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How to Automate Legal Intake Without Losing Control

Most legal intake isn’t novel—it's repeatable. Here’s a pragmatic blueprint to automate requests with AI agents, keep governance tight, and cut cycle time without ripping and replacing your stack.

Jarryd Strydom

September 11, 2025
Legal intake is where work begins—and where time disappears. Across many in-house teams, 40–60% of inbound requests can be resolved without a lawyer drafting from scratch if playbooks are operationalized. The gap isn’t intent; it’s execution. Email threads, missing context, and undocumented decisions create drag.
This is where AI-powered intake becomes a force multiplier: not to bypass judgment, but to route, answer, and draft within the guardrails legal sets.
Why Intake Breaks (and How AI Fixes It)
Intake typically fails for three reasons: ambiguous channels, inconsistent context collection, and tribal knowledge locked in heads or docs. The result: slow triage, rework, and frustrated stakeholders.
An AI intake agent repairs the basics by:
  • Meeting requestors where they are (Slack, email, ticketing) and standardizing required context.
  • Applying your positions and playbooks to propose next actions or draft first-pass documents.
  • Routing by risk and workflow, with clear SLAs and escalation thresholds.
  • Learning from outcomes so decisions compound instead of disappearing.
You don’t need a monolithic CLM overhaul to start. Connect intake to your existing tools: templates, e-sign, matter systems, and procurement workflows. Modern, modular beats heavy and slow.

What to Automate First: High-Signal, Low-Risk Workflows

Start where volume is high and decisions are well-understood:
  • NDAs and low-risk contracts: Auto-generate from approved templates; auto-approve within guardrails; route edge cases.
  • Vendor and procurement intake: Capture data once, map to your data processing and security standards, propose redlines on common clauses.
  • Marketing and sales enablement requests: Answer policy FAQs, provide pre-approved language, escalate only when facts or channels change.
  • Privacy reviews: Triage by data type and geography; trigger standard DPAs; flag unusual processing for counsel.
With Sandstone, an intake agent collects context, applies layered playbooks, and drafts in your tone. It’s crafted precision: your positions, your templates, your routes.

Guardrails That Keep You in Control

Automation without control is a liability. Put governance at the center:
  • Permissions and roles: Define who can approve, who can edit language, and when escalation is mandatory.
  • Data boundaries: Keep PII out of prompts by default; log what’s processed; mask sensitive fields.
  • Audit trails: Every decision, draft, and approval is recorded and searchable.
  • Thresholds: Set risk-based triggers (e.g., non-standard indemnity, sensitive data, non-US law) to stop automation and route to counsel.
  • Shadow mode: Let the agent propose but not send for two weeks; compare accuracy before you turn it on.
This is Sandstone’s strength-through-layers approach: policy, workflow, and data stacked to create reliable outcomes.

The Metrics That Matter

Track adoption and impact, not vanity:
  • Time-to-first-response: From request to acknowledgment. Target minutes, not hours.
  • Auto-resolution rate: % of requests closed without attorney drafting. Start at 20–30%; climb as playbooks mature.
  • Triage accuracy: % routed correctly on the first pass. >95% is achievable with structured intake.
  • Escalation mix: Are escalations driven by real risk or missing context? Reduce the latter.
  • Requestor satisfaction: A quick 1–5 pulse post-close. Watch for improvements as cycle time drops.
Dashboards should show where knowledge is missing so you can refine playbooks and expand automation coverage.
A 30-Day Plan to Pilot AI Intake
Week 1 — Inventory and Design
  • List top 10 request types by volume and cycle time.
  • For the top three, extract the decision trees: mandatory fields, standard positions, red flags, fallbacks.
  • Define SLAs and escalation triggers.
Week 2 — Connect and Configure
  • Plug intake into Slack/email; map to your matter or ticketing system.
  • Load templates and clause libraries; tag with risk metadata.
  • Encode playbooks as decision logic and positions in Sandstone.
Week 3 — Shadow Mode
  • Let the agent collect context and propose drafts/resolutions; humans send.
  • Measure triage accuracy and draft quality; tune prompts and rules.
Week 4 — Controlled Go-Live
  • Turn on auto-resolve for one workflow (e.g., mutual NDA) with tight thresholds.
  • Communicate SLAs and channels to business teams; publish a short “how to request legal help” guide.
  • Review metrics weekly; add one new workflow every two weeks.

Practical Next Step

Pick one workflow—mutual NDAs—and stand up an intake agent that: asks five mandatory questions, generates the right template, and auto-approves if terms are standard. Set an escalation for any non-standard governing law or unilateral NDA. Measure time-to-first-response and auto-resolution for 14 days, then expand.

The Payoff: Faster Cycles, Stronger Trust

When intake runs on a living knowledge layer, legal stops being a bottleneck and becomes connective tissue. Every request strengthens your foundation: positions get sharper, routes get smarter, and decisions compound instead of disappearing.
Sandstone was built for this moment. Layered data, modular workflows, and natural integrations turn playbooks into practice—safely and fast. Start with intake, prove value in weeks, and scale across vendor, privacy, and commercial flows. That’s how legal accelerates the business with clarity and confidence—and how you build an operational bedrock for growth.

About Jarryd Strydom

Jarryd Strydom is a contributor to the Sandstone blog.