How Bricklay deployed three AI models simultaneously to analyze 50,000+ contracts averaging 15 to 20 pages each, achieving 95% extraction accuracy across PDFs, scanned images, and handwritten documents, and delivering 18% organic revenue growth by eliminating the billing errors that had been systematically leaving revenue behind on every account.
Organic revenue growth delivered by eliminating manual contract data errors that had caused systematic missed billing opportunities across the account base.
Contracts analyzed across PDFs, scanned images, and handwritten documents, averaging 15 to 20 pages each, with three AI models running simultaneously.
Extraction accuracy achieved across all contract formats, including the most complex mixed-media and handwritten documents that automated tools typically cannot handle.
Revenue leakage from incorrect account setup. Every new account billed correctly from the first invoice, with no data entry errors propagating through the billing cycle.
A confidential organization in the warehousing, storage, and records management sector, operating with a workforce of 2,500 to 5,000 employees and a high-volume contract base. Every customer relationship is governed by a contract averaging 15 to 20 pages, containing the precise billing terms, service levels, and pricing structures that determine what the organization should be invoicing. The gap between what the contract says and what the billing system contains is where revenue disappears.
Manual contract review at scale is not a quality control process. It is a structured source of error. A 2,500 to 5,000 employee organization processing contracts averaging 15 to 20 pages cannot review each document with the consistency required to extract billing terms accurately across all formats, all volumes, and all variations in contract language. Four structural failures were running simultaneously before Bricklay.
"The revenue was never lost. The terms were always in the contract. The gap was between what the contract said and what the billing system contained. Three AI models closed that gap at 50,000-contract scale."
Reviewing a 15 to 20 page contract manually for billing term extraction is a high-effort, error-prone process even for a skilled Subject Matter Expert. At 50,000+ contracts across PDFs, scanned images, and handwritten documents, consistent extraction accuracy is not achievable without automation. Every deviation from the contract terms introduced a billing error that would propagate through every subsequent invoice for that account.
When contract data is entered incorrectly at account setup, the error does not appear once. It appears on every invoice until it is caught and corrected. In a high-volume billing environment, systematic contract data errors mean that a portion of the account base has been incorrectly billed from the moment of onboarding. The revenue left behind on each account is small but multiplied across thousands of accounts it is material.
Manual contract review required Subject Matter Expert involvement for every document processed. In a 2,500 to 5,000 employee organization with a large and growing contract base, SME time is a constrained and expensive resource. Absorbing that capacity in data extraction work means high-cost expertise is not available for contract negotiation, customer escalations, or strategic account management.
Incorrect invoices generate customer complaints. Complaints generate credit requests. Credit requests require investigation, approval, and processing. Each credit represents a revenue reversal and a customer experience failure. In a recurring billing model, a single billing error per account can generate repeated disputes across months or years before the root cause is identified and corrected in the source contract data.
A significant portion of the contract base existed as scanned images and handwritten documents that standard automation tools cannot reliably process. These contracts presented the highest extraction risk under manual review and the highest failure rate under conventional automation, leaving the most complex agreements as persistent sources of billing error and revenue leakage.
Bricklay deployed three AI models simultaneously, Azure OpenAI, Gemini, and an open-source LLM, to analyze 50,000+ contracts averaging 15 to 20 pages each, achieving 95% extraction accuracy across all document formats including handwritten and scanned documents that had previously been outside automated processing reach. No systems were replaced. The AI layer was built on top of existing contract repositories and billing infrastructure.
18% organic revenue growth did not come from acquiring new customers. It came from billing existing customers correctly, for the first time, using terms that had always been in their contracts.
Correcting the billing terms at the source did not require acquiring new customers, expanding services, or negotiating new contracts. It required reading the existing contracts accurately for the first time at scale. Four outcomes measured what that accuracy delivered.
Organic revenue growth of 18% delivered by eliminating the billing errors that had caused systematic missed revenue opportunities. Growth from correct billing, not from new business.
Three AI models running simultaneously achieved 95% extraction accuracy across 50,000+ contracts including PDFs, scanned images, and handwritten documents.
Subject Matter Expert time previously consumed by per-contract manual review fully redirected to contract negotiation, customer escalation handling, and strategic account management.
Accurate contract data produced accurate invoices from account setup onward. The chain of billing errors, customer complaints, and credit requests was interrupted at the source.
This engagement was built for organizations where every customer relationship is governed by a contract and the gap between what the contract says and what the billing system contains represents systematic, unquantified revenue loss.
You maintain a contract base of 10,000 or more agreements governing recurring billing, where contract terms must be accurately reflected in your invoicing system for every account
Contract data entry is performed manually or semi-manually, meaning billing accuracy depends on the consistency of individual SME review rather than automated extraction from the source document
Your billing system generates customer complaints, invoice credits, or dispute investigations at a volume suggesting systematic contract data inaccuracy rather than isolated human error
Subject Matter Experts in your organization spend measurable time on per-contract data extraction and validation, limiting their availability for contract negotiation, strategic accounts, and customer escalation management
A portion of your contract base exists in non-standard formats including scanned images, mixed-media PDFs, or handwritten documents that conventional automation tools cannot reliably process
Let us analyze a sample of your contract base, identify the gap between contract terms and billing system data, and show you what accurate extraction looks like at your volume.
