AI Automation for Law Firm Client Intake and Case Management
The best AI automation solutions for small law firms are custom systems that use AI to read and classify client documents. These systems route new matters to the right attorney in seconds, with full human-in-the-loop review.
Building a reliable system requires connecting directly to your firm's data sources, like an email inbox or document portal, and handling unstructured PDFs and scans. The complexity depends on the number of distinct matter types and the specific information needing extraction, from client names to property addresses.
We built an intake system for an 8-person real estate firm. New lease agreements arriving as email attachments are now processed in 90 seconds, down from 45 minutes of manual paralegal review. This system has processed over 2,500 documents with an initial classification accuracy of 98%.
What Problem Does This Solve?
Many law firms try to automate intake with their existing Legal Practice Management Software (LPMS) like Clio or MyCase. These tools have rule-based automations that can trigger tasks or send emails, but they cannot read an attached PDF to determine if it is a residential lease or a commercial purchase agreement. A paralegal still has to open every document, identify the matter type, and manually create the case file.
A common next step is a generic automation tool. A firm might try to connect Gmail to their LPMS to create a new matter for every email with an attachment. But the workflow breaks because it has no intelligence. It cannot perform Optical Character Recognition (OCR) on a scanned document, extract the client's name, or classify the matter. The result is a system that creates junk records for every spam email, requiring more manual cleanup than the original process.
These off-the-shelf tools fail because they treat documents as files to be moved, not as information to be understood. They lack the ability to process unstructured data, apply conditional logic based on document content, and integrate human review before taking action. This leaves firms stuck with expensive manual data entry for their most critical business process.
How Does It Work?
Our process begins by connecting directly to the source of your documents, typically an AWS S3 bucket or a dedicated intake email inbox. We use an AWS Lambda function triggered on every new file arrival, which ensures processing begins within seconds. This serverless architecture means you are not paying for an idle server, keeping monthly hosting costs under $50 for most firms.
Once triggered, the system performs OCR on any scanned PDFs to extract raw text. We then use the Claude API to classify the document into one of your predefined matter types, like 'Estate Planning' or 'Commercial Litigation'. The same process extracts key entities, flags non-standard clauses against your firm's clause library, and generates a concise summary. For an 8-person real estate firm, this entire pipeline, from email receipt to classification, executes in 90 seconds.
All extracted data, summaries, and AI confidence scores are written to a Supabase database, which provides a complete audit trail for every document. We build a simple FastAPI web interface for your team. Anything the AI flags with a confidence score below 95% appears in a queue for human review. A paralegal can approve or correct the classification with a single click before the matter is officially created in your system.
Upon approval, the system uses your LPMS's API to create a new case file, assign it to the correct attorney based on your routing rules, and upload the source document. This closed-loop process ensures that no document is missed and every AI-driven action is logged and auditable, all while keeping client data within your own cloud infrastructure.
What Are the Key Benefits?
From 45 Minutes to 90 Seconds
Reduce manual document review time for paralegals by over 95%. New client matters are triaged and routed the moment they arrive.
One-Time Build, Not a Per-Seat Subscription
After the initial build, your only ongoing cost is for cloud hosting, typically under $50/month. No recurring license fees that scale with your firm's headcount.
You Get the GitHub Repo and Runbook
You receive the complete Python source code and technical documentation. Your firm owns the system outright, with no vendor lock-in.
Human Review for Every Flagged Item
The system never acts alone. Low-confidence classifications are automatically gated, requiring manual approval from your team before a case is created or routed.
Integrates with Your Existing Software
We connect directly to your email server and legal practice management software. Your team's workflow does not change; the manual steps just disappear.
What Does the Process Look Like?
Discovery and Access (Week 1)
You provide 5-10 sample documents for each matter type. We map out your existing intake process and you grant secure, read-only access to the document source (e.g., email inbox).
Core AI Build (Week 2)
We build and test the Claude API prompts for classification and extraction. You receive a classification report showing accuracy on your sample documents.
Integration and Deployment (Week 3)
We deploy the system on AWS and connect it to your LPMS. The first live documents are processed through the system with your team monitoring the review queue.
Monitoring and Handoff (Week 4)
We monitor system performance for one month to address edge cases. You receive the full source code, a technical runbook, and a final handoff session.
Frequently Asked Questions
- How much does a custom intake system cost?
- Pricing is based on a fixed project scope, not hourly billing. The final cost depends on the number of distinct document types to be classified, the complexity of data to be extracted, and the number of systems to integrate with. We provide a firm quote after the initial discovery call where we review your documents and requirements. Book a discovery call at cal.com/syntora/discover to discuss pricing.
- What happens if the AI misclassifies a document?
- The system is designed for this. Every AI classification has a confidence score. If the score is below a set threshold (e.g., 95%), the document is automatically routed to a human review queue. Your team can then correct the classification before the case is created. This human-in-the-loop design prevents errors from propagating through your workflow and ensures final say always rests with your staff.
- How is this different from the automation in Clio or PracticePanther?
- Those tools offer trigger-based automation. For example, 'if case status changes to X, then send email Y'. They cannot read or understand the content of an unstructured document like a PDF. Our system uses AI to perform content-aware automation, such as classifying a 10-page lease agreement and routing it to your real estate practice group, something LPMS tools cannot do.
- How do you handle confidential client information and privileged documents?
- We build the entire system within your own dedicated cloud environment. Your documents are stored in your AWS S3 bucket and data is kept in your Supabase instance. While we use the Claude API for processing, we use their most secure offerings and do not permit third-party model training on your data. At no point are privileged documents stored on Syntora's infrastructure.
- What if our firm adds a new practice area?
- The system is designed to be extensible. To add a new matter type, you would provide us with new sample documents. We then update the classification logic, a process that typically takes a few days. We document this procedure in the technical runbook you receive, so your own technical staff or a future partner could make these modifications as well.
- Do we need a technical team to maintain this system?
- No. The system is built on serverless components like AWS Lambda that require minimal maintenance. We set up automated monitoring and health checks that alert us if there is an issue. For ongoing changes or support after the initial monitoring period, we offer an optional, flat-rate monthly support plan. The goal is a system that runs reliably without needing a dedicated engineer.
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