How to Turn Legal Intake Into an AI-Powered Triage Engine

Across mid-market and enterprise teams, intake and triage can consume up to half of legal’s weekly cycles. The surprising part: most requests share the same few variables and follow repeatable decisions. When you turn intake into an AI-powered triage engine, you move from queue management to controlled, measurable throughput—and you compound institutional knowledge with every ticket.

Why Intake Is Where AI Belongs

Intake sits at the intersection of business speed and legal rigor. It’s structured enough to standardize and dynamic enough to benefit from learned context.

- High volume, repeatable decisions: NDAs, vendor reviews, marketing copy checks, policy exceptions, and approvals.

- Policy-heavy, document-light: the core logic lives in playbooks and positions rather than bespoke drafting.

- Dependency hub: privacy, procurement, security, finance, and sales ops all connect here.

- Measurable outcomes: first-response time, cycle time, deflection, and adherence to playbook.

With an AI layer, intake can extract context from requests, match that context to your positions, apply routing and guardrails, and either resolve or escalate with the right artifacts. Instead of triaging ad hoc via email or chat, you run a consistent operating system that gets smarter with use.

Build the Layers: From Playbooks to Decisions to Workflows

A durable intake engine is layered, not monolithic:

1. Canonical request types: Name and define the 5–10 top request types (e.g., NDA, MSA, vendor assessment, marketing review, policy exception, data sharing).

2. Positions and thresholds: Document where the company stands on common forks in the road—e.g., PII categories, data residency, indemnity caps, SLA standards, risk thresholds.

3. Decision trees and fallbacks: Map the logic you already apply: when to auto-approve, when to request more info, when to push to privacy or procurement, when to route to senior counsel.

4. Artifacts and templates: Approved templates, clause libraries, justifications, and pre-written rationale that explains the decision to the business.

5. Integrations and delivery: Intake via Slack, Teams, email, or a form; outputs to Jira, ServiceNow, Salesforce, or a signed document workflow.

Platforms like Sandstone encode these layers so they work together—AI pulls the right variables from unstructured requests, applies your positions with crafted precision, and moves the matter forward inside your existing channels. Knowledge compounds because every decision, exception, and rationale is captured and searchable.

A Concrete Workflow: Vendor NDA and SaaS Procurement

Consider a common path that touches legal, privacy, and procurement.

- Step 1: Intake collects the vendor name, use case, data types, user count, region, term, and urgency—via a short form or by extracting from an email or Slack message.

- Step 2: The AI agent checks your positions: Does this involve personal data, special categories, or cross-border transfers; does spend exceed a threshold; is the vendor on an approved list; is the term within policy.

- Step 3: Decisioning fires:

- If low risk and under thresholds, auto-issue the mutual NDA with a pre-approved signature path and notify the requester with next steps.

- If data risk is present, open a privacy assessment and collect a DPA; pre-populate the questionnaire from the vendor’s security page and prior assessments.

- If spend exceeds policy, route to procurement with the business context and recommended terms (price cap, renewal notice, termination for convenience).

- Step 4: Generate artifacts: a clean NDA, a redline when vendor paper is required, a decision memo explaining the rationale, and tickets for downstream teams.

- Step 5: Capture knowledge: the agent saves outcomes, exceptions, and the clauses that resolved negotiation so the next similar request is faster.

Done right, legal moves from reactive handoffs to proactive, explainable decisions that stakeholders trust.

The Metrics That Matter

Track a small, durable set of KPIs to prove impact:

- First-response time: Time from request to acknowledgment or automated next step.

- Cycle time by request type: Median time from intake to resolution; segment by auto-resolved vs. escalated.

- Deflection rate: Percent resolved without attorney time beyond review threshold.

- Auto-approval and auto-generation rates: NDAs issued, DPAs prepared, approvals granted by policy.

- Playbook adherence: Variance from standard positions; monitored as exceptions per 100 matters.

- Knowledge reuse: Percentage of matters using prior clauses, rationales, or vendor profiles.

These metrics quantify speed, consistency, and the compounding effect of your knowledge layer.

Implement in 30 Days: A Practical Plan

Week 1: Pick five request types that make up 60–70% of volume. Write the minimal decision logic and fallbacks. Collect the templates and standard rationales you already use.

Week 2: Configure intake in the channels people use today (Slack, email, or a form). Enable AI extraction of key variables. Turn on notifications and set clear SLAs.

Week 3: Pilot with one business unit. Keep human-in-the-loop escalation for edge cases. Measure first-response and cycle time daily; review exceptions.

Week 4: Expand to a second request type with cross-functional dependencies (e.g., vendor assessment). Pipe outputs to procurement and privacy systems. Hold a 30-minute retro to tune positions and thresholds.

Actionable next step: Stand up a two-week micro-pilot. Choose NDAs and one vendor review path, map three decision rules each, and enable automated artifacts and routing. Measure deflection and first-response. If you hit a 30% deflection rate, widen scope; if not, refine your thresholds and templates.

The Foundation for Trust and Growth

When intake is a layered, AI-powered system, legal becomes the connective tissue of the business. Every request strengthens your foundation—positions sharpen, decisions speed up, and knowledge stops evaporating. Sandstone is built for this kind of work: strength through layers, crafted precision around your playbooks, and natural integration into how teams already operate.

That is how legal shifts from reactive support to a proactive force for speed, alignment, and trust—at the heart of the business.