AI-Powered Intake and Triage: A Playbook for In‑House Legal Teams
More than half of in-house legal teams report rising request volume without headcount growth. The strain isn’t a lack of legal judgment — it’s missing context, manual triage, and decisions that don’t compound. When intake is noisy, everything downstream slows.
Why Intake Is Your Highest‑Leverage Automation Point
Most legal requests are predictable at the edges: NDAs, vendor reviews, marketing claims, DPAs, playbooked sales clauses. Yet they arrive through fragmented channels (email, Slack, tickets) with inconsistent details. Teams spend hours chasing context, routing work, and re-answering settled positions.
Automating intake and triage addresses three core problems at once:
- Speeds time to first response by standardizing the information you need, upfront.
- Reduces rework by applying playbooks and positions consistently.
- Captures decisions so knowledge compounds instead of disappearing into inboxes.
Done well, intake becomes a living operating system — one that learns with every request. That’s the Sandstone model: strength through layers, crafted precision, and natural integration with how legal already works.
The AI Triage Blueprint: From Request to Decision
Move from ad hoc to orchestrated with a simple, layered flow that AI agents can operate inside Sandstone:
1) Smart intake forms: Dynamically request the right fields based on the request type (e.g., NDA vs. vendor DPA) and pull context from connected systems (CRM for entity, procurement for vendor risk, marketing for campaign details).
2) Classification and enrichment: An AI agent classifies the request, extracts key facts (counterparty, data sharing, template vs. third‑party paper), and checks for required attachments or approvals.
3) Policy and playbook lookup: The agent consults your approved positions, fallback clauses, and risk thresholds. For low‑risk, templated matters, it can propose an answer or assemble a draft using your language.
4) Routing and SLA: Based on type, region, and risk, the agent routes to the right queue or person, sets SLAs, and surfaces a concise brief so humans start with signal, not noise.
5) Suggested action: For routine items, the agent can send an approved template, schedule a renewal task, or return a self‑serve answer that cites the underlying policy.
6) Learning loop: Every outcome (approved, escalated, exception) writes back to the knowledge layer. Next time, the system knows more — and you move faster with less risk.
This “strength through layers” approach compounds. Each decision informs the next, gradually carving a precise fit to your team’s contours.
Guardrails First: Control, Audit, and Alignment
Automation without control is a shortcut to risk. Design guardrails into the workflow:
- Role‑based thresholds: Define what can be auto‑approved (e.g., NDAs under authority levels, low‑risk vendor renewals) and what must escalate.
- Source‑of‑truth content: Lock approved playbooks, clause libraries, and policies so AI outputs only from vetted materials.
- Human‑in‑the‑loop: Require review for non‑templated third‑party paper, red flags (data transfers, indemnities), or novel issues.
- Audit trail: Capture who decided what, when, and why — including the playbook reference or exception rationale.
- Privacy and security: Keep sensitive artifacts in a secure workspace with data residency, access logging, and retention controls.
With these controls in place, AI becomes an accelerator, not a risk vector.
Metrics That Matter: Make the Case and Maintain Momentum
Track a small set of KPIs to prove value and guide iteration:
- Time to first response: From submission to acknowledgment. Target minutes, not days, for standard requests.
- Auto‑resolution rate: Percentage of requests fully resolved without human touch.
- SLA adherence: Percentage of requests completed within agreed timelines by type and risk.
- Rework rate: Requests that bounce back due to missing info or policy misalignment.
- Knowledge reuse: How often prior positions, clauses, or answers are referenced in new matters.
Dashboards that segment by request type, business unit, and region help you spot bottlenecks and expand automation where it’s safe.
A One‑Week Triage Sprint You Can Run Now
If you do one thing after reading this, run a focused triage sprint:
Day 1: Inventory your top five request types (volume x risk). Pull 20 recent examples of each.
Day 2: Define minimum required fields, playbook references, and routing rules per type.
Day 3: Configure smart intake forms; connect CRM/procurement for auto‑enrichment.
Day 4: Add AI classification and draft responses for low‑risk, templated cases. Set guardrails.
Day 5: Launch to one business unit. Track time to first response and auto‑resolution. Iterate weekly.
Platforms like Sandstone make this practical: modular forms, an embedded knowledge layer, AI agents aligned to your playbooks, and natural integration with Slack, email, and your ticketing system. You get crafted precision without forcing the business to change how it works.
The Bedrock of Speed, Alignment, and Trust
Intake and triage are where legal proves its operating leverage. When every request captures context, applies the right playbook, and feeds a living knowledge layer, legal stops being a bottleneck and becomes connective tissue. That’s the promise of a layered, AI‑powered legal ops foundation: faster answers, fewer surprises, and decisions that get better with every matter.
Build that foundation once, and it will keep compounding — with Sandstone turning daily work into durable knowledge you can trust at the heart of the business.