AI-Powered Legal Intake and Triage: What It Is and Why It Matters

Legal teams quietly lose entire days to messy intake. Surveys and internal benchmarks often show 20–40% of in-house legal time goes to capturing requests, asking for missing details, and routing work. That overhead slows the business and frustrates stakeholders. The good news: AI can handle much of it—without forcing your teams to change how they work.

What It Is: AI Intake and Triage, Defined

AI-powered intake and triage automatically captures legal requests from the channels your business already uses—email, Slack, ticketing tools—then classifies, enriches, and routes them. “Intake” is how a request enters legal (e.g., a sales rep asking for an NDA). “Triage” is the decisioning that follows: Is it an NDA? Is signature authority needed? What’s the SLA? Who owns it?

In practice, an AI agent reads the request, maps it to your playbooks, identifies the request type, extracts key data (counterparty, jurisdiction, dates), flags missing info, and pushes a structured ticket to the right queue with the right priority. The result: fewer back-and-forths, faster first responses, and cleaner data from day one.

Why It Matters for Lean Legal Teams

- Speed and predictability: Automated classification and enrichment reduce manual review and deliver consistent first-response times, often down to minutes, not days.

- Alignment with the business: Requests land with clear next steps aligned to approved playbooks and positions, reducing variance and rework.

- Risk and compliance control: Standardized fields and routing help enforce policies (e.g., data processing addenda, export controls) and audit trails by default.

- Data you can trust: Every intake becomes a structured record. Over time, that dataset powers capacity planning, SLA tuning, and pattern detection.

- Scale without sprawl: As volume grows, AI absorbs the repetitive work so your team can focus on negotiation, strategy, and high-risk decisions.

This is where Sandstone shines: layered knowledge (playbooks, fallback positions, approval rules) sits behind a natural, AI-powered intake surface. The agent doesn’t replace judgment—it applies crafted precision at the front door, so your experts operate at the top of their license.

A Workflow You Can Automate Today

Consider a common flow: Sales posts an NDA request in Slack.

1) Capture: The AI agent watches a designated Slack channel and email alias. When a request arrives, it identifies the matter (NDA) and creates a draft ticket.

2) Enrich: It extracts key fields—counterparty name, governing law, mutual vs. unilateral, requested turnaround—and checks against your standard.

3) Complete: If details are missing, the agent asks targeted follow-up questions in-thread (e.g., “Is this mutual or unilateral?”), then updates the ticket automatically.

4) Decide: Using Sandstone’s playbooks, the agent determines whether the standard NDA applies or if deviations trigger approval (e.g., unilateral NDA + 5-year term > legal review).

5) Route: The agent assigns the request to the right queue, sets SLA based on request type and customer tier, and posts a confirmation back to the requester.

6) Generate: For standard paths, it drafts the NDA from your approved template and attaches it to the matter for one-click send.

7) Track: It logs all steps, timestamps, and decisions, so you can measure throughput and adherence.

This is “strength through layers” in practice: layered data, modular workflows, and decisions that build on one another—without forcing your teams to change tools.

Metrics That Prove It Works

- First-response time: Aim for <30 minutes during business hours for standard requests.

- Cycle time to assignment: Target same-day handoff to the right owner.

- Request completeness rate: Increase to 85–95% before human review.

- Auto-resolved rate: 20–50% of low-risk requests handled end-to-end via playbooks.

- SLA adherence: 90%+ on defined request types.

- Stakeholder satisfaction (CSAT): Track post-resolution scores to validate experience, not just speed.

Start with a baseline, then review metrics weekly for the first quarter. Small tweaks to required fields and routing logic can yield outsized gains.

Pitfalls to Avoid (and How to Fix Them)

- Shadow channels: Requests drift into DMs. Fix by enabling capture across Slack/email and posting clear intake guidance.

- Brittle forms: Overlong forms drive abandonment. Use dynamic, AI-driven prompts that only ask what’s needed.

- Fuzzy playbooks: If positions aren’t explicit, AI can’t apply them. Codify thresholds, fallbacks, and escalation paths.

- Governance gaps: Keep a transparent log of AI decisions and maintain human-in-the-loop for exceptions.

- Change fatigue: Launch in one team (e.g., Sales NDAs), prove value, then expand to Privacy, Procurement, and Marketing.

One Practical Next Step

Map your top five request types (NDA, vendor review, marketing review, DPA, product counsel). For each, define:

- Required fields (minimally sufficient data)

- Standard vs. exception triggers

- Routing rules and SLAs

- Approved templates and fallback positions

Then pilot an AI intake flow for one request type in one channel. Measure first-response time, completeness rate, and auto-resolved rate for 30 days. Iterate, then scale.

A strong legal foundation isn’t about more headcount—it’s about smarter layers. With Sandstone, every intake, triage, and decision strengthens your operating system, turning institutional knowledge into action. That’s how legal stops being a bottleneck and becomes the connective tissue for speed, alignment, and trust across the business.