Automated Financial Contract Risk Extraction in Construction

AI extraction engines identify financial and legal risk entities - indemnity clauses, payment terms, liability caps, insurance requirements, prevailing wage triggers - directly from contracts, change orders, and RFIs, then cross-reference them against your master templates, bid assumptions, and regulatory thresholds (OSHA, Davis-Bacon, local codes) to surface margin impacts and compliance gaps in hours instead of days. The system integrates with Procore, Sage 300, and Viewpoint Vista, so risk flags feed directly into your financial workflows without manual data entry. Finance teams review AI-generated risk profiles and approve contracts with full context and control.

Yes. All data transmissions between Procore, Sage 300, and our extraction engine are encrypted end-to-end. Construction-specific regulatory requirements (OSHA documentation, Davis-Bacon wage records, AIA billing formats) are handled within your own secure environment or via dedicated private cloud deployment. Your firm retains full data ownership and audit trails.

We work the C.O.R.E. Method, with a working system live inside the first 100 days. Weeks 1-3 audit the work: system integration with Procore, Sage 300, and Viewpoint Vista. Weeks 4-10 build: model calibration using your historical contracts and risk classifications, then pilot testing with one project team. Weeks 11-14 deploy: full rollout and Finance team training. A rollout like this is scoped to show measurable results - faster change order approvals, reduced manual review time - within 60 days of go-live, with gains compounding as the model learns your escalation patterns.

It reads the contract, change orders, and RFIs, flags indemnity clauses, payment terms, and liability caps against your master templates and bid assumptions, and surfaces the margin impact of each flag. The flags land in Procore, Sage 300, or Viewpoint Vista, where your finance team already works. Your people approve every contract; the system does the reading.

It learns your escalation pattern, not a generic severity scale. If your firm always negotiates liability caps down or waives a certain insurance rider on repeat subcontractors, the system tracks that from Finance's approvals and rejections and starts surfacing the same pattern in new contracts before your team even opens them. That loop only works if Finance logs the decision inside the platform rather than handling it off to the side - skip that step and the model keeps flagging things your team already knows how to handle, which is the fastest way to lose trust in the queue.

Yes, within limits you should test on your own documents. The ingestion layer is calibrated for the formats construction actually runs on - AIA forms, scanned PDF subcontractor agreements, and RFI markups - and converts them to structured text before extraction. Every flag carries a source citation back to the exact clause and page, so your finance team can verify what the system read against the original document. Files too degraded to read reliably get queued for human review rather than guessed at, and the pilot phase is where we confirm how much of your real document flow the system handles cleanly.

By doing the reading no one has time for. The system pulls every indemnity clause, payment term, and liability cap out of contracts, change orders, and RFIs, checks them against your own templates, bid assumptions, and regulatory thresholds, and pushes the exceptions into your financial workflow. Finance reviews the flagged risk profile and approves or rejects the contract with full context - faster decisions, less financial exposure, and nothing signed unread.