JDBOT: Using AI to Redefine Discovery in Family Law

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We are not waiting to see how the practice of law unfolds in the age of AI, we’re driving it.

Behind the scenes, JDBOT, LLC is developing a custom-built pipeline that leverages AI to automate and accelerate one of the most time-intensive parts of litigation: discovery. In family law, discovery can make or break a case. It’s not glamorous, but it provides tremendous leverage in proving your claims and defeating theirs. Until now, it’s often been slow, expensive, and inconsistent.

Here’s a peak into how JDBOT works.

STEP 1: AI-Powered Document Handling

Using the most secure document handling processes that far exceed any prescribed security requirements, the process begins with pleadings, court orders, and agreements being processed by proprietary AI models specially trained to identify legal claims, factual allegations, and procedural history.

These documents are processed by proprietary models having been fine-tuned on family law statutes and case law. The models doesn’t just look for keywords because those can vary widely depending on the forms (or lack of form) used by the other side of the case. Instead, this AI model understands structure, semantics, and context, allowing it to:

• Extract key case facts (e.g. names of parties, children, support obligations, etc.)
• Identify legal claims and allegations (e.g. child support enforcement, modification, etc.)
• Detect procedural posture (e.g. petition, answer, motion)

STEP 2: Classification and Mapping to Legal Theories

The extracted factual allegations and claims are passed into a classification engine that maps allegations and claims to factual issues and then maps factual issues to legal theories. For example, an allegation about nonpayment of child support will automatically be mapped to a theory of enforcement. JDBOT supports dozens of legal theories curated by board-certified family law attorneys and tailored to Texas (for now).

STEP 3: Attorney Review and Confirmation

No AI replaces human judgment.

Once the models classify facts and claims and map them to legal theories, the attorney reviews the suggested legal theories, confirms the ones that are relevant, and, optionally, provides additional guidance (such as specific areas of concern or strategy). This step ensures professional oversight and ethical compliance while keeping the system agile and accurate.

STEP 4: Generating Discovery Requests

Once legal theories are confirmed by the attorney, JDBOT automatically selects standardized discovery requests from a curated library and, as necessary, generates custom requests based on the specific facts, claims, and theories of the case. Each request is validated for clarity, relevance, uniqueness, and legal sufficiency. The result: A ready-to-review package of interrogatories, requests for production, and admissions–precisely tailored to the needs of the case.

STEP 5: Document Assembly

Finally, after the attorney reviews and approves the discovery requests, JDBOT automatically assembles them into a professional-grade Microsoft Word document, ready to be further refined and edited and then served on opposing counsel.

The output is clean, well-formatted, and courtroom-ready with header templates, numbering conventions, and case-specific facts and variables auto-filled.

What Use to Take Hours Now Takes Minutes

Traditionally, generating discovery tailored to a case could take 3-5 hours per matter. With JDBOT, the time drops by 70% – 80%, including the time it takes the attorney to review, approve, and refine the final product. JDBOT is not just saving time, it’s delivering a better, more consistent work product quickly and less expensively: Cheaper, better, faster–that’s the goal!!

And we’re just getting started.

If you value innovation, precision, and results, this is the kind of future we’re building for you–today.