Introducing MDL Match

A client calls about stomach problems after taking a weight loss drug. Standard intake. Maybe a med mal case, maybe nothing. The attorney moves on.

What they didn't catch: the client took semaglutide for 18 months. That's MDL-3094 — Ozempic. A single referral is worth $10K-$50K.

This happens every day. Mass tort cases are hiding in your intake pipeline. You never know what you left on the table.

Nobody Sits at the Intake Layer

Case management platforms help firms manage mass tort cases they already know about. Medical review tools analyze records for cases already accepted. Lead gen companies run ads for specific torts. Intelligence dashboards help firms decide where to spend marketing dollars.

Nobody screens incoming cases against the full universe of active federal MDLs and says "this is bigger than you think."

That's what MDL Match does.

How It Works

Paste intake notes, a medical summary, or any case narrative. Get ranked matches against 158+ active federal MDLs in seconds.

Each result includes:

• MDL number and litigation name — MDL-3094 · Ozempic (Semaglutide)
• Confidence score — 92% match
• Qualifying criteria — why this case matches
• Pending actions — 3,063 cases pending
• Lead counsel and district — who's running the litigation
• Settlement estimates — what similar cases have resolved for

The matching is semantic, not keyword-based. Your client says "weight loss injection" instead of "Ozempic" or "semaglutide"? We still catch it. That's the difference between pattern matching and actually understanding case facts.

Why It Works

Most screening tools rely on keyword matching. Your client says "weight loss injection" instead of "Ozempic"? Missed. They mention "acid reflux pills" instead of "Zantac"? Missed.

MDL Match understands case facts semantically. We catch what keyword matching misses.

Our MDL index updates monthly from official JPML data. Qualifying criteria, lead counsel, settlement estimates, filing deadlines — all current, all verified. You're matching against the most complete picture of active federal MDLs available.

The Math

Consider a practice processing 100 intakes per month. If 2% contain hidden mass tort potential — that's 24 cases per year.

At an average referral fee of $15,000 per case: $360,000 in annual revenue that was previously walking out the door.

One matched case pays for years of subscription. The product pays for itself on the first match.

What You Get

Dashboard access — Paste any case. See matches instantly. Browse the full MDL registry with category filters, pending action counts, and momentum indicators.

API access — POST /api/v1/match with case text. Get JSON back with ranked matches, confidence scores, and MDL metadata. Integrate into your intake workflow.

MDL intelligence — Full profiles on 158+ active federal MDLs. Qualifying criteria, lead counsel, settlement data, filing deadlines. Updated monthly.

Integrations

MDL Match fits into the tools you already use. We integrate with:

• Clio — Screen cases directly from your practice management workflow
• Litify — Mass tort detection built into your Salesforce-based platform
• Lawmatics — Flag potential MDL matches during intake automation
• CasePacer — Add mass tort screening to your case management pipeline
• Filevine — Automated matching across your entire case database

Don't see your platform? Our API works with any system. POST case text, get matches back as JSON.

Who This Is For

Solo and small firm PI attorneys — You handle general intake. MVA, slip and fall, med mal, workers comp. You encounter potential mass tort cases but lack the infrastructure to identify them. One match changes your year.

Mass tort firms — You're focused on Hair Relaxer and Ozempic. But are you catching adjacent MDLs your current plaintiffs might also qualify for? Automated screening across thousands of cases.

Legal tech platforms — You process 10,000 intakes per month. You want mass tort cases flagged automatically before they enter your workflow. API integration, pay per query.

Start Matching

Mass tort cases are hiding in your intake pipeline. The question isn't whether they exist — it's whether you're catching them.