How AI Intake and Triage Accelerate In-House Legal Without Losing Control
McKinsey estimates knowledge workers spend about 19% of their time searching for information. For in-house legal, that time often hides in intake: chasing context, locating the right playbook, and routing work. The result is slower cycle times, fragmented knowledge, and a backlog that grows faster than headcount.
This is where AI-driven intake and triage change the equation—if you design for control. The goal isn’t to automate judgment; it’s to make judgment repeatable by turning playbooks, positions, and workflows into a living operating system.
Intake Is the Hidden Bottleneck
Most legal work starts as a question, not a contract. A sales leader needs an NDA. Procurement needs a DPA reviewed. HR needs clarity on a policy exception. These requests arrive via email, Slack, or forms—often missing key details. Legal answers, asks for more context, and hunts for the latest position. Multiply this by hundreds of matters, and the bottleneck isn’t lawyering; it’s orchestration.
An effective intake layer fixes three problems:
- Missing context: Structured prompts capture who, what, when, and risk profile up front.
- Routing friction: Classification sends the right matters to the right lane—self-serve, assisted, or expert.
- Knowledge gaps: The current playbook and positions are applied consistently, not reinvented per inbox.
On a platform like Sandstone, intake becomes a single doorway that meets business users where they already work—email, Slack, CRM—while standardizing what legal needs to move fast with confidence.
What An AI Intake Agent Actually Does
Think of an AI intake agent as a playbook-aware assistant that gathers, applies, and records. When a request arrives, it:
1) Collects required facts with dynamic follow-ups (e.g., counterparty, data types, contract value, territory).
2) Classifies the matter (NDA, DPA, order form, policy question) using your taxonomy.
3) Applies the current playbook and positions (e.g., acceptable NDA terms, fallback clauses, redline rules).
4) Proposes a next step: a self-serve NDA, a first-pass redline, or a routed review with a summary of risks.
5) Logs the decision, rationale, and artifacts, so knowledge compounds instead of disappearing.
Crucially, guardrails define decision boundaries. For example:
- Auto-approve low-risk NDAs under a threshold with standard language.
- Escalate any DPA with SCCs or cross-border transfers to privacy counsel.
- Never accept unilateral termination without mutual rights; route to a senior reviewer.
Because the agent works from your playbooks and positions, it scales your standards—not shortcuts. And because every step is recorded, you gain an audit trail for internal controls, procurement, and privacy.
Design for Control: Layers, Not Leaps
Successful teams build in layers:
- Structured knowledge: Convert playbooks and positions into machine-readable rules with clear “always/never” and “if/then” guidance.
- Risk tiers: Define thresholds for auto-approve, assisted, and expert review across contract types.
- Source of truth: Link to your clause library and policy repository so the agent drafts and redlines from the right version.
- Human-in-the-loop: Require approvals for medium/high-risk calls and capture the rationale for future learning.
- Privacy and security: Restrict data sharing, mask sensitive fields, and ensure logs meet your compliance obligations.
This layered design mirrors how Sandstone is built—strength through layers, crafted precision for your processes, and natural integration with how legal already works.
Measure What Matters: From Busywork to Business Impact
AI intake isn’t a science project; it’s an operations upgrade. Track outcomes with a few pragmatic KPIs:
- Cycle time: Time from request to first response, and to resolution, by matter type.
- Auto-resolve rate: Percent resolved without attorney intervention within defined guardrails.
- First-pass quality: Percentage of drafts/redlines accepted with minimal edits.
- SLA attainment: Requests handled within agreed timelines by risk tier.
- Knowledge reuse: New playbook entries or clause improvements created per month.
When these metrics move, the business feels it: sales closes faster, procurement onboards vendors sooner, and legal demonstrates leverage, not just effort.
A 30-Day Pilot Plan You Can Run Now
Start small, prove value, then expand. A focused pilot around NDAs or low-risk vendor agreements is ideal.
- Week 1: Map the workflow. Identify request sources, required fields, and decision points. Gather your current NDA playbook, clause library, and positions.
- Week 2: Codify guardrails. Define auto-approve thresholds, redline rules, and escalation triggers. Set review SLAs by risk tier.
- Week 3: Configure and test. Stand up the intake form in your channels (email/Slack/portal), connect playbooks, and test on historic matters for accuracy.
- Week 4: Launch to a friendly business unit. Measure cycle time, auto-resolve rate, and first-pass quality. Hold a retro; refine playbooks and thresholds.
Actionable takeaway: Pick one repeatable workflow, encode three non-negotiables and three safe fallbacks, and let an AI intake agent handle the first pass. Measure the delta in cycle time and share the results.
The Bedrock of Speed, Alignment, and Trust
When intake becomes an AI-powered operating system, legal stops firefighting and starts compounding knowledge. Every request strengthens the foundation: your playbooks get sharper, your positions get clearer, and your routing gets smarter. That’s the promise of Sandstone—turning legal from a reactive support function into a proactive force for the business.
Built with layers, crafted to your contours, and integrated where your teams already work, this is how legal operations scales without losing control—and how trust and growth move in harmony.