From prototype to production
Every project we ship is built to operate — not just to demo. Evaluation, observability, and documentation are part of the deliverable.
Projets
Études de cas techniques en ingénierie AI.
Travaux représentatifs montrant comment Haal Lab transforme la recherche AI moderne en systèmes qui tiennent sous charge réelle. Chaque projet couvre le problème, l'approche et l'architecture livrée.
haal-lab.solutions / projets
01 / Local AI Platform
GGUF Loader
An offline AI platform enabling users to run large language models locally with privacy and control.
Architecture
- Runtime
- llama.cpp
- Format
- GGUF
- Retrieval
- RAG
- Acceleration
- CUDA
PythonLLMsGGUFRAGCUDA
Problem
Running capable open-weight LLMs locally has historically required deep expertise — manual quantization, fragmented runtimes, GPU/CPU juggling, and no clean way to add retrieval. Most users gave up and routed private data through cloud APIs.
Approach
GGUF Loader packages the entire local-inference stack behind a single interface: model loading via the GGUF format, CUDA-accelerated inference through llama.cpp, a retrieval layer for grounded answers, and a clean API for tool integration. The system is hardware-aware — it picks the right quantization, context length, and batch size for the GPU it detects.
Outcome
A platform that turns local LLM deployment from a research project into a one-step operation — without surrendering data to a third-party endpoint.
02 / Semantic Retrieval
Legal Intelligence System
A semantic retrieval system designed for complex document analysis and knowledge discovery.
Architecture
- Embedder
- BGE-M3
- Index
- Vector DB
- Precision
- Reranking
- Ingest
- OCR
BGE-M3Vector DatabaseRerankingOCR
Problem
Legal corpora are hostile to naive search. Documents span decades, mix scanned PDFs with structured text, cite each other across jurisdictions, and use terminology that defeats keyword retrieval. Lawyers waste hours finding the paragraph they already half-remember.
Approach
The system ingests heterogeneous legal documents through an OCR + layout-aware pipeline, embeds them with BGE-M3 for multilingual dense + sparse representations, indexes them in a vector database tuned for high-recall retrieval, and applies a cross-encoder reranker on the shortlist. The result is search that understands intent — not just keywords — across contracts, statutes, and case law.
Outcome
A retrieval system that returns the right clause, in the right document, with citation — even when the query is paraphrased, multilingual, or spans multiple documents.
Engineering commitments
What every Haal Lab project ships with
Open and extensible by default
We build on open-weight models and open-source infrastructure. You own the system, the weights, and the data — no platform lock-in.
Have a project in mind?
We take on a small number of engagements at a time. Tell us what you are building.
FAQ
Questions about our projects
Details on GGUF Loader, the Legal Intelligence System, and how to access our work.
What is GGUF Loader?
GGUF Loader is an offline AI platform built by Haal Lab that enables users to run large language models locally with privacy and control. It uses the GGUF model format, CUDA acceleration via llama.cpp, and includes a retrieval layer for grounded answers — all without sending data to a cloud API.
What is the Legal Intelligence System?
The Legal Intelligence System is a semantic retrieval platform built by Haal Lab for complex legal document analysis. It uses BGE-M3 embeddings, a vector database, cross-encoder reranking, and OCR to ingest heterogeneous legal corpora (contracts, statutes, case law) and return the right clause with citation.
Can I see Haal Lab's code or projects?
Some of our work is open source and available on GitHub at github.com/haal-lab. Client engagements are proprietary and owned by the client. The case studies on our Projects page describe the problem, approach, and architecture of representative work.
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