Contract Intake Triage & Risk Routing

Contract work rarely fails because of law; it fails because the file sits in the wrong inbox. Sales operations pushes, procurement screens, legal reviews—and each step adds searches, reformatting, and handoffs. The bottleneck is logistics, not jurisprudence. This use case replaces the scavenger hunt with an AI-led intake that reduces waiting, standardizes information, and keeps legal decisions firmly in human hands.

The problem today

Agreements arrive as PDFs, scans, and track-changed files. Teams copy clauses into spreadsheets, search for renewal windows, and forward drafts to “likely” owners. Every recheck adds hours; uneven variance tracking hides risk. Leadership sees delays without clarity on where time is lost.

The AI-led flow

An intake address (or portal) receives any contract artifact. An orchestration layer triggers a document-understanding agent to classify type (MSA, DPA, NDA, order form), extract key fields (term, renewal, liability caps, governing law, data residency), and compare findings to approved playbooks. A routing agent assembles a one-page brief, attaches the original, highlights variances, and sends it to the right owner with an audit trail. A review assistant answers “Where is X?” via retrieval-augmented queries over the document and policy library. Legal remains the decision-maker; the system removes the admin maze.

Privacy-by-design, compliance-aligned

Processing follows data minimization, access controls, and auditable logs. Sensitive sections (e.g., PII in DPAs) are masked in summaries. Processing can be confined to EU data centers and configured to avoid training on customer data. This is decision support, not legal advice.

What the pilot proves (30–60 days)

Pilot one document family (e.g., NDAs) and one region. Success means: measurable reduction in time-to-first-review, consistent variance detection against the playbook, and complete routing visibility. Legal validates summaries and approves or amends. The AI team tracks precision/recall on extracted fields and tunes prompts, rules, and schemas.

Hypothesis metrics (illustrative, not guaranteed):

• Cycle time: 5–7 days → 1–2 days to first legal touch
• First-pass extraction accuracy: 93–96% on defined fields after tuning
• Rework reduction: 30–40% fewer back-and-forth emails per contract

Quick ROI math (scenario):
If 600 contracts/year each consume 2 hours of manual prep, that’s 1,200 hours. At €90/hour blended, ~€108,000/year. If the pilot automates 60% of prep, recover ~€65,000 annually—before revenue pull-forward from faster close.

Risks and mitigations

Ambiguous scans, redlines without context, and non-standard layouts challenge extraction. The product and engineering team mitigates with OCR fallback, layout-aware parsing, and a “confidence dashboard” that flags low-confidence fields for mandatory human review. Governance is enforced with immutable routing logs and role-based access.

From pilot to scale

After the initial family succeeds, the AI team adds DPAs and MSAs, connects to e-signature, and feeds structured outcomes to the CLM or ERP. The taxonomy is versioned; playbooks live in the retrieval index; routing expands with rules rather than headcount. The operating model shifts from “Who has the file?” to “What decision is needed now?”