The Ultimate Guide to AI-Powered Legal Intake and Triage

The Ultimate Guide to AI-Powered Legal Intake and Triage


If you’re like most in-house teams, well over half of inbound legal requests are repeats: NDAs, vendor reviews, policy clarifications, and playbooked contract terms. Yet many counsel still spend hours triaging Slack and email, context-switching, and chasing missing information. That is lost leverage. Intake and triage is the upstream control plane for legal. Get it right and you compress cycle time, improve risk decisions, and create data you can actually use.

This guide breaks down a modern, AI-assisted intake stack—what “good” looks like, where AI truly helps, the KPIs that matter, and a one-week pilot to get started.

Why Intake Is Your Leverage Point

Legal intake is not a form; it’s a workflow that governs speed, quality, and data integrity.

- Speed: Clear paths for common work (e.g., NDA, DPA, vendor diligence) stop the slack-back-and-forth and cut queue time.

- Quality: Structured questions up front eliminate rework. Required attachments, counterparty details, and business context ride along from the start.

- Data: Every request becomes a record—type, risk, owner, SLA, disposition—so you can manage capacity and demonstrate value.

On Sandstone, intake isn’t a single portal. It’s a living system layered across where work happens—email, Slack, procurement, CRM—so business users don’t change habits while legal captures consistent data.

What “Good” Looks Like: A Layered Intake System

A resilient intake model is layered, not linear. Think strength through layers:

1) Capture: Accept requests in native channels (Slack, email, procurement, CRM) and normalize them into a common request object.

2) Classify: Use AI to identify request type (e.g., NDA vs. MSA vs. policy question) from free text and attachments.

3) Enrich: Extract entities like counterparty, governing law, renewal terms; pull vendor metadata from procurement; map requester to department and region.

4) Route: Apply playbook rules to assign owner, set SLA, and determine path (auto-response, self-serve, or counsel review).

5) Resolve: Generate first drafts (e.g., NDA), answer policy questions with citations to approved positions, or launch the right workflow.

6) Learn: Capture outcomes (approved/blocked, clauses negotiated, time-to-close) to refine playbooks and automation over time.

The result is crafted precision: right-sized paths for routine, escalations for nuance, and data stitched across systems without forcing teams to adapt to a new tool.

Where AI Agents Actually Help (And Where They Don’t)

AI should remove friction, not replace judgment. High‑impact uses:

- Intent detection and smart forms: Convert a Slack message into a structured NDA request, prompting only for missing fields.

- Document intelligence: Pull entities and risk signals from PDFs (e.g., auto-renewal, governing law) and tag the request.

- Playbook application: Suggest fallback clauses and redlines consistent with your positions; draft NDAs within guardrails.

- Policy answers with citations: Respond to repeat questions (“Can we share customer logos?”) by quoting approved guidance.

- Workflow orchestration: Open tickets in procurement for vendor risk, update opportunity records in CRM, and post status back to Slack.

Where humans stay central:

- Novel or strategic matters, cross‑border nuance, and escalations where risk appetite must be calibrated to business context.

- Final approvals and exceptions to standard positions.

Sandstone agents are designed to be natural integrations—quietly enriching, routing, and drafting—while counsel makes the calls that count.

Metrics That Matter (And How To Move Them)

Measure what you manage:

- Time to triage: Minutes from intake to owner assignment. Goal: near real‑time via auto‑classification.

- Cycle time by type: NDAs should be hours, not days. Dashboards highlight bottlenecks.

- Auto‑resolution rate: Percentage handled without counsel (policy Q&A, self‑serve NDAs). Improves as playbooks mature.

- SLA adherence: By request class and business unit. Signals staffing needs and process gaps.

- Knowledge reuse: How often AI answers with a cited playbook or position. A proxy for compounding institutional knowledge.

Levers to improve: tighten forms to required fields, expand playbooks where questions repeat, and push status updates back to business tools to cut “just checking in” pings.

Try This Next Week: Stand Up an AI‑Assisted NDA Intake

A focused pilot proves value without boiling the ocean.

- Day 1–2: Inventory your NDA variants and approval rules. Identify required fields (counterparty, term, governing law) and where requests originate (email, Slack, sales).

- Day 3: Build a single NDA intake path in Sandstone. Enable AI to classify free‑text requests as “NDA” and prompt only for missing fields.

- Day 4: Connect systems: log requests from Slack, attach files from email, write status back to CRM/opportunity.

- Day 5: Turn on agent‑drafted NDAs using your templates and positions. Route exceptions to counsel with extracted risk highlights.

Success criteria: 70%+ of NDAs auto‑drafted, triage time under 5 minutes, cycle time under 24 hours, full request metadata captured for reporting.

The Bedrock of Trust and Growth

When intake becomes a living, AI‑powered operating layer, legal stops firefighting and starts compounding knowledge. Every request strengthens your playbooks; every decision informs the next. That’s how legal shifts from reactive support to a proactive force—speeding deals, clarifying risk, and earning trust across the business.

Sandstone was built for this: layered data, modular workflows, and a natural fit with how your teams already work. Make intake your leverage point, and let the foundation carry the load.