Why Automated Legal Intake and Triage Matter More Than Ever

More than half of inbound legal requests hit counsel via email, Slack, or ticketing tools without the context needed to act. That chaos doesn’t just slow work—it hides risk, breaks knowledge chains, and burns credibility with the business. The shift is clear: intake and triage must move from ad hoc to automated, from inboxes to an operating system that captures knowledge as it flows.

The Hidden Cost Of Unstructured Intake

Unstructured intake creates four compounding problems:

Delay: Attorneys chase context (Who’s the counterparty? What’s the deadline? Which template?). Work waits while stakeholders wait.
Rework: Missing details force do-overs and side-channel approvals. Cycle time doubles.
Lost knowledge: Decisions live in DMs and stale email threads, not in playbooks or searchable history.
Invisible demand: Leadership can’t see volume, mix, or bottlenecks, making headcount and vendor decisions guesswork.

For mid-sized to enterprise teams, these costs are material. Each request asks the same foundational questions; each response repeats tribal knowledge. Automating intake and triage doesn’t replace judgment—it preserves it, routes it, and applies it consistently. Like its namesake, Sandstone layers data—requestor, matter type, risk flags—so each decision strengthens the next.

What “Good” Intake And Triage Look Like

High-functioning teams standardize the front door without adding friction. The pattern is simple:

A single entry point: A form or chat workflow meets users where they are (Gmail, Slack, Jira, Salesforce) and collects the right fields based on request type.
Dynamic playbooks: Rules map matter type to the right template, fallback positions, and required approvals. A vendor NDA with no data sharing routes differently than a strategic partnership.
Smart triage: Low-risk, policy-aligned requests go straight to fast paths; edge cases escalate with a complete dossier for counsel.
Closed-loop decisions: Approvals, redlines, and rationale are captured in the same thread, auto-filed to the matter, and reusable next time.

On Sandstone, this looks like modular workflows: layered questions only when needed, playbook-linked clauses, and pre-set escalation paths. Tools are carved to fit each team’s contours, not the other way around.

Where AI Agents Fit—And What They Actually Do

AI shines in the “glue work” that steals attorney time but follows patterns. In intake and triage, agents can:

Classify: Detect matter type from free text and attachments, then trigger the right workflow.
Extract: Pull parties, dates, governing law, data flows, and unusual terms from contracts and requests.
Recommend: Propose the right template and clause set based on risk posture, industry, and past decisions.
Triage: Compare terms to playbook thresholds and route accordingly—auto-approve, request edits, or escalate with a concise risk memo.
Learn: Feed each decision back into the knowledge layer so future recommendations get sharper.

Critically, guardrails matter. Sandstone binds agents to your playbooks, positions, and approval matrix—no freewheeling suggestions, just policy-aligned assistance. When confidence dips or a rule trips, the system surfaces a clean packet to the right reviewer: context, diffs, and a one-paragraph summary. Natural integration means this happens inside the tools your business already uses.

Metrics That Prove It’s Working

Automation is only as good as the outcomes it drives. Track a handful of KPIs to validate and tune:

Cycle time by matter type (median and 90th percentile)
Percent auto-resolved vs. escalated
First-time data completeness at intake
Rework rate (number of back-and-forth cycles)
Playbook adherence (exceptions granted and why)
Internal NPS or CSAT from requestors

With these, you can answer leadership’s hardest questions: Where are we bottlenecked? What’s safe to self-serve? Which playbooks need refinement? Over time, the system becomes self-reinforcing: better data at intake drives faster routing; faster routing produces clearer outcomes; clearer outcomes harden playbooks.

A Practical Next Step: Pilot An NDA Intake Bot

If you need a fast, low-risk win, start with NDAs—the quintessential high-volume, low-variability workflow.

Scope: Limit to mutual NDAs under your standard template and thresholds (e.g., no PII sharing, 2-year term).
Build: In Sandstone, configure a chat-based intake in Slack or a short form. Attach your NDA playbook, fallback positions, and escalation rules.
Automate: Let an AI agent classify requests, extract parties/terms, and compare counterparty edits against playbook. Auto-approve when safe; escalate with a one-pager when not.
Measure: Target a 50%+ auto-resolution rate and a 40%+ reduction in median cycle time within four weeks. Tune rules weekly based on exceptions.

Actionable takeaway: Stand up an NDA intake pilot in two weeks; use the results to blueprint intake and triage for your next two high-volume workflows.

When intake and triage become a living system—not a set of inboxes—legal shifts from reactive support to a proactive force for speed, alignment, and trust. That’s the promise of a modern legal ops foundation: strength through layers, crafted precision, and natural integration. Sandstone turns the knowledge you already have into a durable operating system—so every request makes the next one faster, safer, and easier to say “yes” to.