Enterprise data connector and query layer for internal LLMs
A managed integration and query orchestration system that lets mid-market enterprises safely connect proprietary LLMs to siloed data sources (Slack, Notion, databases, file stores) with built-in access control, audit logging, and structured retrieval—removing the need for custom engineering per data source.
The problem
Mid-market enterprises want to deploy internal LLMs over proprietary data (customer records, internal docs, codebase, financials) but lack a secure, auditable, standardized way to connect multiple sources. Today they either build custom integrations (which take considerable time per source), use unsafe direct API access, or skip proprietary data entirely and lose competitive advantage.
Who has it: Mid-market enterprises (500-5000 employees) in regulated industries (financial services, healthcare, manufacturing) deploying internal or on-premise LLMs and needing secure, auditable access to 3+ siloed data sources.
Why now: LLM adoption in enterprises has moved from research to production, but most enterprises have fragmented data stacks (Slack, Notion, Salesforce, custom databases, file stores). Open-source LLMs and on-prem inference (Llama, Mistral) are now viable, forcing enterprises to solve data access without relying on OpenAI. SOC 2 and data residency requirements rule out generic cloud LLM platforms.
Where this came from
2 public sources behind this idea.
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