What Is an AI Legal Intake Engine & Why Do In-House Teams…

If you’re like most in-house teams, 40–60% of legal’s day vanishes into intake emails, triage threads, and status pings—not legal judgment. The work is necessary, but it shouldn’t be manual. That’s where an AI legal intake engine comes in: a layer that captures requests wherever they start, structures the data, applies your playbooks, and routes decisions with speed and confidence.

The Bottleneck Hiding in Plain Sight

Legal intake sounds simple: capture a request, ask clarifying questions, route it, get it done. In reality, it’s where quality and cycle time go to die. Requests arrive incomplete. Context is buried in Slack, tickets, and email. The same questions get asked—and answered—daily. Without structure, every matter becomes bespoke.

For GCs and legal ops leaders, the stakes are bigger than convenience. Poor intake means unclear risk ownership, missed SLAs, and slow deals. It weakens trust with the business and erodes your ability to forecast workload. The fix isn’t another form; it’s a living operating system that turns intake into a repeatable, data-rich workflow.

What an AI Legal Intake Engine Actually Does

An AI intake engine is not just a smarter form. It’s a workflow brain that:

Captures requests from email, Slack/Teams, CLM, and portals.
Extracts key facts (counterparty, value, region, data types) and detects missing context.
Dynamically asks the requester for what’s missing—no back-and-forth.
Applies your playbooks and fallback positions to suggest next steps.
Auto-routes by policy (e.g., deal size, data sensitivity) and kicks off approvals.
Generates first drafts (NDAs, clause swaps, DPIA flags) where allowed.
Keeps the requester updated and logs outcomes to your knowledge base.

On Sandstone, this is powered by layered data: playbooks, positions, precedent, and workflow rules that an AI agent uses to triage, decide, and learn. Every completed request strengthens the next one—your institutional knowledge compounds instead of disappearing into inboxes.

Where It Fits in Your Workflow

Start with two high-volume, high-variance flows:

Commercial requests: NDAs, order forms, MSAs, DPAs, vendor terms. The intake engine classifies the request, fetches the right template, recommends positions for flagged clauses, and routes exceptions by threshold (e.g., ARR, region, privacy scope).
Privacy & security reviews: The engine gathers system facts (data categories, subprocessors, transfers), checks them against your policy, and drafts a risk summary for counsel to approve.

Because Sandstone integrates naturally—email aliases, Slack shortcuts, CLM/CRM hooks—it wraps around how your team already works. No behavior change required. Counsel stays in the loop, but only where judgment is needed. Everything else moves on rails.

Metrics That Matter

The promise of AI intake is not novelty; it’s measurable improvement. Track:

Time to First Response: From hours/days to minutes with auto-acks and clarifying questions.
Cycle Time: End-to-end reduction, especially for low-risk matters.
Request Completeness Rate: % of submissions meeting your definition of “ready to work.”
Auto-Resolution Rate: % handled without attorney touch (e.g., standard NDAs under threshold).
Escalation Mix: See where policy drives review vs. where playbooks can expand.
Satisfaction: Requester NPS or a simple “Was this helpful?” pulse to prove value.

These metrics do double duty: they justify investment and reveal where your playbooks should evolve.

Getting Started: A One-Week Pilot

You don’t need a six-month rollout. Prove value in a week:

Day 1–2: Map two request types (e.g., NDAs under $50k, low-risk vendor DPAs). Define what “complete” means and the routing rules.

Day 3: Import your templates, clause positions, and approval thresholds into Sandstone. Connect one intake channel (e.g., legal@ alias or a Slack shortcut).

Day 4–5: Run in “shadow” mode. The AI intake agent triages and proposes outcomes; counsel reviews and approves. Calibrate questions and routing.

Day 6–7: Turn on limited auto-resolve for the narrowest scope. Measure time to first response, cycle time, and auto-resolution rate against the prior week.

Your goal is a single, defensible win—say, 30% faster NDA turnarounds—plus visibility you didn’t have before.

The Strategic Payoff

When intake becomes a living system, legal stops chasing context and starts compounding knowledge. Playbooks aren’t PDFs; they’re executable policies. Positions aren’t tribal memory; they’re suggested in the flow of work. Business teams get predictable timelines, clearer requirements, and fewer surprises. Counsel gets time back for the matters that demand judgment.

This is Sandstone’s philosophy in practice: strength through layers, crafted precision, and natural integration. By transforming intake, triage, and decisions into an AI-powered operating system, you turn legal from a reactive queue into a proactive force for speed, alignment, and trust.

Actionable next step: Pick one request type and pilot an AI intake engine for seven days. If you can’t measure a faster response, higher completeness, and cleaner routing, don’t expand. If you can, scale with confidence—your foundation will only get stronger with every request.

The Bottleneck Hiding in Plain Sight

What an AI Legal Intake Engine Actually Does

An AI intake engine is not just a smarter form. It’s a workflow brain that:

Captures requests from email, Slack/Teams, CLM, and portals.

Extracts key facts (counterparty, value, region, data types) and detects missing context.

Dynamically asks the requester for what’s missing—no back-and-forth.

Applies your playbooks and fallback positions to suggest next steps.

Auto-routes by policy (e.g., deal size, data sensitivity) and kicks off approvals.

Generates first drafts (NDAs, clause swaps, DPIA flags) where allowed.

Keeps the requester updated and logs outcomes to your knowledge base.

Where It Fits in Your Workflow

Start with two high-volume, high-variance flows:

Commercial requests: NDAs, order forms, MSAs, DPAs, vendor terms. The intake engine classifies the request, fetches the right template, recommends positions for flagged clauses, and routes exceptions by threshold (e.g., ARR, region, privacy scope).

Privacy & security reviews: The engine gathers system facts (data categories, subprocessors, transfers), checks them against your policy, and drafts a risk summary for counsel to approve.

Metrics That Matter

The promise of AI intake is not novelty; it’s measurable improvement. Track:

Time to First Response: From hours/days to minutes with auto-acks and clarifying questions.

Cycle Time: End-to-end reduction, especially for low-risk matters.

Request Completeness Rate: % of submissions meeting your definition of “ready to work.”

Auto-Resolution Rate: % handled without attorney touch (e.g., standard NDAs under threshold).

Escalation Mix: See where policy drives review vs. where playbooks can expand.

Satisfaction: Requester NPS or a simple “Was this helpful?” pulse to prove value.

These metrics do double duty: they justify investment and reveal where your playbooks should evolve.

The Strategic Payoff

What Is an AI Legal Intake Engine & Why Do In-House Teams Need It?

The Bottleneck Hiding in Plain Sight

What an AI Legal Intake Engine Actually Does

Where It Fits in Your Workflow

Metrics That Matter

Getting Started: A One-Week Pilot

Day 1–2: Map two request types (e.g., NDAs under $50k, low-risk vendor DPAs). Define what “complete” means and the routing rules.

Day 3: Import your templates, clause positions, and approval thresholds into Sandstone. Connect one intake channel (e.g., legal@ alias or a Slack shortcut).

Day 4–5: Run in “shadow” mode. The AI intake agent triages and proposes outcomes; counsel reviews and approves. Calibrate questions and routing.

Day 6–7: Turn on limited auto-resolve for the narrowest scope. Measure time to first response, cycle time, and auto-resolution rate against the prior week.

The Strategic Payoff

Actionable next step: Pick one request type and pilot an AI intake engine for seven days. If you can’t measure a faster response, higher completeness, and cleaner routing, don’t expand. If you can, scale with confidence—your foundation will only get stronger with every request.

About Jarryd Strydom

What Is an AI Legal Intake Engine & Why Do In-House Teams Need It?

The Bottleneck Hiding in Plain Sight

What an AI Legal Intake Engine Actually Does

Where It Fits in Your Workflow

Metrics That Matter

Getting Started: A One-Week Pilot

Day 1–2: Map two request types (e.g., NDAs under $50k, low-risk vendor DPAs). Define what “complete” means and the routing rules.

Day 3: Import your templates, clause positions, and approval thresholds into Sandstone. Connect one intake channel (e.g., legal@ alias or a Slack shortcut).

Day 4–5: Run in “shadow” mode. The AI intake agent triages and proposes outcomes; counsel reviews and approves. Calibrate questions and routing.

Day 6–7: Turn on limited auto-resolve for the narrowest scope. Measure time to first response, cycle time, and auto-resolution rate against the prior week.

The Strategic Payoff

Actionable next step: Pick one request type and pilot an AI intake engine for seven days. If you can’t measure a faster response, higher completeness, and cleaner routing, don’t expand. If you can, scale with confidence—your foundation will only get stronger with every request.

About Jarryd Strydom