Rethinking Legal Intake: How AI Triage Delivers Speed and Control
More than half of in-house legal leaders rank intake and triage as their top operational bottleneck. Not headcount—handoffs. When requests arrive across email, chat, and tickets without context, your best lawyers become air-traffic controllers. Fix intake, and you unlock speed, visibility, and trust across the business.
At Sandstone, we see teams reclaim 20–40% cycle time by standardizing intake and layering AI-driven triage on top of living playbooks. The result: fewer back-and-forths, faster routing, clearer decisions—and a knowledge base that compounds with every matter.
The Real Bottleneck Is Triage, Not Volume
Most teams don’t have a volume problem; they have a signal problem. Requests show up unstructured: “Need a quick review,” “Can you look at this vendor?” Without the right metadata—deal value, counterparty, jurisdiction, deadlines, template usage—legal can’t prioritize, so everything slows down.
Symptoms look familiar:
- Ambiguous ownership, resulting in multiple touches before landing with the right specialist.
- Rework and context-chasing that burn senior time.
- SLA drift and opaque queues that frustrate business partners.
The fix isn’t more forms for the sake of forms. It’s a layered model that meets requesters where they already work, captures the minimum viable context, and turns that context into automated decisions.
What Good Looks Like: A Layered Intake Model
High-performing teams use three layers:
1) Capture: A single, accessible front door—email alias, Slack/Teams app, or web form—mapped to the same schema. Ask only what you need (e.g., contract type, dollar value, deadline, template used, data access).
2) Classify and Route: Convert inputs into matter type, risk tier, and policy thresholds. Route by expertise and SLA. Low-risk paths should skip queues entirely.
3) Resolve and Record: Apply playbooks and positions to drive consistent outcomes. Every decision—accepted, redlined, escalated—writes back to the knowledge layer so the next request is faster and smarter.
This layered approach plays to Sandstone’s strengths—strength through layers, crafted precision, and natural integration—so legal operates as connective tissue, not a gate.
Where AI Agents Fit: Practical Automations You Can Ship
AI in legal ops shouldn’t be a moonshot. It should be a set of reliable agents that execute narrow, high-value tasks inside your workflow:
- Classification: Parse a request and label matter type, jurisdiction, data sensitivity, and business unit.
- Metadata Extraction: Pull key fields from attachments (counterparty, term, governing law, renewal windows) and populate the matter record.
- Policy Routing: Auto-route under defined thresholds (e.g., NDAs under $X or standard DPAs) to fast paths or auto-approval.
- Drafting and Redlines: Generate first-pass NDAs/SOWs from living playbooks; propose redlines aligned to your positions.
- Context Requests: If required fields are missing, the agent asks the requester for what’s needed—without lawyer intervention.
- Status Updates: Push Slack/Teams notifications as the matter advances; no more “any update?” pings.
- Risk Flags: Escalate out-of-bounds clauses or terms to a specialist with rationale and suggested alternatives.
On Sandstone, these agents run on top of your centralized playbooks and decision history. Each intake strengthens the foundation: what the agent learns today becomes tomorrow’s default.
Metrics That Matter: Prove the Win in 30 Days
Executives fund what they can measure. Track these KPIs to show impact quickly:
- Time-to-Triage: Minutes from submission to routed (or auto-resolved) state.
- Cycle Time by Matter Type: Intake to signature/recommendation.
- Auto-Resolve Rate: Percentage of requests closed without attorney touch under approved thresholds.
- First-Touch Right Routing: Requests assigned correctly on first attempt.
- Rework Rate: Number of times a matter is sent back for missing info.
- Requester CSAT: Quick “Was this fast and clear?” rating on closure.
- Knowledge Capture: Percentage of matters that update playbooks/positions.
Across deployments, we’ve seen teams move NDAs from days to hours and cut triage times from hours to minutes once auto-classification and policy routing are live.
How to Start—In a Week
You don’t need a full transformation to see gains. Start small, ship fast, iterate.
1) Map the Top Five Request Types: NDAs, vendor DPAs, SOWs, marketing reviews, product counsel questions.
2) Define Minimum Required Fields: 6–8 signals that drive routing (deal value, counterparty, template, data access, deadline, region).
3) Encode One Playbook per Type: Your non-negotiables, safe positions, and fallback terms.
4) Turn On Two Agents: Classification and context-collection. Route low-risk paths to auto-approval or templatized self-serve.
5) Instrument KPIs: Baseline this week; review deltas after two weeks.
Actionable next step: Stand up a single intake front door and pilot auto-classification on one request type. Measure time-to-triage and auto-resolve rate. If both improve, expand to routing and drafting.
The Payoff: A Stronger Foundation with Every Request
When intake becomes a living, AI-powered operating system, legal shifts from reactive to proactive. Work flows to the right place. Positions stay consistent. Knowledge compounds instead of disappearing. The business gets answers faster—and trusts legal more.
This is the promise of Sandstone: a modern legal ops platform and knowledge layer where every intake, triage, and decision strengthens your foundation. Build the layers now, and let AI carry the load. If you’re ready to see intake become a growth accelerant, we’d love to show you how a two-week pilot can change your queue—and your credibility.