Automate Your Response to Customer Bankruptcy Filings
When a customer files for bankruptcy, you must stop all collection efforts immediately. You then file a proof of claim with the court to recover any money owed.
An AI system can extract key data from the notice and prepare your claim in seconds. This ensures you never miss a filing deadline and maximizes your recovery chance. The system's complexity depends on the volume and format of notices. Processing a few dozen standardized PDF notices per month is straightforward, while handling thousands of varied, scanned documents requires more sophisticated optical character recognition.
We built a system for a 15-person commercial supplier handling about 50 bankruptcy notices per month. Their collections manager spent 20 minutes per notice manually finding the case number, court, and deadline. The AI automation pipeline we deployed now processes each notice in under 8 seconds.
What Problem Does This Solve?
Most small businesses handle bankruptcy notices manually. When a PDF notice arrives in an email, a paralegal or an accounts receivable clerk opens it, searches for the key details, and types them into a spreadsheet or case management system. This process is slow and full of risk. A single typo in a 14-digit case number can get your claim rejected. Missing the bar date, often buried on page three, means you forfeit your right to collect entirely.
A regional B2B equipment supplier we worked with faced this exact issue. Their two-person AR team received notices as low-quality scans. They tried a generic PDF-to-text converter, but the output was a jumble of unformatted text. The tool could not distinguish the filing date from the meeting of creditors date, creating more cleanup work than it saved. They lost a $30,000 claim after one bar date was entered incorrectly into their calendar.
These notices are dense legal documents with inconsistent layouts that vary by court district. Off-the-shelf OCR tools fail because they lack the contextual understanding to correctly identify specific legal data points. They see characters and lines, not the legal concepts needed to take correct action, like identifying a Chapter 11 filing versus a Chapter 7.
How Does It Work?
Our system connects to the AR team's inbox using the Microsoft Graph API. An AWS Lambda function is triggered by new emails with subject lines like "Notice of Bankruptcy Filing." The function downloads the PDF attachment to an S3 bucket and uses Amazon Textract for OCR, which reliably extracts text from low-quality scans and complex table structures. This ingestion and text extraction takes about 3 seconds per page.
Once the raw text is available, we send it to the Claude 3 Sonnet API. The prompt we engineer instructs the model to act as a paralegal and find 7 key fields: Debtor Name, Case Number, Court District, Chapter (7, 11, or 13), Filing Date, Bar Date, and any listed assets. We provide 10 different notice examples in the prompt to ensure the model handles various formats correctly. Claude returns a structured JSON object in under 4 seconds.
The returned JSON is validated using a Pydantic model in our Python code. This checks that all fields are present and correctly formatted, for example, that dates are in ISO 8601 format and the case number matches a specific regex pattern. The validated data is then written to a Supabase table. If validation fails for any reason, the original PDF and the API response are sent to a designated Slack channel for manual review. This happens on less than 2% of notices.
From the Supabase table, we build a simple Retool dashboard where the AR team can view all processed notices. With a single click, they can generate a pre-filled Proof of Claim (Form 410) PDF. The system also integrates with their calendar, automatically creating events 14 days and 3 days before each bar date. The entire serverless architecture costs under $50 per month to run for up to 1,000 notices.
What Are the Key Benefits?
File Claims in Seconds, Not Hours
Reduce the 20-minute manual review per notice to an 8-second automated process. Your collections team can focus on recoverable accounts, not administrative data entry.
Stop Paying for Human Error
A fixed-price build eliminates the variable cost of typos and missed deadlines. Never lose a claim because someone mistyped a case number or misread a date.
You Get the Code and the Data
We deliver the complete Python codebase to your GitHub repository and set up the Supabase database in your AWS account. You have full ownership and control.
Alerts Before Deadlines, Not After
The system automatically creates calendar events for every bar date and sends Slack alerts for any processing failures. You always know what needs attention.
Connects to Your Existing Inbox
The process starts from your team's existing Microsoft 365 or Google Workspace inbox. No need to change how you receive legal notices.
What Does the Process Look Like?
Week 1: Document Review
You provide 10-15 sample bankruptcy notices from different court districts. We create read-only access to your email account to understand the intake workflow.
Week 2: Core Build
We write the Python code for the AWS Lambda function, build the Claude API prompts, and set up the Supabase database schema. You receive a daily progress update.
Week 3: Integration and Testing
We connect the system to your inbox and test it with incoming notices in a staging environment. You receive a private link to the Retool dashboard for review.
Week 4: Go-Live and Monitoring
We switch the system to production. For 30 days, we monitor every notice processed to ensure accuracy. You receive a runbook detailing the system architecture and error handling.
Frequently Asked Questions
- What does a system like this cost?
- Pricing depends on the volume and variability of your notices. A system for a small business processing under 100 similar notices a month is a straightforward build. A high-volume system for a lender dealing with thousands of notices from every court district requires more complex logic. We provide a fixed-price quote after the initial discovery call and document review.
- What happens when a notice can't be processed automatically?
- If OCR quality is too low or the Claude API returns invalid data, the system flags it. The original PDF and extracted text are sent to a designated Slack channel with an alert. This allows a human to review the failure in under 60 seconds and manually enter the data. This happens with less than 2% of documents.
- How is this different from using a paralegal service or BPO?
- Paralegal services charge per document or per hour, a cost that scales directly with volume. A BPO introduces data security risks, as you are sending sensitive customer financial data offshore. This automated system is a one-time build cost with minimal monthly hosting fees. It runs on your cloud infrastructure, so the data never leaves your control.
- Can this handle other legal documents besides bankruptcy notices?
- Yes. The core engine (OCR + Claude API + validation) can be adapted to other structured documents like contracts, invoices, or purchase orders. Each new document type requires a separate prompt and validation logic. We can scope this as an add-on to the initial build or as a future project.
- What kind of maintenance is required after the 30-day monitoring period?
- The system is built on serverless components (AWS Lambda, Supabase) that require no server management. We offer an optional flat monthly plan that covers dependency updates, API changes from Claude or AWS, and up to two hours of support for debugging processing failures. Most clients do not need this unless their document volume is extremely high.
- Which courts or document formats do you support?
- The system is trained on U.S. Federal Bankruptcy Court notices, which have semi-standard formats. We have processed notices from dozens of districts. It can handle scanned PDFs, digitally generated PDFs, and even text in an email body. If you receive unusual formats, we tune the OCR and prompts during the initial build using your specific examples.
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