Every industry has the same problem: critical data trapped in PDFs, faxes, emails, and handwritten forms. Your team extracts it by hand. We build AI that does it in seconds.
200+ implementations across our team's 20-year track record
It does not matter whether you run a lab, a law firm, a bank, or a dealership. The workflow is identical: a document arrives, someone opens it, reads every field, and re-types the data into your system. Then someone else checks their work. This is the most expensive copy-paste operation in your business.
❌ Staff spends 4-8 minutes per document re-keying data that already exists in a structured form somewhere
❌ Error rates of 3-8% on manual entry create downstream rework in billing, compliance, and operations
❌ Scaling document volume means hiring more data entry staff, not building better systems
❌ Documents sit in queues for hours or days because your team can only type so fast
How This Looks In Your Industry
Same capability. Custom-built for your business.
Lab staff spend 6+ minutes per requisition manually keying patient demographics, test codes, and insurance data from faxed and scanned order forms. Handwritten fields and checkbox forms compound the error rate.
We build an AI pipeline that reads incoming order forms from any channel — fax server, email, portal — extracts every field, validates against your test catalog and payer rules, and populates your LIMS automatically. High-confidence orders flow straight through. Low-confidence items route to a review queue with the original document side-by-side.
43% faster order entry · 76% fewer data entry errors
Attorneys and paralegals spend hours reviewing contracts, leases, and agreements to extract key terms, deadlines, and obligations. Every missed clause is a liability. Every late deadline is malpractice exposure.
We build AI that reads contracts and legal documents, extracts key clauses, identifies non-standard terms, flags risk provisions, and populates your matter management system. The system learns your firm's specific clause library and risk thresholds over time.
Loan officers receive application packages with 30-50 pages of supporting documents — tax returns, pay stubs, bank statements, property appraisals. Staff manually extracts financial data and enters it into the loan origination system field by field.
We build AI that classifies each document in a loan package, extracts relevant financial data, cross-references figures across documents for consistency, and populates your LOS. Missing documents are flagged immediately instead of discovered days later.
Deal jackets contain 15-25 documents per vehicle transaction — titles, loan applications, trade-in appraisals, warranty contracts, DMV forms. F&I managers spend 45 minutes per deal assembling and verifying paperwork.
We build AI that reads every document in a deal jacket, extracts VIN, buyer data, financing terms, and trade-in details, validates completeness against your checklist, and populates your DMS. Missing documents trigger alerts before the customer leaves.
Mortgage processors handle 80-page loan files with tax returns, appraisals, title reports, and employment verifications. Each document needs data extracted and cross-referenced. A single missing page delays closing by days.
We build AI that ingests entire loan files, classifies each page, extracts financial and property data, checks for completeness against closing requirements, and populates your LOS. Discrepancies between documents are flagged automatically.
65% faster file processing · 80% fewer stacking order errors
Estimators receive bid packages with hundreds of pages of specifications, drawings, and addenda. Extracting quantities, materials, and scope requirements is manual, slow, and error-prone. A missed addendum can cost six figures.
We build AI that reads bid documents, extracts scope items, quantities, and specification requirements, and generates a structured comparison matrix across multiple bids. Addenda are tracked and cross-referenced automatically against the base documents.
Account managers receive renewal documents, policy declarations, and endorsements from multiple carriers. Comparing coverage terms, exclusions, and pricing across 4-6 carriers means reading hundreds of pages per client review.
We build AI that reads policy documents from any carrier format, extracts coverage terms, limits, deductibles, and exclusions, and generates side-by-side comparisons. Gaps in coverage are flagged automatically before the client meeting.
80% faster policy comparison · 100% coverage gap detection
AP teams process invoices from 20-40 vendors per location — food suppliers, equipment vendors, service providers. Every invoice has a different format. Staff manually enters line items, matches to POs, and codes to GL accounts.
We build AI that reads vendor invoices in any format, extracts line items and totals, matches to purchase orders, flags discrepancies, and posts to your AP system. Three-way matching (PO, receipt, invoice) happens automatically.
Order management teams receive purchase orders via email, fax, EDI, and phone. Each customer uses a different PO format. Staff manually enters line items, SKUs, quantities, and shipping details into the ERP — one order at a time.
We build AI that reads purchase orders in any format, maps customer part numbers to your SKUs, extracts quantities and shipping instructions, validates against inventory, and creates sales orders in your ERP. Mismatches and backorder situations are flagged before confirmation.
90% faster order entry · 95% fewer SKU mapping errors
We diagnose your top 3 automation opportunities on a 30-minute call. You tell us where your team spends time. We tell you where AI can help. Free, no strings. Free, 30 min call.
2
AI Roadmap
We go deep into your workflows, interview your team, analyze your data systems, and deliver a custom report with ROI projections. This is the document your CFO needs to approve the build. $7,500 flat. If we can't identify at least $100K in annual savings, the roadmap is free.
3
Build
We build your custom AI extraction pipeline in 2-week sprints with demos at every checkpoint. We train on your actual documents. $25K–$75K.
4
Run
We monitor, maintain, and optimize. New document formats are added without rebuilds. Your workflow gets better over time. $2K–$5K/mo, 30-day cancel.
Every implementation is custom-built for your business. The examples above are based on real client engagements. Your workflow will be designed around your specific document types, systems, and data requirements.
The system handles PDFs, scanned documents, faxes, email attachments, photographs of paper forms, and handwritten notes. If your team can read it, the AI can read it. We train the system on your specific document layouts during implementation, so it learns your forms, not generic templates.
Across our implementations, AI extraction achieves 99.2% field-level accuracy. Manual data entry typically runs 96-97% due to fatigue and transcription errors. The AI also includes confidence scoring — low-confidence extractions route to human review rather than flowing through with silent errors. You get better accuracy and you know exactly where the uncertainty is.
No. We build a processing layer that sits between your incoming documents and your existing system of record. The AI extracts data and populates your current system via API, database connection, or file transfer. Your team keeps using the same tools they use today. We add intelligence to the front end, not a new back end.
Most document processing implementations go live within 60 days. The first two weeks are assessment and document analysis. Weeks 3-6 are build and training. Weeks 7-8 are parallel testing with your team. Your involvement is heaviest in week one — showing us your current flow, providing sample documents, and giving us system access.
Unknown formats route to your review queue with the AI's best extraction attempt pre-filled. Your team validates and corrects, and those corrections feed back into the model. Over time, the system learns new formats and variations without requiring a rebuild. Most clients see new format adoption within 2-3 review cycles.