The Results
FlumePublished Research
Published benchmarks: Spider 2.0, ICLR 2025; BEAVER, arXiv 2024.
Research baseline: Sequeda, Allemang & Jacob, ACM SIGMOD 2024. Flume: internal evaluation, Q1 2026.
Why Flume

More than “talk to your data.”

We started in healthcare, integrating the systems no one else wanted to touch. Then we realized: the same infrastructure that makes integrations trustworthy makes AI trustworthy.

Why Enterprise AI Breaks

Four failure modes no model
can solve alone.

01

Context window overload

Context window overload

Stuff hundreds of tables into a prompt and the model loses what matters.

02

No memory between runs

No memory between runs

Every session starts from zero. The model can’t learn which joins work or what broke last time.

03

Join hallucination

Join hallucination

The model writes SQL that executes, returns plausible numbers, and is completely wrong.

04

Schema ≠ meaning

Schema ≠ meaning

Column names don’t explain business logic. Naming conventions can’t be inferred from metadata.

How It’s Built

Infrastructure that earns trust.

/∞

Subgraph resolution

Resolves only the relevant subgraph before generation.The prompt stays small and focused.
Subgraph resolution
/∞

Persistent graph

Every validated join compounds over time.No cold starts.
/∞

Confidence-scored joins

Trusted join paths with confidence scores.The model reads validated relationships, not guesses.
Confidence-scored joins
/∞

Human artifact ingestion

Docs, tickets, and tribal context encoded alongside schema.Meaning, not just column names.