Intelligent Document Processing with Databricks Agent Bricks

1. The Hidden Cost of Unreadable Contracts

Every large enterprise sits on a mountain of contracts - Master Service Agreements, amendments, purchase orders, statements of work. Many exist only as scanned images: photocopied, faxed, or digitized from paper archives. The challenge is deceptively simple to state and enormously expensive to solve: how do we know what's inside these documents? How do we not only understand what's inside these documents, but use that understanding to organize information and drive smarter business decisions?

A skilled analyst reading a single 41-page Master Services Agreement, extracting parties, dates, SLA targets, pricing, termination clauses, and signatories, takes 2 to 4 hours. For an organization managing 10,000 such contracts, that is 20,000–40,000 person-hours of manual work. At fully loaded labor costs, that translates to $1.5M–$3M, and the output is only as reliable as the human reading the document on a given day. Worse, because these are scanned images, analysts cannot use Ctrl+F. Every page must be read visually every time a question arises.

What organizations really need

• Structured field extraction - parties, dates, financial terms, SLA metrics, payment terms, signatories
• Verbatim clause text - the exact legal language for termination, liability caps, force majeure, dispute resolution
• Decision intelligence - a queryable repository that powers analytics, risk assessment, and negotiation strategy

What about e-signatures?

A common objection: "We have moved to e-signatures. Our new contracts are born-digital." This does not close the problem for two reasons. First, every contract signed before the e-signature transition still exists as a scanned image - IDP is the only scalable path to digitize and structure that historical archive. Second, even a born-digital PDF is not a database. A 41-page agreement with selectable text still requires intelligent extraction to become queryable. IDP solves the problem for the past, the present, and the future.

2. From Scanned Pages to Structured Data

To demonstrate IDP in practice, we use a synthetic but realistic B2B telecom contract: a 41-page Master Services Agreement between Zenith Radio AB (provider) and Value Store AB (customer), covering fiber internet, managed WiFi, mobile connections, and SIP trunks across ~30 locations in Sweden. Contract reference ZR-VS-2025-0847, effective 1 March 2025, estimated annual value ~SEK 12,000,000.

The document is rendered as scanned images, rasterized at 300 DPI with realistic scan artifacts: warm paper tint, Gaussian noise, slight rotation, edge shadows, and ink specks. No selectable text layer. This is exactly what an enterprise encounters when digitizing a physical contract archive.

Stage 1 - Document Parsing with Visual Grounding

Agent Bricks provides the ai_parse_document function, which converts the scanned PDF into machine-readable, structured text. What separates this from basic OCR is visual grounding. The model understands document layout and semantic context, distinguishing body text from table cells, headers from footers, and signature blocks from running prose.

Visual grounding lets you verify extraction quality before proceeding. For a scanned document with artifacts, this is essential: you can confirm the model read "99.95%" and not "99.45%", or that "SEK 12,500" was not misread as "SEK 12,800".

 ai_parse_document output with visual grounding overlay on Page 11, Section 6 (Charges, Invoicing and Payment).

Stage 2 - Information Extraction Agent

Parsing gives you raw text. The Information Extraction Agent turns that text into structured, queryable records. We define the fields we need along with plain-language descriptions of what each represents, and the agent maps extracted content against those definitions, returning a structured JSON output ready for a Delta table.

ai_extract output with visual grounding: field-level citations and confidence scores. New in v2.1, released 05/05/2026.

Example field definitions for the telecom MSA:

The output is a structured record: 

What This Enables

With extracted data in a Delta table, the organization can immediately:

• Query at scale - search and filter across thousands of contracts instantly
• Build dashboards - aggregate financial exposure, track renewal dates, monitor SLA commitments across the portfolio
• Compare clause language - surface differences in liability caps or force majeure provisions across vendors
• Feed downstream systems - populate CLM platforms, ERP systems, and risk models directly from source documents
• Build a knowledge graph - link contracts to amendments and side letters, with full provenance and traversal

3. Economics and the Path to Production

Cost Per Contract: $0.53 vs. $150–$300

The ROI case is straightforward. Here is the direct comparison for a 41-page MSA:

* ~6 DBUs for ai_parse_document + ~1.5 DBUs for ai_extract @ $0.070/DBU. Pricing ref: databricks.com/product/pricing/ai-functions

The per-page cost also scales favorably with document length. A 41-page document costs ~$0.011/page vs. ~$0.020/page for a 9-page document, as the fixed overhead per document is amortized across more pages. For an enterprise processing 10,000 contracts: manual cost is ~$1.5M–$3M; where cost with Agent Bricks would be ~$300K (Development + Agent Bricks cost).

From Pilot to Production: The ATOM.AI Accelerator

Moving from a proof-of-concept notebook to a production-grade pipeline requires significantly more than calling an AI function. It requires pipeline orchestration, quality assurance, schema management, guardrails, downstream integration, and monitoring. Tredence's ATOM.AI accelerator provides a pre-built framework for all the above:

Handling Revisions and Amendments with Knowledge Graph

In practice, a Master Services Agreement is rarely standalone. Amendments, addendums, side letters, and change orders modify or supersede specific provisions over the contract's life. Tredence helps enterprises build a knowledge graph that tags amendment documents to their parent agreement, tracks which clauses have been modified and when, maintains the current effective version of each clause, and enables full traversal with provenance back to source documents.

Who Benefits Most

IDP delivers the highest impact for organizations that manage thousands to millions of contracts, have significant historical archives of scanned documents, operate in regulated industries where auditability and clause retrieval are critical, or are undergoing digital transformation but still need to deal with the legacy backlog. The telecom B2B scenario demonstrated here applies equally to insurance policy documents, loan agreements, procurement contracts, lease agreements, and regulatory filings.

One recent example comes from a large TMT enterprise managing more than 1 million documents across 20+ years of contract history. Much of that estate existed only as scanned or low-searchability files, making retrieval, review, and downstream analysis slow and expensive. In six months, we implemented an IDP-led solution that transformed this archive into structured, queryable intelligence. The result was a faster path from legacy documents to business value, with annual savings of $2M-$3M+ through reduced manual effort, improved accessibility, and better decision support.

Your contracts have stories to tell. It is time to listen at scale.

The information locked inside enterprise documents will not unlock itself with time. E-signatures address the future, but the past remains. Databricks Agent Bricks with ai_parse_document, ai_extract, and visual grounding provides a scalable, cost-effective path from scanned images to structured intelligence. The question is not whether to adopt IDP, but how quickly to move from pilot to production. That is exactly where Tredence and the ATOM.AI accelerator come in.

To learn more about how Tredence can help your organization unlock value from unstructured documents using Databricks Agent Bricks and the ATOM.AI Brickwork accelerator, contact us.