Hey Product Hunt! We're the team behind Kaman, and we'd love to tell you what drove us to build this. Every AI agent demo looks great — until you ask it about your actual data. That's when the cracks show: stale context, no memory between sessions, siloed databases that agents can't reach, and ballooning LLM costs from routing everything to GPT-4. We spent months running agents in production and hitting every one of those walls. So we built the infrastructure layer we wished existed — starting with KDL, a version-native data lake designed specifically for AI workloads. Sub-second queries, automatic versioning, time travel, and OpenLineage-compatible lineage tracking — without the Spark/JVM overhead of Delta Lake or Iceberg. The rest of the platform grew from there: hierarchical agent memory (KMMS), 16-channel deployment, 39+ MCP connectors, and intelligent multi-provider LLM routing that's cut inference costs by 35–55% in production. We're live and would love your feedback — especially from anyone who's wrestled with data infrastructure for AI agents. Ask us anything! 🚀

Kaman.ai

Your data, unified. Your agents, deployed everywhere.

Your data, unified. Your agents, deployed everywhere.