How to Automate Client Intake Triage with AI

Every new inquiry is a race. The firm that responds first and qualifies fastest wins the work. Most firms still triage intake by hand, which means leads sit in a voicemail or web-form queue until a coordinator gets to them, and the strongest prospects get the same slow treatment as the weakest.

This guide builds an AI intake triage workflow in n8n. It reads every inbound inquiry the moment it lands, scores it against your firm's criteria, routes it to the right person with a recommended next step, and writes the whole thing back to your CRM CRMCustomer Relationship Management software. The system of record for contacts, deals, and client communication. Examples: HubSpot, Salesforce, Pipedrive.. This is the practical build behind Play 2 (Lead Qualification), reinforced by Play 1 (Hands-Free CRM) so the first human touch starts with context instead of a blank screen.

Expected setup time: 3 to 5 hours. Expected ROI: most firms running this cut the hours spent on manual triage from low double digits per week to a handful, and they stop losing after-hours leads to a slow Monday-morning catch-up. The leakage you currently feel but cannot measure becomes a number you can shrink on purpose.

Before you start, confirm you have:

• A running n8n instance (self-hosted is the default recommendation in this playbook; n8n Cloud works too)
• Admin access to your CRM or case system (HubSpot, Lawmatics, Filevine, or Litify)
• An API APIApplication Programming Interface. The connection point that lets two pieces of software exchange data. How n8n talks to your CRM. key for an AI provider (OpenAI or Anthropic)
• Your inquiry channels identified: web form, a call tracking tool like CallRail, and a shared intake inbox
• Your qualification criteria written down in plain language: what makes a strong inquiry, a borderline one, and a clear decline

If you have never used the AI node in n8n, read how to use the AI/LLM node in n8n with OpenAI first. It covers the credential setup and prompt structure this workflow relies on.

1. Capture every inquiry channel into one trigger

Start a new workflow and add a Webhook WebhookA way for one app to send real-time data to another the instant an event happens. Example: DocuSign pings n8n the moment a contract is signed. node. This is the single entry point that every channel will hit.

• Point your website form (or its Zapier/Make connector) at the n8n Webhook URL so each submission fires it.
• In CallRail, add an outbound webhook (Settings > Integrations > Webhooks) that posts call data and the call transcript to the same n8n endpoint on every completed or missed call.
• For the shared intake inbox, add a separate IMAP Email or Gmail Trigger node in the same workflow that fires on new mail to that address.

The goal is simple: nothing waits for someone to refresh a tab. Every inquiry, on any channel, kicks off the workflow within seconds.

2. Normalize the inquiry into one clean record

Each channel sends data in a different shape. Add a Set node (or a small Switch node ahead of it to detect the source) that maps everything into one consistent record:

• name
• phone
• email
• source (form, call, or email)
• rawMessage (the form text, the call transcript, or the email body)

Now the rest of the workflow only has to deal with one clean structure no matter where the lead came from.

3. Score the inquiry with an AI node

Add an OpenAI or Anthropic node and feed it the normalized record. Use a prompt like this, edited to your firm's reality:

You are an intake analyst for a professional services firm. Read the inquiry below and return JSON with three fields: score (one of "strong", "borderline", "decline"), facts (the key details that matter for qualification, such as the service needed, urgency, and any disqualifiers), and reason (one or two plain-English sentences explaining the score). Do not invent details that are not in the inquiry. Inquiry: {{ $json.rawMessage }}

Set the model temperature low (around 0.2) so scoring is consistent. Turn on JSON output mode so the next nodes can read the fields directly.

4. Route by score with a recommended action

Add a Switch node keyed on score:

• Strong: Branch to an immediate alert (next step) and have a second AI node pre-draft a short callback script the coordinator can use.
• Borderline: Branch to a "needs review" queue, which is simply a CRM tag plus a task assigned to your intake lead.
• Decline: Branch to a logged, courteous reply (an Email or SMS node) and a CRM note recording the decline reason. No one should have to re-decide a lead that was already declined.

5. Write everything back to the CRM

On every branch, add your CRM node (HubSpot, Lawmatics, or your case system). Configure it to create the contact, or update it if a record already matches on email or phone, and write:

• The score and the AI reason
• The extracted facts
• The source channel
• The routing decision and timestamp

This is the Play 1 piece: the case record is current automatically, so the first person to pick up the lead starts with full context.

6. Notify the right person and log the reason

For the strong branch, add a Slack node or an Email node that alerts the on-duty intake person with the name, source, score, reason, and the pre-drafted callback script. Include a direct link to the CRM record.

For borderline leads, batch them into a single daily digest rather than pinging all day. For declines, the CRM note is the log; no human alert is needed.

• n8n orchestrates the whole workflow. See n8n examples and best practices.
• CallRail (or your phone system) feeds inbound and missed-call data plus transcripts.
• OpenAI or Anthropic reads and scores each inquiry. The AI/LLM node guide covers setup.
• HubSpot, Lawmatics, Filevine, or Litify is the system of record. See how to connect HubSpot to n8n.
• Slack or email delivers the strong-lead alerts.

• Scoring with a vague prompt. "Is this a good lead?" produces inconsistent results. Write the prompt around your actual qualification rules so the score means the same thing every time.
• Skipping the normalize step. If you score raw form data on one branch and raw transcripts on another, your scoring drifts by channel. Normalize first, score once.
• Letting the AI message clients directly without review on strong leads. Pre-draft the callback, but keep a human in the loop for anything high-value. The automation prepares the touch; it does not replace it.
• Alerting on everything. If every lead pings the team, the alerts get ignored. Reserve real-time alerts for strong leads and digest the rest.
• Not logging declines. The decline log is what stops the same lead from getting re-worked three times. Write the reason every time.

This is the industry-agnostic build. For the vertical-specific version with named systems, criteria, and compliance notes, see AI for Client Intake Triage for Personal Injury Law Firms. The same pattern applies to accounting firms, financial advisory practices, agencies, consulting firms, and any services business where the first response wins the work. Swap the case system and the qualification criteria; the workflow shape stays the same.

For the full strategy this build sits inside, see Play 2: Lead Qualification and Booking.