, what we built, and what broke.Read more
Building an Enterprise Sandbox for AI AgentsWe tried to buy the sandbox. We ended up building one. This is the story of why, what we built, and what broke.Read moreSystems
Crash-Proof Custom AgentsShipping long-running agents with retries, stops, and durable state.Read more
Crash-Proof Custom AgentsShipping long-running agents with retries, stops, and durable state.Read more