Case Study: Slite achieves 12x higher experimentation velocity with Langfuse

Slite boosts AI experiment velocity 12x with Langfuse

Slite, a company that builds an AI-powered knowledge base, faced the challenge of evaluating and debugging its non-deterministic AI agents. Without proper observability into tool usage and reasoning, the team was debugging blindly. Furthermore, data residency concerns for customer information initially limited their ability to trace all production traffic.

By implementing the self-hosted Langfuse observability platform, Slite gained deep tracing and evaluation capabilities. They replaced a manual, CEO-run spreadsheet with a managed Langfuse dataset and an automated evaluation pipeline using LLM-as-a-judge. This solution increased their experimentation velocity by 12x, allowing them to confidently test new ideas weekly instead of quarterly.

Slite