Pavo

Explore more ways to improve your production system.

Pavo develops and evaluates competing changes across prompts, workflows, heuristics, models, training, and optimization, then carries the strongest candidates toward production.

platform.pavoai.com/interventions

Interventions

SystemSupport Resolution AgentOpportunityMissing order-status tool callBenchmarkSupport Resolution v3
7 candidates
Intervention portfolioExploration · Support Resolution Agent
7explored
4building
3refining
1ready
ExploreBuildDeepenProduction
Activity
7 families constructedEvidence attached3 branches stopped3 front-runners refinedExperiment plan ready

The worldview

Every production system has more ways to improve than any team can explore manually. Pavo expands the credible search space while concentrating effort where evidence is strongest.

Production candidate selected
10
Initial directions
Prompts, workflows, heuristics, features, models, fine-tuning, reward models, and policies.
04
Working candidates
Lightweight but credible implementations, compared on one benchmark.
02
Front-runners
Variants, better datasets and training, ablations, and hard-slice fixes.
01
Production candidate
Benchmarked and hardened, with guardrails, experiment design, and rollout plan.
SELECTEDLearned router + validationselected for productionexperiment-ready
LINEAGE

Learned tool router + validation heuristic, the strongest overall case, not merely the top offline score.

INHERITED / OUTCOME

Benchmarked +14%, guardrails cleared, experiment design and staged rollout plan attached.

  1. Explore broadly: one production opportunity fans into credible intervention families across system logic, agent behaviour, predictive models, and learning. Two families are pruned by an added constraint.OPPORTUNITYMissing order-status tool call34% MISS · RESOLUTION RATEMECHANISM: IMPLICIT INTENT≤ 4 WEEKSNO NEW SERVING PATHSystem logic & policyTOOL-VALIDATION HEURISTICELIGIBILITY GUARDFALLBACK ROUTINGAgent behaviour & workflowPROMPT + CONTEXTWORKFLOW PLANNERCONFIDENCE FALLBACKData & predictive modelsLEARNED TOOL ROUTERINTENT CLASSIFIERLearning & optimizationFINE-TUNEREWARD POLICYCONTEXTUAL BANDITFIG · EXPLORE — ONE OPPORTUNITY FANS INTO 7 CREDIBLE FAMILIES (● = TAKEN INTO THE PORTFOLIO)CONSTRAINT ADDED · 2 FAMILIES PRUNED

    Investigate credible ways to improve the system across prompts, agent behaviour, workflows, heuristics, data, features, models, training, and optimization.

  2. Build in parallel: three intervention branches advance from design to runnable candidates on shared datasets, feature pipelines, eval harness, and guardrail suite.DESIGNPROTOTYPERUNNABLEBENCHMARKEDPROMPT + CONTEXTWORKFLOW PLANNERLEARNED ROUTERSHARED WORKTRACE CORPUS · 180K TURNSFEATURE PIPELINE · INTENT + TOOLSEVAL HARNESS · SUPPORT RESOLUTION V3GUARDRAIL SUITE · 5 CHECKSFIG · BUILD — THREE BRANCHES BECOME RUNNABLE AT ONCE ON SHARED INFRASTRUCTURE

    Turn multiple promising directions into lightweight prototypes and working candidates at the same time, reusing data, infrastructure, and learning across branches.

  3. Deepen what works: staged evidence gates prune weak branches and concentrate budget on two front-runners. Stopped branches stay inspectable.MECHFITFEASIBILITYBENCHLIFTSLICEROBUSTGUARDRAILSCOSTBUDGETLEARNED ROUTER+14%VALIDATION HEUR.+9%WORKFLOW PLANNER+11%STOPPEDCONFIDENCE FALLBKLOWSAFETY NETFIG · DEEPEN — EVIDENCE GATES PRUNE WEAK BRANCHES; BUDGET CONCENTRATES ON 2 FRONT-RUNNERSSTOPPED BRANCHES STAY INSPECTABLE

    Evaluate early, stop weak branches, and progressively invest in approaches showing the strongest benchmark performance, mechanism fit, feasibility, and guardrail behaviour.

  4. Prepare for production: the selected candidate becomes a controlled online experiment with randomization, guardrails, ramp, ownership, monitoring, and rollback conditions.Harden & ablateExperiment designRollout rampMonitoringRollback readyPRODUCTION CANDIDATE · EXPERIMENT PLANLEARNED ROUTER + VALIDATION HEURISTICEXPERIMENT UNITconversation sessionRANDOMIZATION50 / 50 · hashed userEXPOSURE LOGGINGtool-decision eventsPRIMARY METRICresolution w/o repeat contactGUARDRAILSpolicy · leakage · latencyRAMP SCHEDULE1% → 5% → 25% → 50%APPROVAL OWNERSupport Platform leadROLLBACK IFleakage +0.5% or latency +40msFIG · PRODUCTIONIZE — ONE CANDIDATE BECOMES A CONTROLLED, REVERSIBLE ONLINE EXPERIMENT

    Refine the leading candidates, harden implementation, resolve difficult cases, validate second-order effects, and create the production experiment and rollout plan.

The tournament, in full

Candidates scored against the benchmark, learnings carried across dead ends, and the result written back.

04 · WHAT SHOULD CHANGE?InterventionsBRANCH · COMPARE · PROVE →WRITE THE RESULT BACKOFFLINE · THE TOURNAMENTONLINE · THE PROOFGEN 1 · EXPLOREBENCHMARK ΔOUTCOMEOPPORTUNITYOPP-142tool-call gap onhigh-value refundsPROMPT · clarify step+0.02NO OFFLINE LIFTRETRIEVAL · chunk rerank+0.04EFFORT > VALUERETRIEVAL · hard negatives+0.06LATENCY +180MSREUSED · HARD NEGATIVESRANKER · fine-tuned+0.09ADVANCEDRANKER · pairwise scorer+0.05ADVANCEDPROMPT · policy recap+0.03ADVANCEDGEN 2 · DOUBLE DOWN ON RANKER3 VARIANTSPROMPT · policy recapPLATEAUEDV1 · +0.10V2 · +0.12V3 · +0.09CANDIDATE · RERANKER V2OFFLINE +0.12PRODUCTION EXPERIMENTLIVEGUARDED ROLLOUTA/B split · kill switch armedCONTROL90%TREATMENT10%EXPOSURE48,200 sessions · 14 daysPRODUCTION RESULTp < 0.01Resolution71% → 76%Repeat contact18% → 14%CSAT4.1 → 4.3Guardrails2 OF 2 HELDEXPERIMENT LOG ATTACHED PER ROW · SELECT TO INSPECTWRITE-BACK · SYSTEM BOOK · BENCHMARK · PRIORS

Intervention examples

  • 01

    Recommendation

    Improve content allocation through exploration heuristics, new features, objective changes, contextual bandits, or constrained reinforcement learning.

  • 02

    Search

    Repair tail-query performance through candidate-generation changes, hybrid retrieval, reranking, fine-tuning, or optimization of the full search policy.

  • 03

    Pricing

    Increase conversion without leaking margin through eligibility rules, uplift models, constrained optimization, or journey-aware sequential policies.

The result is not another generated suggestion. It is an intervention that has earned the right to be tested in production.