The Ultimate Guide to AI-Powered Legal Intake and Triage for In‑House Teams

McKinsey estimates knowledge workers spend nearly 20% of their time searching for information. For in-house legal, that time is magnified by ad hoc requests arriving via email, Slack, and ticketing tools—often without the context needed to act. Intake isn’t busywork; it’s where speed, alignment, and trust are won or lost.

This guide shows how AI-powered intake and triage turns legal from a reactive inbox into a proactive operating system. It’s built for GCs, legal ops leaders, and tech-forward buyers who want measurable impact this quarter—not another vague AI promise.

Why Intake Is the Highest-Leverage Workflow

Intake is the front door to legal. When it’s unstructured, everything downstream slows: prioritization, routing, cycle time, and risk decisions. When it’s standardized, you get:

- Predictable throughput and SLAs (service-level agreements)

- Faster cycle times because requests arrive complete

- Fewer escalations through clear thresholds and playbooks

- A living knowledge base as every decision feeds future work

The leverage comes from making requests machine-readable at the start—so triage and decisioning can be automated or assisted, not reinvented every time.

The Anatomy of AI-Assisted Intake

An effective AI intake and triage flow has five layers:

1) Channel capture: Meet business users where they are—Slack, email, or a portal—with a friendly, structured form that asks for the minimum viable details (counterparty, urgency, jurisdiction, data types, business owner).

2) Classification: An AI agent tags the request by type (NDA, vendor contract, marketing review, employment) and risk posture using your policy thresholds, not generic models.

3) Enrichment: The agent pulls missing context from connected systems (CRM, procurement, DPA library), links related matters, and applies your playbook positions.

4) Routing and SLAs: The request is auto-routed to the right queue or approver, with an auto-acknowledgement that sets expectations (e.g., “NDA within policy—ETA 4 business hours; exceptions route to Counsel”).

5) Decision logging: Approvals, redlines, and exceptions are captured as structured knowledge—so every outcome trains the next one.

On a platform like Sandstone, these layers sit on a knowledge foundation: playbooks, positions, clause guidance, and workflow rules that the AI can cite, not just infer. Every intake strengthens that foundation.

A Concrete Workflow: NDA Triage End‑to‑End

NDAs are high-volume, low-complexity—perfect for an AI pilot.

- Business user submits in Slack or a form with counterparty name, purpose, and urgency; uploads paper or selects your template.

- Sandstone’s agent classifies the request (mutual vs. one-way), checks term limits, governing law, confidentiality carve-outs, and IP ownership against your NDA playbook.

- If within thresholds, the agent assembles the approved template, fills metadata, and sends for e-signature; counsel is notified, but no human work is required.

- If out-of-policy (e.g., 7-year term, non-standard injunctive relief), the agent generates a redline aligned to your positions, flags exceptions, and routes to the right approver with a one-screen summary of risks and recommended responses.

- All decisions (accepted carve-out, rejected venue, approved term) are logged as structured data, improving auto-classification and future guidance.

Result: Legal stays in control of risk while moving at the speed of the business. Business users get clarity, not a black box.

What Good Looks Like: KPIs and Guardrails

Measure what matters, and enforce it with clear controls.

Key KPIs

- Time to first response: Target under 1 hour for standard requests

- Median cycle time by request type: NDAs should trend under 1 business day

- Auto-resolution rate: Percent of requests closed without human drafting

- Exception rate: Track by clause to pinpoint playbook gaps

- Knowledge reuse: Percent of matters citing existing positions or templates

Guardrails

- Approval thresholds: Define when humans must review (e.g., data sharing, unusual jurisdictions)

- Data handling: Restrict sensitive fields; mask personal data by default

- Audit trail: Immutable logs of who approved what, when, and why

- Model transparency: AI must cite the specific playbook or clause driving a decision

Two-Week Pilot Plan You Can Run Now

Start narrow to go fast. Here’s a simple path to value with NDAs:

Days 1–3: Baseline and scope

- Pull last 50 NDA requests; note average cycle time, common exceptions

- Finalize a 1-page NDA playbook with default positions and redline guidance

Days 4–7: Configure intake and triage

- Launch a lightweight NDA intake form in Slack/email with required fields

- Set routing rules (auto vs. review) and SLAs

- Connect e-signature and your contract repository

Days 8–10: Train and test

- Feed past NDA decisions to the AI agent; validate classification and redlines

- Dry-run 10 test requests; tune thresholds and summaries

Days 11–14: Go live and measure

- Enable auto-approval within policy; route exceptions to counsel

- Track KPIs daily; capture feedback from business users

If cycle time drops by 40–60% and exception rates hold steady, expand to vendor intake or marketing reviews next.

The Foundation for Scalable, Trusted Legal Ops

Great intake is more than a form; it’s a knowledge layer that compounds. Sandstone is built for strength through layers—each request, decision, and exception adds context. Crafted precision means your playbooks shape how the AI works, not the other way around. And natural integration meets teams in the tools they already use.

When intake and triage run on a living, AI-powered operating system, legal stops being a bottleneck. It becomes the connective tissue of the business—moving with clarity and confidence, at scale.

Actionable takeaway: Pick one workflow (NDAs), publish a structured intake, and enforce clear thresholds. Instrument KPIs from day one. Let the AI handle the routine, and use your humans for the judgment calls that build trust and accelerate growth.