The Ultimate Guide to AI-Powered Legal Intake and Triage

More than 20% of a knowledge worker’s time is spent searching for information. For in-house legal, that often balloons during intake—when requests arrive incomplete, unprioritized, and scattered across email, Slack, and ticketing tools. The result: slow response times, frustrated business partners, and legal seen as a bottleneck.

This guide explains how to redesign intake and triage with AI, layered knowledge, and pragmatic process—so every request strengthens your legal foundation.

Why Intake Is the Hidden Bottleneck

Most teams don’t fail at complex work—they stall on the routine. NDAs, vendor reviews, policy questions, and clause checks make up the bulk of volume, yet they arrive with missing context (deal size, jurisdiction, data flows), unclear urgency, and no routing logic. Without structure:

- Time-to-first-response drifts.

- Work bounces between attorneys.

- Institutional knowledge lives in inboxes.

When intake is unstructured, your smartest attorneys become human routers. That’s expensive—and it erodes trust with the business.

What “Good” Looks Like: A Living Intake Layer

High-performing legal teams approach intake as a living operating system, not a form. Core components:

- Standardized capture: Requester, business unit, contract type, counterparty, value/term, data categories, deadlines, governing law, and risk flags.

- Embedded knowledge: Playbooks and positions that surface contextually (e.g., “Our stance on DPAs with subprocessor changes”).

- Automated routing: Queues by work type, expertise, and SLA; clear escalations for high-risk or time-sensitive matters.

- Feedback loops: Auto-acknowledgment, status transparency, and structured outcomes that update the knowledge base.

The goal is layered strength—each decision, exception, and resolution becomes part of the system so future requests move faster with higher confidence.

Where AI Agents Add Real Speed (Without Risk)

AI becomes transformative when it augments—not replaces—judgment. Practical agent patterns for intake and triage include:

- Classification and enrichment: Auto-label requests by matter type, jurisdiction, and risk; prompt requesters for missing fields (deal value, data flows) before a human ever sees it.

- Policy Q&A: Resolve repeatable questions against your approved positions and policies, with citations to source pages and confidence thresholds.

- Document-first triage: Parse inbound NDAs/MSAs, detect key clauses (termination, data processing, governing law), and assign to the right queue with a risk score.

- Drafting and deflection: Generate approved NDA variants or vendor questionnaires automatically for low-risk paths; route only exceptions to counsel.

- SLA-aware routing: Balance workloads and deadlines; escalate proactively when a request risks breaching SLA.

On a platform like Sandstone, these agents sit on top of your playbooks, templates, and prior decisions—the knowledge layer—so outputs are explainable and aligned to your standards. Every intake strengthens the system.

Metrics That Matter: Prove the Lift

To move from anecdotes to accountability, track a small set of leading and lagging indicators:

- Time to first response (median/90th percentile)

- Cycle time by work type (NDA, DPA, procurement review)

- Auto-resolution rate (percent resolved without attorney touch)

- Deflection to self-serve (policy Q&A, template paths)

- SLA attainment and aging backlog

- Requester satisfaction (post-close CSAT)

Teams that implement structured intake with AI commonly see 30–50% faster cycle times on routine work and higher satisfaction from business stakeholders—because they get clear answers faster.

A 30-Day Pilot Plan You Can Run Now

You don’t need a year-long program to see value. Start small, prove impact, then scale.

Week 1: Scope and sources

- Select one high-volume pathway (e.g., NDAs or vendor DPAs).

- Centralize intake for that path (form or Slack app) with required fields.

- Import your current playbook, template(s), and positions into a searchable knowledge space.

Week 2: Agent enablement

- Turn on classification + enrichment to label and complete missing context.

- Enable document-first triage (clause extraction + risk scoring) for inbound agreements.

- Set routing rules and SLAs; auto-acknowledge with a clear ETA.

Week 3: Guardrails and feedback

- Require human review above a risk threshold; auto-resolve low-risk paths with approved templates.

- Capture outcomes (approved/edited/escalated) to refine the playbook.

Week 4: Measure and expand

- Report on cycle time, auto-resolution rate, and SLA attainment.

- Socialize results; add a second pathway (e.g., policy Q&A or marketing review) using the same pattern.

Actionable next step: Audit last quarter’s intake. Identify the top two repeatable request types and the three fields most often missing on submission. Use those to design your pilot form and routing rules.

The Bedrock Advantage

When intake is structured and knowledge is layered, legal stops reacting—and starts compounding. Each request teaches the system. Each exception sharpens your positions. The business gets speed and clarity; legal earns trust.

This is the philosophy behind Sandstone: strength through layers, crafted precision, and natural integration with how your team already works. By turning playbooks, positions, and workflows into a living, AI-powered operating system, Sandstone makes institutional knowledge accessible and actionable. Intake, triage, and decisions become a flywheel—not a queue—so legal can operate as the connective tissue that helps the company grow with confidence.