Streamlining Legal Intake: Why AI-Driven Triage Powers Faster, Safer Decisions

If you cannot see the queue, you cannot manage the risk. Knowledge workers spend nearly a day each week searching for information, and in-house legal feels this most at the front door: intake. When every request is a one-off email, Slack DM, or hallway ping, velocity stalls and risk hides in the gaps.

What Is Broken in Legal Intake

Most teams face the same pattern: high volume, mixed channels, and no consistent metadata. A vendor NDA looks just like a product launch review inside an inbox. Without structure:

- Priority is arbitrary instead of risk-based.

- Work bounces between owners and stalls in review loops.

- Cycle time and SLA reporting are impossible.

- Institutional knowledge walks out the door in fragmented threads.

The result is not just slower service. It is avoidable exposure: missed obligations, approval bypasses, and decisions made without the right facts. Intake chaos becomes operational debt.

Why AI-Driven Triage Changes the Equation

AI turns unstructured requests into structured matters the moment they arrive. Instead of forcing the business to adapt, AI meets users where they are and quietly adds the missing layer of order.

Here is what that looks like:

- Entity and intent detection: extract parties, dates, amounts, jurisdictions, and ask type from email or chat text.

- Dynamic follow-ups: if a counterparty is missing, the system asks for it, not a person. If a contract is attached, it labels type and risk drivers.

- Policy gating: route privacy, security, or export control triggers to the right reviewers automatically.

- Risk scoring and routing: apply playbooks to set priority, owner, and SLA based on business context and thresholds.

- Audit trail: every decision and handoff is captured, searchable, and reportable.

The payoff is clarity. Legal gets a single, ordered queue. The business gets faster answers with less back-and-forth. And leadership gets visibility that supports capacity planning and defensible governance.

How It Runs on Sandstone

Sandstone is the knowledge layer and operating system under your intake. It blends into how your team already works, then adds layered structure that compounds over time.

- Unify channels: connect a simple request portal, shared inbox, and Slack or Teams. The entire card is clickable; if a user starts in chat, Sandstone still creates a structured matter behind the scenes.

- Normalize data: AI agents classify request type, extract key fields, and tag risk signals. No more free-text tickets.

- Apply playbooks: your positions and policies become machine-actionable rules. For standard NDAs, Sandstone can auto-approve or return a pre-negotiated template with tracked changes. For higher-risk items, it auto-assigns owners and adds required reviewers from privacy, security, or finance.

- Orchestrate handoffs: two-way integrations keep work moving in the systems teams already love, whether that is CLM, Jira, Salesforce, or procurement. Status changes sync everywhere.

- Learn by doing: every intake, triage, and decision trains the layer. Next week’s work is faster because last week’s work is now metadata.

This is strength through layers: each request adds context; each decision sharpens the model; each workflow becomes a repeatable asset. Crafted precision ensures the rules match your contours, not generic defaults. Natural integration means the business keeps its rhythm while legal gains control.

What To Measure and the ROI

Operationalize AI triage with metrics that matter to the business:

- Time to first response: target minutes, not days.

- Auto-resolve rate for low-risk requests: raise the floor on velocity.

- Cycle time by category: surface bottlenecks by contract type, product area, or region.

- Rework and bounce rate: fewer handoffs equals fewer errors.

- SLA attainment by risk tier: prove control where it counts most.

- Intake-to-outcome traceability: show how requests become decisions, approvals, or signed contracts.

Beyond speed, the compounding value is visibility. With normalized intake data, you can forecast demand, set capacity plans, and negotiate better with outside counsel. You can show which work should be automated, which belongs with the business under playbooks, and which merits deep legal expertise.

Try This 30-Day Pilot

Pick one high-volume, repeatable flow and prove the model before you scale. For most teams, start with NDAs, vendor due diligence, or marketing review.

- Week 1: Map your current intake sources and define a minimal form plus email and Slack capture. List required data fields and owners.

- Week 2: Codify the playbook. Define risk tiers, auto-approve thresholds, fallback rules, and SLAs. Connect CLM or document templates if needed.

- Week 3: Run in shadow mode. Let Sandstone AI triage in parallel while the team works as usual. Compare routing, response times, and outcomes.

- Week 4: Turn on assisted mode. Allow AI to auto-populate fields and propose actions; humans confirm. Enable auto-resolve for the safest tier.

- Retro: Publish results. Lock in the workflow, then add the next request type.

Actionable takeaway: schedule a 45-minute working session this week to choose the pilot flow, owners, and success metrics, and stand up the intake connectors. Momentum beats perfection.

The Bottom Line

When intake becomes a living system, legal stops being a bottleneck and starts being a force multiplier. Structured, AI-driven triage turns scattered requests into reliable throughput, with the transparency the business expects and the control legal requires. That is the foundation Sandstone delivers: layered data, crafted workflows, and natural integration that scales with your growth. Build the bedrock once, and let every new request make it stronger.