From Documents to Decisions: Leveraging Databricks and AI to Extract Data Accurately

From Documents to Decisions: Leveraging Databricks and AI to Extract Data Accurately

Every organization has a pile of documents that someone reads by hand: applications, notices, contracts, claims, invoices, referrals. Each one has to be identified, read, and turned into the handful of facts the business actually runs on. AI can do much of that work now. The hard parts are everything around the model: getting documents in reliably, keeping the results governed, knowing when the AI got it wrong, and getting the answers to the people who need them.

In this webinar, Synaptiq runs a complete document intake pipeline on Databricks in front of you. We drop a batch of PDFs into Unity Catalog and watch them flow through: parsed, classified by document type, and turned into structured fields, all in SQL with Databricks’ built-in AI Functions, running in a Lakeflow pipeline. The results land in governed Delta tables and on an AI/BI dashboard as they arrive. Then we do what most demos skip: we measure how accurate the extraction actually is, document type by document type and field by field, so you know where the AI can be trusted and where a person still needs to check.

We’ll cover:

• The why: where manual document intake costs organizations time,money and errors, and why “the model reads it” is only a small part of the solution

• The approach: how we decide which documents to automate first, which fields matter, and what “good enough” means before anything is built

• The live demo: documents into a Unity Catalog Volume, parsing withai_parse_document, classification and extraction with AI Functions,a Lakeflow pipeline that runs as new files arrive, lineage from every field back to its source page, and a dashboard on top

• Trust and governance: measuring extraction accuracy against a labeled sample, routing low-confidence results to human review, and keeping every document, field and decision under one set of permissions

• The lessons: the document types that break naive extraction, hyper-field accuracy matters more than one overall score, and what it takes to go from a demo to a pipeline people rely on

The demo uses only public datasets, and the same pipeline can be pointed at an organization’s own governed data.