How Kore.ai Trains Enterprise AI Agents Across Industries and Languages with Collinear's Simulation Lab
Kore.ai powers AI-driven customer and employee experiences for 150+ Fortune 2000 enterprises globally. At the heart of their platform is XO GPT, a family of custom models fine-tuned for contact center conversational use cases.
As Kore.ai scaled, their agents needed to perform consistently across a growing matrix of industries, use cases, and languages. But they hit a wall: there was no controlled environment where agents could practice against the complexity of real enterprise interactions before going live. Static evals couldn't capture it. Production data was too scarce and too sensitive. They needed a simulation lab.
Watch Thiru Bandam (VP, Tech) and Ravi Rachannavar (Director of AI/ML Products) share how they use Collinear's Simulation Lab.
Kore.ai's agents operate across a broad spectrum of industries, languages, and customer scenarios. A banking customer in Brazil has different expectations than a healthcare provider in Germany. Every deployment is a different environment with its own edge cases.
The problem: there was no scalable way to reproduce these conditions and prepare agents for them. Static benchmarks tested isolated capabilities but missed the multi-turn, multi-lingual failures that surface in production. Manual QA couldn't keep pace with the volume of scenarios. And real production data was too scarce and sensitive to train on directly.
Kore.ai needed a simulation environment where agents could encounter realistic enterprise scenarios across industries and languages, fail safely, and produce the signal needed to improve.
Kore.ai used Collinear's Simulation Lab to build a simulation-driven training pipeline for XO GPT.
Custom performance standards. The team configured the Simulation Lab's verification layer with the accuracy, consistency, and behavioral requirements their enterprise customers demand. These became the scoring criteria agents were measured against in every simulated interaction.
Simulated enterprise conversations at scale. The Simulation Lab generated 10k+ multi-turn, multi-lingual conversations across industries and edge cases that static datasets would never surface. Agents encountered simulated users who interrupted, escalated, switched languages, and changed their requests mid-conversation.
A continuous improvement flywheel. Each round of simulation surfaced harder failure modes and produced higher-signal training data. That data fed reinforcement fine-tuning cycles for XO GPT, which improved agent performance and raised the bar for the next round.
- 91% of AI-generated responses showed measurable improvement in accuracy, consistency, and resolution quality after training on simulation-generated data.
- Multi-lingual agent performance validated across 9 languages, enabling Kore.ai to serve global enterprise customers with consistent quality.
- Faster customer query resolution, translating directly into stronger enterprise adoption and higher customer satisfaction.
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As Kore.ai expands into new industries, languages, and use cases, the Simulation Lab scales with them. New domains feed into the environment, producing fresh training signal that keeps XO GPT improving.
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