Converra runs full multi-turn conversations against personas built from your production data. Variants compete head-to-head. Regressions are caught before deployment. Every change ships with proof it works.
You tweak a prompt, review a few test inputs, and push to production. Maybe you run a production A/B test if you have enough traffic. Customers are your test subjects, and regressions show up in support tickets.
Simulation testing flips this. Every change is validated against realistic conversations before it goes live. Your customers only see improvements.
Your real conversations contain patterns: the confused first-timer, the impatient repeat caller, the edge-case power user. Converra extracts these into typed personas that behave like your actual users.
Single-turn evals miss the failures that matter. Converra runs complete conversations: follow-ups, interruptions, topic changes, confusion loops. The same patterns that trip up agents in production.
Every candidate prompt runs the same personas and scenarios as your current production prompt. Same conditions, same personas, same edge cases. The comparison is direct and fair.
A variant that improves one scenario and breaks three others is worse than no change at all. Converra runs regression suites automatically and surfaces tradeoffs before deployment.
Production A/B testing means real customers hit the broken variant. Simulation testing catches regressions before any customer is affected.
A simulation cycle takes minutes. You can test 5 prompt variants in the time it takes to get statistically significant results from one production A/B test.
Production traffic is random. Simulation lets you target specific failure patterns, persona types, and scenarios that matter most to your business.
Converra simulations are multi-turn, persona-driven conversations that follow the patterns in your real data. They behave like your users, including the difficult ones.
Converra generates variants, runs simulations, evaluates results, and checks for regressions in a single automated cycle. When a variant wins, it ships. When the next failure pattern emerges, the cycle starts again from a higher baseline.
Personas are generated from your actual production conversations. They carry the same intents, confusion patterns, and edge cases your real users bring. The conversations are multi-turn and follow natural dialogue flow, including interruptions, topic changes, and follow-up questions.
Yes. Converra generates scenarios from your data automatically, and you can add custom scenarios for specific cases you care about. Both are used in every simulation run.
A typical optimization cycle runs 36+ simulated conversations across multiple personas and scenarios. Validation mode runs more for higher statistical confidence.
No. Simulation testing validates changes before they ship. You still need production monitoring to catch issues from real-world drift, new user patterns, and model updates. Converra handles both: pre-deployment simulation and post-deployment rollback if metrics regress.
Task completion, response quality, user sentiment, safety/policy adherence, and schema compliance. You can configure which metrics matter most for your use case.
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