AI Integration · Document Intelligence
Read every contract, invoice, and PDF — at production scale.
Extract, classify, and route the documents your team currently re-reads by hand. Contract review, invoice parsing, KB extraction, due-diligence packs — integrated into the systems that already own those documents, with audit trails your legal and finance teams will accept.
The documents your team re-reads every quarter
Every funded business has at least one document choke-point. Procurement reads vendor contracts before renewal. Finance keys invoice line-items into the ERP. Legal redlines NDAs that are 90% identical to last week’s. Sales engineers cut RFP responses out of a five-year archive of similar responses. Operations files compliance evidence by hand.
This is the work that AI does best — well-defined extraction tasks with measurable accuracy and a clear human fallback. It’s also the work that goes wrong loudly when AI is deployed naively, because the consequences of a wrong number on an invoice or a missed clause in a contract are real.
What we ship
Contract review and clause extraction
Vendor agreements, NDAs, MSAs, employment contracts, customer paper. The system extracts the clauses you care about — termination, liability cap, payment terms, IP assignment, auto-renew, governing law — and flags deviations from your playbook. Your legal team gets a redline-ready summary instead of a 40-page PDF.
Invoice and PO parsing
Header fields, line items, tax breakdowns, GL coding suggestions. Posted into NetSuite, QuickBooks, Xero, Sage, SAP, or your custom ERP. Exceptions route to a human; clean invoices flow through with an audit trail.
Knowledge extraction from your archive
RFP responses, past proposals, technical specs, internal wikis. We index them and put a retrieval layer in front so your team stops rewriting the same answer for the fourth time this quarter. Every retrieved passage carries its source — no orphan content.
Compliance and due-diligence packs
SOC 2 evidence, ISO documentation, vendor security questionnaires, data-room files for a financing round. The system pulls answers from your existing policy library, drafts the response, and flags anything that needs a human eye.
20 – 40%
Typical reduction in document-handling time across contract review, invoice processing, and RFP work in our recent deployments. Accuracy is held at or above the human baseline before anything ships to production.
The systems we integrate with
On the storage side: SharePoint, Google Drive, Box, Dropbox Business, NetDocuments, iManage, S3, Azure Blob. On the workflow side: DocuSign, Ironclad, NetSuite, QuickBooks, Xero, Sage Intacct, SAP, Jira, ServiceNow. The pipeline is built to be storage-agnostic — wherever the documents already live, we work with that.
Where the model runs — privacy first
Document AI is the area where data residency matters most. Three deployment patterns, picked by document sensitivity:
- Vendor API with no-retention endpoints: Claude, GPT, or Gemini configured for zero data retention. Suitable for routine business documents.
- Your cloud: The full pipeline — ingestion, extraction, storage of intermediate state — runs inside your AWS, Azure, or GCP tenant. Vendor models accessed via your own keys; nothing crosses out of your environment except the prompt and response.
- On-prem / open-source: For privileged legal documents, regulated finance flows, or air-gapped environments. Llama, Mistral, or other open models running on hardware you control. Slower to ship; full data isolation.
How we control AI cost
Document pipelines are the fastest way to blow an AI budget — a 200-page contract is a lot of tokens, and processing 5,000 of them is real money. Cost discipline in every pipeline we ship:
- Cheap-model first pass for classification, premium model only on the segments that need deep extraction
- Aggressive caching at the document, page, and clause level — re-processing the same NDA returns the cached result
- Token budget caps per document type, daily and monthly ceilings, automatic shutoff
- Batch processing where SLA allows it — overnight runs on large archives cost a fraction of real-time
- A cost dashboard you watch directly, not one we screenshot for a slide
Audit trails your reviewers will accept
Every extraction is logged with the source document, the page, the model version, the prompt, and the confidence. Anything below confidence threshold goes to human review automatically. Your auditors get a clean record of how every figure or clause was produced — which is also what makes the system safe to scale.
The 30-day proof of value
One document class — vendor contracts, AP invoices, RFP responses, security questionnaires, pick the one that hurts most. We ship the pipeline in 30 days, measured against your current accuracy and cycle time. If the numbers don’t move, you don’t scale to class two. Cost ceiling in writing on day one.
Frequently asked
What about scanned PDFs and bad image quality?
We chain OCR (Tesseract, Azure Document Intelligence, or AWS Textract depending on quality) into the pipeline before the LLM sees the page. Low-confidence OCR gets flagged and routed to a human instead of feeding garbage into extraction.
Can the model decide on its own, or does a human always check?
You set the threshold per document type. High-confidence routine invoices can flow straight to posting. Contract clauses outside your playbook always go to a human. We tune those thresholds with you as your confidence in the system grows.
What about privileged or confidential documents?
Those go through the on-prem or your-cloud deployment path. We don’t recommend sending privileged material through any third-party API, even no-retention ones, and we’ll tell you that on day one.
What does this cost ongoing?
Token cost scales with volume — typically $500 – $5,000 / month at mid-market document throughput, lower with strong caching. Optional retainer for monitoring, accuracy tuning, and adding new document types. We size both honestly before you sign.