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System Improvement

  • Opportunity DiscoveryRank headroom by expected lift
  • Frontier BenchmarksTurn real behavior into a rising bar
  • InterventionsValidate the lift before you ship
  • System KnowledgeCompounding ground truth for the system
Trust CentreDeploy safely on production data

Topics

  • 01
    Systems IntelligenceLifelong learning, multi-agent coordination, agent safety
  • 02
    Code IntelligenceContext retrieval and production-grade code recommendations
  • 03
    Decision Systems & RLCausal reasoning, planning, and bandit optimization
  • 04
    Personalization at ScaleLarge-scale user modeling and recommendation
  • 05
    Marketplace IntelligenceMatching, ranking, and marketplace optimization

Selected papers

  • How Task Structure Limits Multi-Agent SuccessOpenReview 2026
  • Improving FIM Code Completions via Context & Curriculum Based LearningWSDM 2025
  • Ad-load Balancing via Off-policy Learning in a Content MarketplaceWSDM 2024
  • Disentangling Causal Effects from Sets of InterventionsNeurIPS 2022
  • All papersThe full research index

From the blog

  • Compounding systems intelligenceTalk
  • Every PR Gets Its Own WorldSystems
  • Better offline tests lead to more production winsApplied Science
  • System Ground TruthResearch
  • Building an Enterprise Sandbox for AI AgentsSystems
  • Crash-Proof Custom AgentsSystems
  • All blogsNotes from production
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Case studiesPricingCareer
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Case studies

Proof from production

How teams use Pavo to move the metrics that matter in production, at scale.

  1. NotificationsHow category-level personalization lifted notification CTR +43% and doubled second-video startsA 25M-user edutainment appRead the case study→
    +43%
    Notification CTR
    +94%
    Second-video initiation
  2. RecommendationsHow Pavo’s Segment-Adaptive Personalization Lifted Time Spent by 4.5%Feed Personalisation 2Read the case study→
    +4.5%
    Platform-wide time spent
    +9.5%
    Strongest feed-watchtime slice
  3. EvaluationBetter offline tests lead to more production winsA high-traffic consumer platformRead the case study→
    −0.4 → +0.8
    Offline–online rank correlation
    14 → 3
    Candidates requiring live traffic

Deploy a continual improvement loop on your systems.

Pavo

The improvement layer for production systems.

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