Connect your agent stack
Connect code, prompts, tools, traces, user feedback, outcome data, evals, and experiment history.
Agent improvement
The applied science factory for improving AI agents
Pavo learns how your agent behaves across traces, tools, code, user feedback, and outcomes. It builds the benchmark, finds failures, and compares interventions before a controlled rollout.
Current agent stack
prompts · tools · routing
Production traces
steps · tool calls · errors
Task outcomes
success · abandonment · feedback
Candidate interventions
prompt · tool · retrieval · model
Eval suite
built from your own traces
Staged rollout
a slice of real tasks
Task outcomes improve
Increase experimentation velocity
Explore prompts, tools, retrieval, routing, and models in parallel. Move stronger candidates to controlled tests faster.
Increase release confidence
Evaluate candidates on production-derived cases and hard cohorts. Advance only after task and regression checks.
Force multiplier for your team
Automate trace analysis, eval construction, and experiment preparation.
How it works
From a production gap to a controlled test. Connect your stack, verify what Pavo understands about your system, then start giving it projects.
Build system understanding
Pavo reconstructs how your production system works. Your team verifies the system book.
Run applied science projects with Pavo
Give Pavo a task outcome and guardrails. It finds gaps, builds evals, compares interventions, and opens PRs.
Capabilities
Applied science capabilities for the whole team.To improve task outcomes across production journeys while controlling reliability and cost.
Applied science judgement
Picking the right problem, the right approach, the right lever
- Finds where the agent is failing - which task type, which step, which user intent
- Knows whether to fix the prompt, the tools, the retrieval, the routing, or the model
- Grounds it in your own past traces and fixes, not just what's in the repo
Scientific rigour
Explores widely, and ships only what's proven
- Tries prompts, tool definitions, routing and fine-tunes at once instead of one change at a time
- You see which candidates win on an eval set built from your own traces, before it reaches users
- Checks hard cases and regressions, not just the headline score - no fix that breaks what worked
Knowledge compounds
Every iteration makes the next one cheaper
- The work itself produces new knowledge - how users actually phrase things, which failures cluster, which fixes don't generalise
- Written back to your system book, reviewed by your team, reused next time - it stays with you
- Your team owns the learnings, so v2 to v3 to v4 gets faster, not just further
Use-cases
Our eval suite misses the failures users keep finding in production
Pavo groups traces by task, intent, tool path, and outcome, separates recurring failures from workflow noise, then turns reviewed hard cases into versioned regression tests.
Failure coverageCluster sizeReviewer agreementRegression recall
Our agent reports success even when the requested action never happened
Pavo grades the final state against the requested outcome, then uses traces to inspect tool calls, state changes, and partial completion across the full multi-turn trajectory.
Task successOutcome mismatchTool errorsPartial completion
A prompt or model fix lifts one task type and breaks another
Pavo compares prompt, tool, retrieval, routing, and model candidates on one held-out benchmark, then checks regressions, latency, cost, and guardrails before a controlled rollout.
Task successRegression rateLatencyCost per task
Security and trust
The security posture your compliance team wants
ISO 27001
Certified
SOC 2 Type II
Compliant
Encryption
Encrypted in transit and at rest, with managed production keys.
Access control
Least-privilege access, enforced with MFA and reviewed quarterly.
Data control
Tenant-segmented, with retention and customer-controlled deletion.
Monitoring and response
Continuously monitored, centrally logged, and ready to respond.
Tested and patched
Independently pen-tested, scanned, and kept current against threats.
Resilient by design
Multi-AZ with backups and a tested continuity and recovery plan.
Full reports, policies, and the complete control list are available on request through the Trust Center.
Build an AI agent that improves, with every production run.
Start with one agent journey and one task outcome. Pavo helps your team find the failure, build the evaluation, compare competing fixes, and ship the first team-approved experiment.