CaseMeister: AI case management for law firms

In short

CaseMeister is an AI case-management platform for law firms that makes case handling 50% faster. The system automatically classifies files with AI, generates summaries and searches documents through Mistral-based vector search. This helps legal professionals find the right information sooner and frees up more time for substantive work.

Next.js✦React 19✦TypeScript✦Laravel 12✦Tailwind CSS✦Filament✦Node.js✦Python✦PostgreSQL✦Redis✦Supabase✦Prisma✦Shopify✦Stripe✦Mollie✦GraphQL✦tRPC✦React Native✦OpenAI✦Anthropic Claude✦LangChain✦RAG✦Computer Vision✦Pinecone✦AWS✦Cloudflare✦Vercel✦Docker✦Kubernetes✦Terraform✦Three.js✦WebGL✦GSAP✦Framer Motion✦Playwright✦Vitest✦Next.js✦React 19✦TypeScript✦Laravel 12✦Tailwind CSS✦Filament✦Node.js✦Python✦PostgreSQL✦Redis✦Supabase✦Prisma✦Shopify✦Stripe✦Mollie✦GraphQL✦tRPC✦React Native✦OpenAI✦Anthropic Claude✦LangChain✦RAG✦Computer Vision✦Pinecone✦AWS✦Cloudflare✦Vercel✦Docker✦Kubernetes✦Terraform✦Three.js✦WebGL✦GSAP✦Framer Motion✦Playwright✦Vitest✦

50%
Sneller dossierbeheer
Juristen handelen dossiers half zo snel af dankzij AI.
AI
Automatische classificatie
Dossiers worden automatisch ingedeeld en samengevat.
Mistral
Vector-search
Semantisch zoeken door stukken op basis van Mistral.
LegalTech
Markt advocatuur
AI dossierbeheer gebouwd voor advocatenkantoren.

— CHALLENGE

The challenge at CaseMeister.

Law firms drown in casework. Every new matter arrives as an unsorted pile of material: correspondence, pleadings, attachments and emails that someone has to read, classify and summarise before a lawyer can engage with the substance. That preparation took hours per file and scaled badly as the caseload grew.

The brief to NedDev was specific: build a system that understands legal documents on its own, organises them and extracts the essentials, without sensitive client data leaving the firm for some uncontrollable external party.

— APPROACH

Our approach.

We built CaseMeister on the NedDev stack: a Laravel backend with Filament for case management, and a Next.js frontend for the people handling the files. The substantive intelligence runs on Mistral, which classifies incoming documents by type and produces a concise summary per matter.

To surface the right passages we added a vector-search layer: documents are embedded and made semantically searchable, so a lawyer searches on meaning rather than keyword. Classification and summarisation run as queue jobs, so uploading a large file never leaves the user waiting.

— OUTCOME

What it delivered.

The result is that case handling runs roughly 50% faster. Documents are classified and summarised automatically on arrival, so the lawyer starts with an overview instead of an unsorted stack.

The vector search means relevant passages are found in seconds, even across sizeable files. The time freed up goes back into the work that actually matters: legal assessment and client contact, rather than the administrative reading that used to come first.

CaseMeister

Not just client work. Our own products too.

NedDev WRK^®

NedDev Journal^©

NedDev Lab^™

The challenge at CaseMeister.

Our approach.

What it delivered.

Ready for your next build.

CaseMeister

Not just client work. Our own products too.

NedDev WRK®

NedDev Journal©

NedDev Lab™

The challenge at CaseMeister.

Our approach.

What it delivered.

Ready for your next build.

NedDev WRK^®

NedDev Journal^©

NedDev Lab^™