AI Intake for Legal Ops Leaders: 5 Plays to Cut Cycle Time…

Most in-house teams tell us that 30–50% of matters start with missing or unclear context. That gap—more than staffing or complexity—drives days of back-and-forth and a third of total cycle time. The fastest lever isn’t more people; it’s smarter intake. Enter AI intake agents: software that reads a request, collects missing details, applies your policies, and routes or acts accordingly—with auditability.

Below is a five-play plan to deploy intake agents the right way, grounded in workflows legal teams already run on Sandstone.

Play 1: Structure Intake Around Decisions, Not Forms

Traditional forms collect everything “just in case.” Agents collect only what the decision requires.

Define request types by outcome: “Sign this NDA,” “Review vendor MSA,” “Answer a marketing claim,” “Approve data use.”
Map the minimum decision inputs per type (e.g., signer, counterparty, term, data categories, revenue, region).
Auto-enrich requests so business users type less and accuracy goes up: pull counterparty metadata from your CRM, vendor risk from procurement, region from HRIS, product names from your catalog.
Add policy-aware prompts: if the requester selects “PII involved,” dynamically require data categories and storage location.

On Sandstone, these inputs live as layered data—portable across workflows—so one good intake strengthens the next.

Play 2: Triage With Rules You Can Defend

Triage isn’t a vibe; it’s codified risk. Agents classify, prioritize, and route with reasons.

Classification: detect request type and urgency using plain-language cues in the message and attachments.
Routing: assign to the right queue or person based on product, region, deal size, or specialization.
SLA: set first-response and completion targets by risk tier; escalate before breaches, not after.
Self-serve: when the request matches a safe pattern (e.g., mutual NDA under approved template), the agent delivers the document immediately and logs the decision.

Every action should carry a why. In Sandstone, the agent’s decision trace shows the rule, data, and policy that drove the outcome.

Play 3: Encode Playbooks As Policy, Not PDFs

Your best playbooks shouldn’t live in a wiki—they should power decisions in real time.

Thresholds: “Mutual NDAs under 12 months term auto-approve; others route to Legal.”
Jurisdictions: “If governing law = Delaware and counterparty is pre-cleared, proceed; otherwise escalate.”
Data/privacy: “If special-category data or cross-border transfers, trigger DPIA checklist.”
Exceptions: “If revenue > $X or redlines touch indemnity, notify GC and hold for review.”

Agents apply these rules consistently and capture exceptions as structured data, so each deviation becomes teachable and reportable.

Play 4: Automate Drafts, Receipts, and Answers With Guardrails

Agents shouldn’t replace judgment, but they can remove repetitive drafting.

Drafts: generate first-pass NDAs, vendor emails, intake receipts, and clause comparisons—always from your approved templates and positions.
Retrieval: answer common questions (“Can we use this logo?”) by citing the exact policy section and linking to source.
Summaries: produce a one-paragraph brief of the request and key risks for faster attorney review.
Guardrails: enforce model selection, redaction of sensitive data, and no external calls for high-confidentiality matters.

In Sandstone, generation is scoped to your knowledge layer, so outputs stay on-brand and on-policy.

Play 5: Close the Loop With Analytics That Matter

If you can’t measure it, you can’t scale it.

Cycle time: end-to-end per request type, with and without agent touch.
First-response time: how quickly requesters get a meaningful answer or next step.
Self-serve rate: percent resolved without attorney intervention.
Exception rate: where policy thresholds trigger review—and why.
Rework: drafts that needed significant changes, signaling a playbook gap.

Use these signals to harden policies, tune prompts, and retire edge cases into clear rules. Knowledge should compound, not disappear.

Governance First: Safety, Auditability, and Human-in-the-Loop

AI that moves fast must also stand up to scrutiny.

Data boundaries: constrain training and inference to approved models; keep sensitive content in your tenant.
Access control: least privilege by requester, request type, and data category.
Audit logs: preserve full decision trails for every automated action.
Human oversight: require review for flagged risks; make overrides easy and explainable.
Retention: apply matter-based retention and deletion policies automatically.

Sandstone bakes these guardrails into the workflow, so agents accelerate work without compromising defensibility.

Try This Next Week: A 2-Week Pilot Plan

Pick one high-volume, low-variance workflow: mutual NDAs or standard vendor onboarding.
Write the decision map: inputs, thresholds, auto-approve paths, and exceptions.
Configure the agent: dynamic intake, routing, and templates tied to your positions.
Launch to a small group with clear SLAs and a feedback loop.
Measure: first-response time, self-serve rate, and exception reasons. Iterate weekly.

Actionable takeaway: Stand up an AI intake agent for your mutual NDA flow in two weeks. Aim for a 60%+ self-serve rate and a sub-1-hour first response; use exceptions to refine policy.

The Bedrock of Faster, Safer Legal

Legal is strongest when knowledge layers, workflows, and decisions build on each other. AI intake agents make that foundation tangible—turning playbooks into action, shrinking cycle time, and giving the business clear, defensible answers faster. That’s the promise of Sandstone: a modern legal ops platform and knowledge layer where every intake, triage, and decision compounds, moving business and law in harmony—at speed and with trust.

Below is a five-play plan to deploy intake agents the right way, grounded in workflows legal teams already run on Sandstone.

Play 1: Structure Intake Around Decisions, Not Forms

Traditional forms collect everything “just in case.” Agents collect only what the decision requires.

Define request types by outcome: “Sign this NDA,” “Review vendor MSA,” “Answer a marketing claim,” “Approve data use.”
Map the minimum decision inputs per type (e.g., signer, counterparty, term, data categories, revenue, region).
Auto-enrich requests so business users type less and accuracy goes up: pull counterparty metadata from your CRM, vendor risk from procurement, region from HRIS, product names from your catalog.
Add policy-aware prompts: if the requester selects “PII involved,” dynamically require data categories and storage location.

On Sandstone, these inputs live as layered data—portable across workflows—so one good intake strengthens the next.

Play 2: Triage With Rules You Can Defend

Triage isn’t a vibe; it’s codified risk. Agents classify, prioritize, and route with reasons.

Classification: detect request type and urgency using plain-language cues in the message and attachments.
Routing: assign to the right queue or person based on product, region, deal size, or specialization.
SLA: set first-response and completion targets by risk tier; escalate before breaches, not after.
Self-serve: when the request matches a safe pattern (e.g., mutual NDA under approved template), the agent delivers the document immediately and logs the decision.

Every action should carry a why. In Sandstone, the agent’s decision trace shows the rule, data, and policy that drove the outcome.

Play 3: Encode Playbooks As Policy, Not PDFs

Your best playbooks shouldn’t live in a wiki—they should power decisions in real time.

Thresholds: “Mutual NDAs under 12 months term auto-approve; others route to Legal.”
Jurisdictions: “If governing law = Delaware and counterparty is pre-cleared, proceed; otherwise escalate.”
Data/privacy: “If special-category data or cross-border transfers, trigger DPIA checklist.”
Exceptions: “If revenue > $X or redlines touch indemnity, notify GC and hold for review.”

Agents apply these rules consistently and capture exceptions as structured data, so each deviation becomes teachable and reportable.

Play 4: Automate Drafts, Receipts, and Answers With Guardrails

Agents shouldn’t replace judgment, but they can remove repetitive drafting.

Drafts: generate first-pass NDAs, vendor emails, intake receipts, and clause comparisons—always from your approved templates and positions.
Retrieval: answer common questions (“Can we use this logo?”) by citing the exact policy section and linking to source.
Summaries: produce a one-paragraph brief of the request and key risks for faster attorney review.
Guardrails: enforce model selection, redaction of sensitive data, and no external calls for high-confidentiality matters.

In Sandstone, generation is scoped to your knowledge layer, so outputs stay on-brand and on-policy.

Play 5: Close the Loop With Analytics That Matter

If you can’t measure it, you can’t scale it.

Cycle time: end-to-end per request type, with and without agent touch.
First-response time: how quickly requesters get a meaningful answer or next step.
Self-serve rate: percent resolved without attorney intervention.
Exception rate: where policy thresholds trigger review—and why.
Rework: drafts that needed significant changes, signaling a playbook gap.

Use these signals to harden policies, tune prompts, and retire edge cases into clear rules. Knowledge should compound, not disappear.

Governance First: Safety, Auditability, and Human-in-the-Loop

AI that moves fast must also stand up to scrutiny.

Data boundaries: constrain training and inference to approved models; keep sensitive content in your tenant.
Access control: least privilege by requester, request type, and data category.
Audit logs: preserve full decision trails for every automated action.
Human oversight: require review for flagged risks; make overrides easy and explainable.
Retention: apply matter-based retention and deletion policies automatically.

Sandstone bakes these guardrails into the workflow, so agents accelerate work without compromising defensibility.

Try This Next Week: A 2-Week Pilot Plan

Pick one high-volume, low-variance workflow: mutual NDAs or standard vendor onboarding.
Write the decision map: inputs, thresholds, auto-approve paths, and exceptions.
Configure the agent: dynamic intake, routing, and templates tied to your positions.
Launch to a small group with clear SLAs and a feedback loop.
Measure: first-response time, self-serve rate, and exception reasons. Iterate weekly.

Actionable takeaway: Stand up an AI intake agent for your mutual NDA flow in two weeks. Aim for a 60%+ self-serve rate and a sub-1-hour first response; use exceptions to refine policy.

The Bedrock of Faster, Safer Legal

AI Intake for Legal Ops Leaders: 5 Plays to Cut Cycle Time and Risk

Play 1: Structure Intake Around Decisions, Not Forms

Play 2: Triage With Rules You Can Defend

Play 3: Encode Playbooks As Policy, Not PDFs

Play 4: Automate Drafts, Receipts, and Answers With Guardrails

Play 5: Close the Loop With Analytics That Matter

Governance First: Safety, Auditability, and Human-in-the-Loop

Try This Next Week: A 2-Week Pilot Plan

Actionable takeaway: Stand up an AI intake agent for your mutual NDA flow in two weeks. Aim for a 60%+ self-serve rate and a sub-1-hour first response; use exceptions to refine policy.

About Jarryd Strydom

AI Intake for Legal Ops Leaders: 5 Plays to Cut Cycle Time and Risk

Play 1: Structure Intake Around Decisions, Not Forms

Play 2: Triage With Rules You Can Defend

Play 3: Encode Playbooks As Policy, Not PDFs

Play 4: Automate Drafts, Receipts, and Answers With Guardrails

Play 5: Close the Loop With Analytics That Matter

Governance First: Safety, Auditability, and Human-in-the-Loop

Try This Next Week: A 2-Week Pilot Plan

Actionable takeaway: Stand up an AI intake agent for your mutual NDA flow in two weeks. Aim for a 60%+ self-serve rate and a sub-1-hour first response; use exceptions to refine policy.

About Jarryd Strydom