AI Finance Analyst

6 Interactive Sessions

Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.

1. 1

   Data Lake — five sources that disagree by design

   Normalise filings, transcripts, market data, internal reports and analyst notes into one Document shape — without throwing away the financial metadata that makes each one useful.

2. 2

   Encode — the chunker that quietly breaks your numbers

   Chunk financial documents so that a table survives as one piece, and attach the metadata that makes precise retrieval possible.

3. 3

   Retrieve — three searches, fused

   Combine semantic, keyword and structured search so an exact financial question gets an exact set of chunks — then rerank for what finance actually values.

4. 4

   Gateway — check every number before an analyst sees it

   Put a graph of explicit safety nodes between the model and the analyst, and make number validation the one that matters most.

5. 5

   App — the model writes the prose, the code does the maths

   Turn the pipeline into something an analyst opens every morning — dense with numbers, every claim traceable to its filing.

6. 6

   Deploy — reach the analyst, and prove it still works

   Put the system where analysts already are, and instrument the one metric that tells you whether it's still trustworthy.

What you walk away with