How to automate client intake for law firms

What this automation does

Every inquiry, from the website, a referral email or a phone call, ends up as a clean, conflict-screened intake record on the right attorney’s desk.

1. Capture. A structured web form collects the inquirer’s name and contact details, the other parties involved, the matter type, key dates, and how they heard about the firm. It shows the firm’s non-engagement notice and records that it was displayed.
2. Summarize and extract. A model reads the free-text description and returns a neutral two-sentence matter summary, a practice-area label, any deadline mentioned (such as a hearing date or a limitations concern), and a list of every person and organization named.
3. Conflict search. The workflow searches your practice management system’s contacts and matters for each extracted name, with spelling variants and business-name normalization, and builds a hit list.
4. Route. The intake coordinator or responsible attorney gets a single card in Microsoft Teams or email with the summary, the hit list and the deadline flag. Inquiries mentioning a near-term deadline go to the top.
5. Clear and schedule. Once a person clears conflicts, the prospect gets a scheduling link for a consultation. If not cleared, a polite decline template is sent and nothing more is collected.
6. Engage. After the consult, an engagement letter is generated from the firm’s template and sent for e-signature. On signature, the matter is opened in the practice management system.

When it is worth building

It pays off when inquiries sit unanswered. Prospective clients often contact several firms, and a quick, organized response matters. If your firm gets more than a handful of inquiries a day across a website form, email and phones, or if conflict checks are run inconsistently, this fixes both.

If you already use a dedicated legal CRM with intake forms, such as Clio Grow, use its built-in forms and automations first. Build the custom workflow when you need the AI summary, the multi-name conflict preparation, or connections the product does not offer.

Do not use it to give legal information to prospects automatically. An auto-reply should acknowledge, set expectations on timing and nothing more. If you want after-hours coverage, see AI after-hours answering, configured so it takes messages rather than advising.

Architecture

How to build it

1. Agree on the minimum data. With the managing partner, list exactly what you need before conflicts clear: names of all parties, matter type, key dates and contact details. Leave detailed facts for the consultation.
2. Build the form. Use repeatable fields for “other parties involved” so names arrive structured. Add the firm’s notice text and a required acknowledgment.
3. Write the extraction prompt. Ask for every named person and organization with their role (client, opposing party, witness, related company), plus a neutral summary. Instruct the model not to assess the merits.
4. Build name normalization. Strip suffixes like LLC and Inc., handle initials and common nicknames, and search each variant. Return matches with the matter they belong to and the role the person had.
5. Connect the practice management API. Register the API application, scope it to read contacts and matters, and store the token in n8n’s credential store, not in a workflow field.
6. Build the attorney card. Show summary, deadline flag, practice area and conflict hits side by side, with clear, decline and “need more info” actions that write back the decision and the reviewer’s name.
7. Gate scheduling on clearance. The consultation link and any further questionnaire go out only after the clear action.

The downloadable starter template below covers form intake, AI extraction, the conflict search scaffold and the Teams approval card.

What breaks in production

• Missed adverse parties. The prospect mentions “my ex-husband’s company” but not its name. The model should flag unnamed parties as a follow-up question rather than silently skip them.
• Name-matching noise. Common surnames return dozens of hits, and married names or transliterations return none. Show match reasons and let staff search manually from the card.
• Prospects oversharing. People paste whole stories, documents and medical details into the description box. Set a character limit, show the notice prominently, and restrict access to unscreened submissions.
• Deadline buried in the narrative. “I was served last Tuesday” implies a response deadline. Have the model extract dates and events into fields, and surface anything within 30 days at the top of the queue. An attorney still calculates the actual deadline.
• Duplicate inquiries. The same person submits the form and calls. Match on email and phone so the second contact attaches to the first record.

Timeline and cost

A form-to-engagement workflow connected to one practice management system typically takes three to four weeks, including testing on past inquiries with names anonymized. Lumeor builds law firm intake automation in the range of $6,000 to $12,000, depending on the number of intake channels, whether the practice management API is available on your plan, and how much of the engagement step you want automated. Running costs are model usage, which is small at intake volumes, plus n8n hosting.

If your firm is weighing several automations at once, an AI readiness assessment helps rank them before you commit.

How to measure success

• Median time from inquiry to first human response
• Share of inquiries with a documented conflict decision before any consult is scheduled (target 100 percent)
• Consultations booked as a share of qualified inquiries
• Intake staff minutes per inquiry
• Conflict hits found by the automated search that manual intake missed, and the reverse