How To Turn Legal Intake Into an AI-Powered Triage Layer

Legal intake automation isn’t about chatbots replacing judgment; it’s about turning scattered asks into a consistent, auditable flow. Here’s how legal ops can deploy an AI-assisted triage layer that speeds response times, reduces rework, and builds a living knowledge base as you go.

The Intake Bottleneck You Can Actually Fix

Across multiple in-house benchmarks, legal teams report spending more than half their week on intake, triage, and status chasing—work that rarely requires deep legal analysis. Shared inboxes, Slack pings, and ad hoc forms create inconsistent data capture, murky SLAs (service level agreements), and avoidable delays.

The result: slow cycle times, duplicate reviews, and institutional knowledge that never compounds. Each request is a one-off instead of a decision that strengthens your foundation. The solution isn’t another portal no one uses. It’s a triage layer that:

Standardizes the questions you ask.
Routes work based on risk and complexity.
Applies your playbook automatically where possible.
Logs decisions so the next similar request is faster.

That’s the core job for an AI agent when paired with clear guardrails.

Define the Triage Model Before You Automate

AI amplifies structure; it does not replace it. Spend a few hours designing your triage model. Keep it lightweight and focused on what changes routing and risk.

Request types: e.g., NDA, vendor agreement, marketing claim, policy question, product counsel.
Required data: counterparty, value, region, data types involved, deadline, approvers.
Routing rules: low-risk NDAs auto-serve; anything involving personal data routes to privacy; >$250k to commercial counsel; regulated markets to specialist.
Playbook links: preferred positions, fallback clauses, escalation thresholds.
Decision log: capture rationale (why we approved/blocked), not just the outcome.

Document this as a living artifact. In Sandstone, these become modular workflows—layered data and decisions that build on each other rather than disappear in email.

Where AI Agents Add Real Value (Not Magic)

When your triage model exists, an AI agent can operate as connective tissue across tools you already use.

Classification and extraction: parse emails, forms, or tickets; identify request type; extract key fields (value, jurisdiction, data categories) with confidence scores.
Knowledge retrieval: check your playbooks, prior similar matters, and clause library to propose next steps that align with precedent.
Auto-responses: for truly low-risk asks (e.g., mutual NDA within thresholds), generate and send the pre-approved template with the right names and dates.
Smart routing: package the request with normalized data and the suggested path; post to your matter system or ticket queue; notify stakeholders.
Decision logging: append the reasoning and source materials, so every action strengthens the knowledge layer.

Because Sandstone is built to blend into existing workflows, this agent can read from shared inboxes, forms, or Slack, and write back to CLM (contract lifecycle management), ticketing, and docs—without forcing users to change their habits.

What to Measure: Four KPIs That Prove It’s Working

Measurement moves the conversation from anecdotes to outcomes.

First-response time: minutes from submission to acknowledgment or auto-resolution.
Cycle time by request type: median days from intake to closure; watch for variance by business unit.
Auto-resolution rate: percentage of requests fully handled without attorney intervention, gated by risk thresholds.
Rework rate: percentage of matters returned due to missing info; the triage form and agent should drive this down.

Add a simple requester satisfaction pulse (one-click CSAT) to corroborate the operational data.

A Safe Two-Week Pilot Plan

You don’t need a multi-quarter transformation. Prove value in one workflow, then expand.

Days 1–2: Select a single high-volume workflow (mutual NDA, marketing content review, or low-risk vendor reviews). Define success metrics and SLAs.
Days 3–5: Configure a minimal intake form and routing rules. Import the relevant playbook pages and templates.
Days 6–7: Connect systems (SSO, ticketing, CLM) and set guardrails (DLP, role-based access).
Days 8–10: Train the agent with 10–20 prior examples; validate extraction and suggested actions against counsel review.
Days 11–14: Go live with one business unit. Enable auto-resolve only for low-risk scenarios; everything else routes with suggested next steps. Hold a 30-minute daily standup to review edge cases.

By the end of week two, you should see faster acknowledgments, cleaner data capture, and a baseline auto-resolution rate you can confidently expand.

Actionable Next Step

Pick one workflow and draft a one-page triage model. If you can’t articulate routing and thresholds in plain language, you’re not ready for automation. Once you can, let an agent do the repetitive work—classification, extraction, and playbook application—while counsel focuses on exceptions.

The Bedrock of Trust and Growth

A scalable triage layer turns legal from a perceived bottleneck into the system that keeps the business moving with clarity. Every intake, classification, and decision enriches your knowledge layer, so the next request is faster and safer. That’s the Sandstone approach: strength through layers, crafted precision, and natural integration—an operating system where business and law move in harmony.

How To Turn Legal Intake Into an AI-Powered Triage Layer

The Intake Bottleneck You Can Actually Fix

Standardizes the questions you ask.
Routes work based on risk and complexity.
Applies your playbook automatically where possible.
Logs decisions so the next similar request is faster.

That’s the core job for an AI agent when paired with clear guardrails.

Define the Triage Model Before You Automate

AI amplifies structure; it does not replace it. Spend a few hours designing your triage model. Keep it lightweight and focused on what changes routing and risk.

Request types: e.g., NDA, vendor agreement, marketing claim, policy question, product counsel.
Required data: counterparty, value, region, data types involved, deadline, approvers.
Routing rules: low-risk NDAs auto-serve; anything involving personal data routes to privacy; >$250k to commercial counsel; regulated markets to specialist.
Playbook links: preferred positions, fallback clauses, escalation thresholds.
Decision log: capture rationale (why we approved/blocked), not just the outcome.

Document this as a living artifact. In Sandstone, these become modular workflows—layered data and decisions that build on each other rather than disappear in email.

Where AI Agents Add Real Value (Not Magic)

When your triage model exists, an AI agent can operate as connective tissue across tools you already use.

Classification and extraction: parse emails, forms, or tickets; identify request type; extract key fields (value, jurisdiction, data categories) with confidence scores.
Knowledge retrieval: check your playbooks, prior similar matters, and clause library to propose next steps that align with precedent.
Auto-responses: for truly low-risk asks (e.g., mutual NDA within thresholds), generate and send the pre-approved template with the right names and dates.
Smart routing: package the request with normalized data and the suggested path; post to your matter system or ticket queue; notify stakeholders.
Decision logging: append the reasoning and source materials, so every action strengthens the knowledge layer.

What to Measure: Four KPIs That Prove It’s Working

Measurement moves the conversation from anecdotes to outcomes.

First-response time: minutes from submission to acknowledgment or auto-resolution.
Cycle time by request type: median days from intake to closure; watch for variance by business unit.
Auto-resolution rate: percentage of requests fully handled without attorney intervention, gated by risk thresholds.
Rework rate: percentage of matters returned due to missing info; the triage form and agent should drive this down.

Add a simple requester satisfaction pulse (one-click CSAT) to corroborate the operational data.

A Safe Two-Week Pilot Plan

You don’t need a multi-quarter transformation. Prove value in one workflow, then expand.

Days 1–2: Select a single high-volume workflow (mutual NDA, marketing content review, or low-risk vendor reviews). Define success metrics and SLAs.
Days 3–5: Configure a minimal intake form and routing rules. Import the relevant playbook pages and templates.
Days 6–7: Connect systems (SSO, ticketing, CLM) and set guardrails (DLP, role-based access).
Days 8–10: Train the agent with 10–20 prior examples; validate extraction and suggested actions against counsel review.
Days 11–14: Go live with one business unit. Enable auto-resolve only for low-risk scenarios; everything else routes with suggested next steps. Hold a 30-minute daily standup to review edge cases.

By the end of week two, you should see faster acknowledgments, cleaner data capture, and a baseline auto-resolution rate you can confidently expand.

How To Turn Legal Intake Into an AI-Powered Triage Layer

The Intake Bottleneck You Can Actually Fix

Define the Triage Model Before You Automate

What to Measure: Four KPIs That Prove It’s Working

Actionable Next Step

The Bedrock of Trust and Growth

About Jarryd Strydom

How To Turn Legal Intake Into an AI-Powered Triage Layer

The Intake Bottleneck You Can Actually Fix

Define the Triage Model Before You Automate

What to Measure: Four KPIs That Prove It’s Working

Actionable Next Step

The Bedrock of Trust and Growth

About Jarryd Strydom