How to Operationalize Legal Intake and Triage With AI Agents

Most in‑house teams discover that 60–70% of legal intake maps to just 10–15 repeatable patterns. Yet those same requests still arrive as scattered emails, Slack pings, and ambiguous “quick questions.” The result: context switching, slow cycle times, and inconsistent answers.

There’s a better path. By turning playbooks, positions, and workflows into a living knowledge layer—and letting AI agents handle the first mile—you can route, resolve, and record routine matters at scale. Legal stops being a bottleneck and becomes the connective tissue across the business.

Start With the Workflow You Already Have

Before you buy tools or design automations, map reality:

- Top request types: NDAs, vendor reviews, marketing approvals, policy questions, contract amendments

- Request channels: email, Slack/Teams, ticketing systems, CRM, procurement portal

- Decision points: what data you need to accept, how you classify risk, when to escalate, who approves

- Service levels: time to first response, resolution targets by category and risk tier

- Data you wish you had: entity names, contract value, governing law, data-sharing flags, counterparty paper

Capture this as a lightweight decision tree for your top two request types. The goal is clarity, not perfection. You’re defining the contours of the work so AI can take the first pass and humans can focus on judgment calls.

Turn Playbooks Into Structured, Layered Knowledge

Playbooks are often PDFs or wikis nobody opens under pressure. Operationalize them:

- Canonical positions: preferred, acceptable, and fallback stances with rationale

- Clause and fallback libraries: tied to each position and risk tier

- Risk taxonomy: low/medium/high with clear triggers (e.g., data types, jurisdictions, contract value)

- Exceptions policy: who decides, how to document, and how exceptions feed back into the playbook

- Versioning and provenance: track what changed, why, and who approved

On Sandstone, these elements become your legal knowledge layer—structured, searchable, and executable. Each intake strengthens the foundation: decisions are recorded, exceptions inform the next version, and guidance stays current without manual digging.

Design AI‑Driven Intake, Classification, and Routing

With a structured playbook, AI agents can reliably handle the first mile:

1) Capture: Offer a single front door (web form, Slack app, or email alias) that requests the minimum viable data for each intake type.

2) Classify: The agent identifies request type and risk tier using plain‑English descriptors and your taxonomy.

3) Extract: Pull key entities from attachments or links (party names, term, governing law, data types, DPA presence).

4) Policy checks: Compare request against playbook positions; flag variances and propose next steps.

5) Route: Assign to the right queue/owner based on category, risk, and workload; set SLAs and due dates.

6) Respond: Provide a clear, templated response or a redlined document when risk is low and playbook coverage is high.

7) Record: Log all metadata, decisions, and outcomes back to your system of record for audit and reporting.

Example: NDA intake

- Low‑risk: Extract parties/term, confirm mutual NDA on company paper, auto‑approve with standardized routing and countersign. Cycle time: minutes.

- Medium‑risk: Generate redlines against sensitive disclosures or restrictive governing law; route to a contracts specialist with AI‑generated summary and risk notes.

- High‑risk: Escalate to counsel with a one‑page brief: issues, recommended positions, and suggested fallback language.

This is where “natural integration” matters. Sandstone’s agents meet requesters where they already work, update the ticketing system or CRM, and push approved language back to the CLM—without forcing the business to adopt a new ritual.

Measure What Matters and Tighten the Loop

Automation without measurement is just a demo. Define a small, durable KPI set:

- Time to first response by category and risk tier

- Median and 90th‑percentile cycle time by workflow

- Auto‑resolution rate (percentage resolved without attorney review)

- Playbook coverage (requests fully handled by current guidance)

- Rework rate (matters bounced between queues or reopened)

Create weekly reviews where the team inspects one metric and one workflow. When a request falls out of auto‑resolution, ask why: was the playbook unclear, the data missing, or the exception legitimate? Update the knowledge layer, not just the single document. Strength through layers comes from small, consistent improvements that compound.

A 30‑Day Pilot You Can Run Now

Pick one workflow (NDAs or low‑risk marketing reviews) and ship value in weeks:

- Week 1: Map the decision tree, define required fields, and agree on risk tiers and SLAs. Identify three common clauses and approved fallbacks.

- Week 2: Configure a single front door, structure the playbook in Sandstone, and wire basic routing to the right queue.

- Week 3: Enable AI extraction and classification; test on 10 real requests. Compare outputs against human decisions; tune thresholds.

- Week 4: Turn on auto‑responses for low‑risk cases; publish a simple dashboard for the KPIs above. Socialize the win with go‑to‑market and procurement partners.

Actionable takeaway: Stand up an NDA intake pilot this month using the steps above. If you can auto‑resolve even 30% of volume, you’ll free meaningful attorney capacity and create the data backbone for broader automation.

The Bedrock of Trusted, Scalable Legal Operations

When intake, playbooks, and workflows live as an AI‑powered operating system, legal shifts from reactive to proactive. Every request strengthens your foundation; knowledge compounds instead of disappearing into inboxes. The business gets faster answers with clearer guardrails. Legal gets time back for judgment‑heavy work.

That’s the promise of Sandstone: crafted precision that fits how your team already works, layered decisions that build on each other, and natural integration across the tools your business uses every day. Start with one workflow. Make it airtight. Then let the layers do the heavy lifting as you scale.