Agents that close the loop: lessons from 60% auto-resolution
What it took to move an AI support suite from demo to resolving the majority of tickets on its own.
Read articleRetrieval is easy to demo and hard to trust. Here’s how we make enterprise search answers verifiable.
An answer without a source is a rumour. In enterprise search, people need to verify before they act — so every answer we generate links back to the passage it came from.
How you split documents decides what the model can find. We chunk by structure — headings, tables, tickets — not by arbitrary token counts.
Retrieval respects the same access rules as the source systems. If you can’t open the document, the assistant can’t quote it.
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What it took to move an AI support suite from demo to resolving the majority of tickets on its own.
Read articleShipping LLM features without evaluation is shipping blind. How we build eval sets that actually catch regressions.
Read articleCaching, routing and smaller models — practical ways to make AI features affordable at scale.
Read article