hindcast

Use cases & endpoints

Financial data for CrewAI — shared tools across agents

Multi-agent workflows fail in a specific way when data is inconsistent. A research agent pulls a figure from one source, an analysis agent pulls a related figure from another, and the two silently refer to different entities or different vintages. The crew produces a confident synthesis of two incompatible facts, and nothing in the output reveals the mismatch.

A shared data layer removes that class of failure. When every agent in a crew resolves companies through the same identifier graph and asks for values with the same as-of semantics, disagreement between agents becomes a real disagreement rather than an artifact of plumbing.

Hindcast is built to sit in that position: one endpoint, one identity space, citations on every response so a supervising agent can check a claim rather than arbitrating between two unsourced assertions. Access opens to the waitlist first.

hindcast — session

preview

hindcast> two agents query the same entity from different angles

→ tool: entity_resolve { id:"...", type:"ticker" }

one entity node both agents can key their questions to

source: preview — endpoints open to the waitlist first

Preview of the shape of an answer — endpoints open to the waitlist first.

What's covered

Join the waitlist for early access.

Questions

Why does shared identity matter more in multi-agent setups?

Because errors compound instead of surfacing. A single agent's wrong entity match produces a wrong answer someone might notice; a crew's mismatch produces a synthesis that reads as corroboration between two sources that were never talking about the same thing.

Does every agent need its own credentials?

That is a deployment choice. The endpoint is remote and standards-based, so a crew can share one connection or hold separate ones depending on how you want usage attributed.

Join the waitlist. Vote the roadmap. First in, first served.

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