Building Your Legal Front Door: AI Intake and Triage as the New 'Must-Have'

Here’s the quiet killer: 60–70% of in-house legal work starts with an unstructured request in email or chat. Teams burn 30–40% of cycle time clarifying basics—counterparty, deal size, data flows—before any substantive review begins. Multiply that by hundreds of requests and you get expensive latency, uneven risk decisions, and a perception that legal is the bottleneck. The fix isn’t hiring another coordinator; it’s building a reliable front door and letting AI handle the first mile.

Intake Is the Bottleneck You Can Actually Fix

Most legal delays aren’t about law; they’re about missing context. Who’s the counterparty? Which template? Is this under an MSA? Where is data stored? Without a consistent entry point, intake fragments across forms, inboxes, and DMs. Work disappears. SLAs slip. Knowledge doesn’t accumulate; it evaporates.

A legal front door centralizes how work enters the team: a single, accessible path that asks the right questions once, applies the right playbook, and routes to the right person. On Sandstone, that front door is more than a form. It’s a living, AI-powered operating layer that captures structured data, references playbooks and positions, and records every decision so the system gets smarter. Your request volume becomes signal, not noise.

AI Intake and Triage: What ‘Good’ Looks Like

An effective AI front door does five things out of the box:

- Structured capture: Turns messy messages and attachments into clean fields (counterparty, value, data types, regions, timelines) without making business users feel like they’re filing a tax return.

- Policy lookup: Maps the request to current playbooks and positions—preferred clauses, fallback terms, approval thresholds—so answers are consistent.

- Classification and routing: Identifies request type, risk level, and owner, then assigns with SLA and context, not just a name in a queue.

- Drafting and enrichment: Proposes markup, clause selections, and checklists based on your standards and the request’s facts.

- Decision memory: Logs outcomes and exceptions so the next similar request moves faster (and the system can flag drift from policy).

With Sandstone, AI agents handle this first-mile orchestration natively—capturing layered data, applying crafted workflows, and integrating naturally with Slack, email, CLM, CRM, and procurement tools you already use.

Playbook in Action: Commercial Contracts

Consider the most common fire drill: a sales Slack DM asking for a “quick review.” Here’s how an AI agent on Sandstone turns it into a predictable flow:

1) Intake bot in Slack gathers essentials (counterparty, value, region, data flows) and extracts terms from attachments.

2) The agent checks your positions: template availability, risk thresholds, required approvals (e.g., data processing addendum, security review).

3) It classifies the request (new MSA + DPA; medium risk), assigns to the right owner, and sets SLA based on queue and business priority.

4) It drafts initial redlines or a clause pack aligned to your playbook, annotating rationale for each position.

5) It opens the matter in CLM, updates CRM or intake system with status, and creates tasks for approvers where needed.

6) It logs decisions and exceptions (e.g., allowed a 30-day limitation tweak with CFO approval) to the knowledge layer for future reuse.

The result: less back-and-forth, faster drafts, consistent risk posture, and a complete audit trail without extra clicks.

Prove It with KPIs That Matter

If you can’t measure it, you can’t scale it. Start with:

- First-response time: Time from request to acknowledgment with clear next steps.

- Time-to-triage: Time to complete intake, classification, and routing.

- Cycle time by request type: Days from intake to signature or resolution.

- Rework loops: Number of clarification touches before substantive review starts.

- Playbook adherence: Percent of requests resolved within standard positions; exceptions by reason and approver.

- Business satisfaction: Simple post-matter CSAT—did legal accelerate or delay the deal?

Sandstone surfaces these metrics natively because every intake, triage, and decision is captured as structured data. Knowledge compounds instead of disappearing.

A Practical Rollout You Can Do in Weeks

You don’t need a big-bang transformation. Sequence by value and risk:

1) Baseline: Audit two weeks of inbound requests across channels. Quantify volume, rework loops, and time-to-triage.

2) Narrow the scope: Choose one high-volume workflow (e.g., NDAs, low-risk SOWs, vendor DPAs).

3) Encode the playbook: Translate your preferred, fallback, and forbidden positions into Sandstone’s knowledge layer.

4) Wire the front door: Enable Slack/email intake and set auto-questions for missing context. Turn on AI classification and routing.

5) Pilot and iterate: Run for 30 days with real users. Track KPIs. Tune prompts and thresholds. Expand to the next workflow.

Actionable next step: Run a 10-day intake baseline. Capture three numbers—percent of requests missing required info, average clarification touches, and average time-to-triage. Those numbers will pinpoint where an AI agent should start and give you a before/after story for leadership.

When legal becomes the front door instead of the hallway, the business moves with clarity. Sandstone makes that shift durable: strength through layers of data, workflows carved to fit your process, and integrations that feel natural. With an AI-powered intake and triage foundation, legal stops firefighting and starts compounding knowledge—the bedrock for speed, alignment, and trust at the heart of the business.