Why AI Triage Matters for In‑House Legal (and How to Automate It Safely)

Your legal inbox is not a queue—it’s your operating system. For the mid-market and enterprise teams we work with, intake and triage can swallow 25–40% of attorney hours. Every unstructured Slack ping, email forward, and Jira ticket creates drag, inconsistency, and risk. The opportunity is clear: if legal can triage with precision at scale, you turn chaos into compounding knowledge—and you buy back the time to practice law.

The Cost of Manual Intake (and Why It Compounds)

Manual intake feels manageable until it isn’t. Requests spread across Slack, email, Confluence pages, and ad‑hoc spreadsheets. Context is missing, SLAs are ambiguous, and work gets routed by whoever is online rather than by risk or skill. The result: slow first responses, uneven service, and institutional knowledge that evaporates when people change roles.

The hidden costs stack up:

- Lost metadata: business owner, jurisdiction, data types, and deadlines aren’t captured consistently.

- Rework: attorneys chase context, duplicate analysis, and reopen closed loops when facts change.

- Risk drift: inconsistent triage means low‑risk items clog pipelines while high‑risk issues wait.

- Poor visibility: leadership can’t answer basic questions like volume by matter type, cycle time by risk tier, or SLA adherence by partner team.

When legal is the bottleneck, the business routes around you. That’s how exceptions become norms—and how risk scales quietly.

What “AI Triage” Actually Means for Legal Ops

AI triage isn’t magic. It’s a repeatable flow where agents interpret requests, apply your policies, and take the next safe step. In practice:

- Normalize intake from Slack, email, and forms into a single queue with required fields (e.g., counterparty, data handled, geography, urgency, system touchpoints).

- Classify by request type (NDA, vendor DPA, marketing claim review, product counseling, procurement clause deviation) and risk tier using your rubric.

- Extract entities (counterparty, effective date, data categories) and map them to your playbooks.

- Route to the right owner or auto‑resolve if the request meets pre‑approved conditions.

- Generate first drafts: NDA approvals within policy, risk summaries for vendor DPAs, issue lists for product tickets.

- Log everything: decision, rationale, version of the playbook used, and timestamps for audit.

On Sandstone, AI agents operate on your living playbooks and positions—the knowledge layer that encodes how your team decides. The more you triage, the sharper the system gets: each intake strengthens institutional memory instead of diluting it.

Guardrails: How to Automate It Safely

Triage is where speed meets judgment. To automate without losing control, put guardrails where they matter most:

- Policy‑first decisions: encode risk rubrics, fallback paths, and escalation criteria in versioned playbooks. No policy, no automation.

- Human‑in‑the‑loop: route anything above a confidence threshold or outside guard bands for review; keep low‑risk within policy on autopilot.

- Data hygiene: redact or mask sensitive fields before model access; apply least‑privilege access and role‑based routing.

- Auditability: capture reasoning, source citations (contracts, policies, prior decisions), and model/version IDs for every action.

- Jurisdiction & residency: ensure model routing and data storage meet your residency and privacy constraints.

- Integrations with intent: push structured outcomes back to CLM, Jira, Salesforce, or procurement tools so downstream teams see the same truth.

Safety isn’t a bolt‑on. It’s the blueprint. With Sandstone, guardrails are carved into the workflow: AI agents act inside your rules, not outside them.

Start Small: A 30‑Day Pilot Plan

If you’re new to AI triage, resist the urge to boil the ocean. Prove value with one high‑volume, low‑risk flow—often NDAs or marketing claim reviews.

Week 1

- Baseline: measure current volume, first‑response time, auto‑resolvable rate, and cycle time.

- Playbook: codify approval criteria and exceptions (e.g., mutual NDA with standard terms and no changes = auto‑approve).

Week 2

- Intake: add a short form to Slack/email with required fields; normalize into a single queue in Sandstone.

- Extraction: enable AI to pull entities (party names, term, governing law) and flag deviations.

Week 3

- Automation: auto‑approve in‑policy requests; generate explainable summaries for out‑of‑policy ones and route to the right owner.

- Comms: publish the new path and SLA to partner teams.

Week 4

- Review: compare metrics to baseline; interview business users and attorneys; capture learnings.

- Iterate: broaden to a second flow (vendor DPA intake with risk tiers or product counsel tickets in Jira).

Actionable takeaway: pick one request type, write a one‑page playbook with clear approval thresholds, and wire it to a single intake channel. Let AI handle the in‑policy 60–70% and use the reclaimed hours to improve the next playbook.

The Payoff: Faster Business, Stronger Trust

When triage is precise, legal stops being a speed bump and becomes the connective tissue of the company. Business teams get instant answers for routine asks, attorneys focus on the hard problems, and leaders get visibility into throughput, risk, and resourcing.

This is the Sandstone philosophy: strength through layers, crafted precision, and natural integration. By turning playbooks, positions, and workflows into a living, AI‑powered operating system, every intake, triage, and decision compounds your knowledge instead of leaking it. That’s how legal scales with clarity—and how trust becomes the foundation for growth.