How to Automate Legal Intake and Triage

Illustration of AI-powered legal intake workflow

How to Automate Legal Intake and Triage

Most in-house legal teams still manage requests via email and Slack pings. Research from UC Irvine shows it takes 23 minutes to refocus after an interruption—multiply that by a day of ad‑hoc requests and you’re underwater before lunch. AI‑assisted intake and triage changes that, turning scattered asks into structured data, policy‑driven routing, and faster answers with less risk.

This is where a knowledge layer matters. When your playbooks and positions live in a system like Sandstone, every intake becomes a chance to apply—and strengthen—institutional knowledge.

Why Automate Legal Intake Now

Intake is the front door for commercial queries, vendor reviews, NDAs, policy questions, and privacy tickets. Left unmanaged, it creates delays, inconsistent decisions, and opaque risk tradeoffs. Automating intake doesn’t mean replacing attorneys; it means standardizing capture, using AI to resolve the routine, and escalating what truly needs counsel.

Modern intake blends three elements:

A single front door (web form or Slack app) that captures context.
A living knowledge base of playbooks and fallback positions.
AI agents that classify, respond, and route under policy guardrails.

Compared to legacy ticket queues, AI intake reduces context switching, enforces playbooks consistently, and surfaces metrics you can defend in QBRs—time to triage, auto‑resolution rate, and cycle time by request type.

The Blueprint for AI Intake and Triage

Here’s a practical pattern legal teams deploy on Sandstone:

Single front door

Offer a simple form or Slack / Teams app. Ask for request type, counterparty, links/docs, urgency, and key terms (e.g., data types, value, jurisdiction).

Structured data capture

Normalize inputs with dropdowns and required fields so downstream automation has clean data (e.g., map “DPA” vs “data addendum” to one type).

Knowledge lookup and instant answers

An AI agent checks your approved playbooks to auto‑answer policy questions ("What’s our position on MFN?"), provide self‑serve NDA or MAT agreements, or propose redlines aligned to fallback clauses.

Policy routing and auto‑approvals

Route by business line, deal size, data risk, or region. Approve low‑risk items automatically (pre‑approved NDAs) and queue exceptions for attorneys.

Human‑in‑the‑loop review

For escalations, present a concise brief: request context, suggested positions, risk flags, and recommended next steps. Attorneys edit once, not start from scratch.

Audit trail and feedback loop

Log every step. Capture “accept/override” feedback to refine playbooks so the system gets smarter with each decision.

Example flows you can automate on day one:

NDAs: request → template match → e‑signature → archive in repository.
Low‑risk vendor DPAs: classify data types → compare to approved standard → auto‑approve or flag deltas.
Sales redlines: identify clause variants → propose fallbacks → route to deal desk if threshold exceeded.

Guardrails, Metrics, and Change Management

Automation must be safe and measurable. Bake in:

Data controls: redact PII on upload, enforce least‑privilege access, and log model prompts/outputs.
Policy constraints: AI acts only within approved playbooks; anything ambiguous escalates.
Model transparency: show the sources used to generate an answer and the confidence band.
Legal recordkeeping: preserve artifacts for audit, discovery, and governance.

Track the right KPIs from day one:

Time to triage (request to first response)
Auto‑resolution rate (no human touch)
Cycle time by request type
Exception rate (and top reasons)
Requester satisfaction (simple 1–5 post‑resolution)

Change management tips:

Start narrow: pick one or two request types with clear playbooks.
Socialize the front door: post it in Slack, pin it in Confluence, embed in CRM.
Close the loop: announce wins (e.g., “NDA cycle time down 65%”) and iterate.

Get Started in Two Weeks

A lean rollout plan:

Map your top 10 request types by volume and pain.
Extract the current playbook for your #1 candidate (e.g., NDAs or low‑risk DPAs).
Configure the intake form/app and required metadata.
Encode policy thresholds and fallbacks.
Pilot with one business unit; measure baseline vs. post‑launch.

Actionable takeaway: Stand up a sandbox intake for a single workflow (NDAs). Target 30% auto‑resolution in week one. Use attorney feedback to tighten fallbacks and expand to the next workflow.

With Sandstone’s layered knowledge and modular agents, this pattern fits how your team already works—Slack requests, email attachments, CRM context—without forcing a new behavior. Each intake, triage, and decision compounds your knowledge base, turning legal from a reactive queue into a proactive operating system for speed, alignment, and trust.

Ready to see it in action? Get a demo of Sandstone’s AI‑powered intake and triage.

FAQ

What if our playbooks are still in docs?

Start there. Import them, tag clauses and fallbacks, and iterate—automation improves as your knowledge layer matures.

Will AI make the wrong call?

Not if you constrain it. Keep auto‑actions within low‑risk thresholds and route ambiguity to humans with full context and an audit trail.

How does this fit our security posture?

Use role‑based access, data loss prevention on uploads, and model logging. Sandstone supports enterprise controls without slowing work.

Illustration of AI-powered legal intake workflow