How do you build tools for problems people don't know they have yet? That's what was the biggest challenge for me as an AI Infra startup founder.
I thought the hardest part would be technical like distributed systems, latency optimization, GPU juggling. But the real challenge?
Building infrastructure before people realize they need it.
You're solving tomorrow's problems while users are still fighting today's. We were debugging memory bloat and token tracing in multi-agent workflows long before most teams hit those issues.
At the time, it felt like over-engineering, until those same teams returned, overwhelmed by cost, latency, and chaotic agent behavior.
AI infra is tough because you're balancing advanced tech like real-time context and agent coordination with essential basics like fallbacks, evals, and metrics. And when things break, the infra gets blamed, even if the problem isn't there!
We understood there are fundamental issues with the current landscape. Retrieval is incomplete with poor recall, and semantic search isn't solving the problem. Chunk-based strategies only add overhead without providing meaningful outcomes because chunks are independent, losing crucial context.
Innovation in retrieval means solving for unnecessary and ineffective integrations teams bolt on after getting subpar results from vector databases.
That's why we built Agent Analytics; to give teams visibility into what their LLM agents are doing, and why. From trace-level introspection to real-time goal alignment, we help you monitor, debug, and improve autonomous systems.