How to Run Legal Intake Like a Product Queue: The Key to Speed, Visibility, and Trust

How to Run Legal Intake Like a Product Queue: The Key to Speed, Visibility, and Trust


_Transform unstructured requests into a living, AI-powered workflow that speeds cycle time, clarifies ownership, and compounds institutional knowledge._

Across in-house teams, 60–80% of legal requests arrive unstructured via email or chat. That noise creates delays, shadow queues, and frustrated partners. The fix isn’t more forms—it’s a product-style intake queue that routes, prioritizes, and learns.

Sandstone turns intake into an operating system: AI agents read requests, map them to playbooks, and triage with crafted precision. Legal shifts from reactive tickets to an orchestrated service that moves the business with clarity and confidence.

What Is “Legal Intake as a Product Queue”?

Think of every request as a backlog item. Your intake isn’t a shared inbox; it’s a prioritized, status-driven queue with clear owners, SLAs, and definitions of done. AI classifies the work, applies policy, and surfaces the next best action.

Example: A sales rep drops a redline in Slack. Sandstone’s AI parses the counterparty name, detects the template, checks playbook thresholds, and auto-assigns to the right owner with a due date. Routine clauses are auto-suggested. Exceptions are flagged before human review. Nothing slips through; everything moves.

Why It Matters Now for In‑House Legal

- Speed without chaos: Shrink cycle time by standardizing intake, triage, and next steps.

- Visibility that drives decisions: See volume, aging, and risk hotspots across lines of business.

- Ownership at every step: Clear DRI, SLA, and “definition of done” reduce back-and-forth.

- Risk managed by design: Playbooks and thresholds apply consistently—no heroics required.

- Knowledge that compounds: Each decision strengthens the knowledge layer for future automation.

When intake becomes a product queue, you trade ad hoc scrambles for predictable throughput, trust, and defensibility.

How to Get Started in Sandstone

1) Map intake to ownership

- Define the few sources you’ll support first (e.g., Sales, Procurement, Marketing). Create simple, channel-native entry points—Slack app, email alias, or a lightweight portal.

2) Instrument your workflow

- Standardize request types (NDA, MSA, Privacy Review, Vendor Paper) and statuses (New, In Triage, In Review, With Counterparty, Blocked, Done). Set SLAs by type and risk.

3) Codify playbooks into AI prompts

- Translate positions and thresholds into Sandstone’s AI agents. Example: “If vendor DPIA risk > medium, route to Privacy; suggest fallback clauses X/Y; block signature if unresolved.”

4) Automate triage and enrichment

- Auto-extract metadata from files, parse counterparty details, link records to CRM or ERP, and pre-fill forms. The agent proposes an initial path; humans confirm or override.

5) Close the loop with metrics

- Dashboards show inflow, cycle time by type, SLA attainment, and exception trends. Weekly reviews refine playbooks and reduce rework.

Start lean—three request types, two playbooks, one SLA policy—and expand as signal strengthens.

Best Practices

- Meet users where they work: Add a Slack “Create Legal Request” shortcut; don’t force new behavior.

- Define “good intake”: Required fields, attachments, and business context that enable first-pass triage.

- Set clear thresholds: What can ship on rails, what needs counsel, what stops the line.

- Use queue hygiene: Daily triage, aging alerts, and a published SLA policy.

- Treat exceptions as product feedback: Update playbooks so the same fire never burns twice.

Metrics That Matter

- Intake-to-first-touch: Time from submission to initial triage decision.

- Cycle time by request type: Start-to-finish duration, including counterparty waits.

- SLA attainment: Percentage of matters meeting defined response and resolution SLAs.

- Reopen rate: Matters reopened after “Done”—a proxy for quality and clarity.

- Exception rate: Percentage of requests that deviate from playbooks; target downward trend.

- Automation coverage: Share of requests fully or partially handled by AI agents.

Example: From Slack Chaos to Reliable Cycle Time

Before: A 600-person SaaS company handled all commercial requests in an email alias. Average turnaround was 9 business days, with no visibility into bottlenecks. Sales escalated in DMs; Legal lived in inboxes.

After Sandstone: Sales submits via a Slack shortcut. AI classifies the request, enriches context from Salesforce, and applies the Commercial Playbook. Low-risk NDAs auto-complete; MSAs route with clause suggestions and a two-day SLA. Weekly dashboards show aging by owner and exception hotspots.

Results in 90 days: 42% faster cycle time, 18-point increase in internal NPS, and a 35% drop in exceptions due to clarified thresholds. Trust went up because expectations were visible—and met.

One Actionable Next Step

Pick one high-volume request type (e.g., NDA) and ship a “rails” flow:

- One entry point (Slack shortcut or email alias).

- One playbook with auto-suggested clauses.

- One SLA everyone can remember (same-day triage, two-day turnaround).

Prove the win, then scale to MSAs and vendor reviews.

Build on Bedrock—not Bottlenecks

When intake runs like a product queue, Legal stops being the team of last resort and becomes the connective tissue of the business. Sandstone layers your playbooks, positions, and workflows into an AI-powered operating system where every request strengthens the foundation.

That’s how you get speed, visibility, and trust at scale—crafted to fit your process, and natural to how your teams already work.