AI Intake Triage for Legal Ops: From Noise to Signal

AI Intake Triage for Legal Ops: From Noise to Signal

Across recent Sandstone deployments, 40–60% of legal intake volume is routine, policy-backed, and automatable. Yet most teams still push these requests through shared inboxes and unstructured chats. The result: long cycle times, inconsistent decisions, and knowledge that never compounds.

AI can fix this—but only if intake is structured and your playbooks are codified. Here’s how to stand up AI intake triage that turns every request into faster, safer outcomes and a stronger legal foundation.

What It Is: Structured Intake Backed by Playbooks

AI intake triage converts free‑form requests into structured data, applies your positions and thresholds, and then routes or resolves the work.

Core components:

- Structured capture: guided forms that collect facts (counterparty, value, jurisdiction, data types, deadlines).

- Knowledge layer: playbooks, positions, clause libraries, and approval thresholds the AI can cite and apply.

- Risk triage: rules and models that classify requests by risk and complexity.

- Action paths: self‑service for low risk, automated approvals within thresholds, and fast routing for exceptions.

- Feedback loop: every decision updates the knowledge layer and improves the next one.

This is not “chat with a bot.” It’s a living operating system for intake, triage, and decisioning that scales what your team already does well.

Why It Matters: Speed, Consistency, and Compounding Knowledge

AI-driven intake triage impacts what business partners care about:

- Faster cycle time: low-risk work clears in minutes, not days.

- Lower legal lift: attorneys focus on exceptions and strategy.

- Reduced variance: decisions trace back to policy, not inbox roulette.

- Better stakeholder experience: clear SLAs, status, and self‑service paths.

- Measurable risk control: thresholds and approvals become auditable data.

- A stronger foundation: each request strengthens the knowledge layer instead of vanishing in email.

When intake becomes a structured front door, AI doesn’t just “move faster”—it compounds institutional knowledge.

How It Works on Sandstone: A Layered Playbook

Sandstone is built for strength through layers—data, playbooks, and workflows that build on each other. Standing up AI intake triage typically follows this path:

1) Standardize request types

- Start with one high‑volume workflow: NDAs, vendor onboarding, marketing reviews, or sales contract addenda.

- Define the fields needed to decide (e.g., contract type, value, data shared, urgency, counterparty region).

2) Codify your positions

- Capture clause fallbacks, redline boundaries, and approval thresholds (e.g., DPA required if PII leaves region; auto‑approve NDAs under 2 years with mutual confidentiality).

- Store them as versioned, citeable rules in Sandstone’s knowledge layer.

3) Train the AI agent on your knowledge

- Give it your playbooks, templates, and past decisions.

- Bind it to structured forms and routing rules so it can act, not just answer.

4) Automate paths by risk

- Self‑service: generate standard NDAs or marketing claim approvals when answers fit policy.

- Auto‑approval: within thresholds, the agent approves and logs the decision with citations.

- Escalation: exceptions route with a summary, risk flags, and suggested redlines.

5) Close the loop

- Capture outcomes, reasons for overrides, and new edge cases.

- Update playbooks in-line; the next similar request gets smarter handling.

Mini-scenario: Marketing needs a partner NDA. The requester completes a two‑minute form. The AI agent checks counterparty type, term, and geography against your NDA playbook, generates the correct template, and sends for signature. If the counterparty insists on unilateral confidentiality and 5‑year term, the agent drafts fallbacks and routes to counsel with a delta summary.

Common Pitfalls to Avoid

- Starting without playbooks: AI can’t invent your risk posture. Write minimal, testable rules first.

- Letting free text dominate: require the 6–10 fields that determine risk; keep “notes” optional.

- Exception‑first design: optimize for the 60% routine path, then handle edge cases.

- No human‑in‑the‑loop: set escalation thresholds and require approvals where policy demands.

- Uninstrumented approvals: log every decision with policy citations for audit and learning.

Metrics That Prove It Works

Track a small, durable set:

- Median cycle time by request type and risk band

- Percent auto‑resolved/self‑service vs. routed to counsel

- SLA adherence and backlog age

- Escalation rate and top reasons for override

- Rework/variance (how often decisions deviate from playbook)

- Knowledge reuse (rules/templates referenced per decision)

One Practical Next Step

Pilot with one workflow. Pick NDAs or vendor onboarding, define a 10‑field form, document five non‑negotiables and three fallbacks, set auto‑approval thresholds, and run a two‑week trial with Sales or Procurement. Measure cycle time, auto‑resolve rate, and escalations. Tune weekly; then roll to the next workflow.

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When intake is structured and decisions are layered, legal stops being a bottleneck and becomes the connective tissue of the business. Sandstone’s AI‑powered operating system turns every request into speed, alignment, and trust—knowledge that compounds rather than disappears.

Call to action: Book a demo to see AI intake triage running on your top workflow in under 30 days.