Discovery
We start with the problem, not the model. A focused engagement to understand constraints, data, success criteria, and the production environment the system will live in.
Soluciones
Capacidades AI, diseñadas para producción.
Cuatro capacidades interconectadas que cubren todo el stack AI — desde inferencia local privada hasta sistemas de conocimiento empresarial. Cada una puede entregarse de forma independiente o componerse en una plataforma unificada.
haal-lab.solutions / soluciones
01 / Capability
Local AI Systems
Private AI solutions that run securely on your infrastructure.
We build AI systems that operate entirely inside your environment — on workstations, on-prem servers, or air-gapped clusters. Local execution removes the trust, latency, and compliance constraints of cloud-hosted models, while preserving the full capabilities of modern open-weight LLMs. Our deployments are designed around quantized inference runtimes, GPU/CPU co-scheduling, and hardware-aware model selection so that the system remains responsive on whatever hardware you already own.
What we deliver
- On-prem inference with GGUF, llama.cpp, vLLM, and TGI
- Air-gapped deployment for regulated environments
- Hardware-aware quantization (INT4 / INT8 / FP8)
- Data sovereignty by architecture, not policy
- Offline model registry and version control
Stack
GGUFllama.cppvLLMCUDADockerKubernetes
02 / Capability
LLM Applications
Custom AI assistants, agents, and intelligent automation systems.
We design and ship LLM applications that go beyond chat — assistants that take action, agents that orchestrate tools, and automation systems that integrate cleanly with your existing software. Every application is built with evaluation harnesses, guardrails, and observability from day one, so behaviour stays predictable as you scale from prototype to production traffic.
What we deliver
- Agent orchestration with structured tool calling
- Tool-augmented LLMs over your internal APIs
- Workflow automation with human-in-the-loop safety
- Prompt and response evaluation pipelines
- Streaming, structured output, and function calling
Stack
PythonTypeScriptOpenAIAnthropicOpen weightsLangGraph
03 / Capability
Knowledge Intelligence
Advanced RAG systems, semantic search, and document intelligence.
We build retrieval systems that actually find the right answer. Our RAG pipelines combine dense and sparse retrieval, cross-encoder reranking, query rewriting, and source attribution into a single, observable system. For document-heavy domains we add OCR, layout-aware chunking, and table understanding — so the system works on contracts, research papers, and scanned archives, not just clean text.
What we deliver
- Hybrid retrieval (BM25 + dense embeddings)
- Cross-encoder reranking for precision
- Multi-vector and parent-document indexing
- OCR + layout-aware document chunking
- Source attribution and citation tracking
Stack
BGE-M3QdrantPostgresColPaliOCRrerankers
04 / Capability
AI Infrastructure
Deployment, optimization, and scalable AI engineering.
We build the infrastructure layer that makes AI systems run reliably in production. That means model serving tuned for your hardware, autoscaling that respects GPU memory, observability that surfaces latency and quality drift, and CI/CD pipelines that evaluate models — not just unit tests. The result is an AI platform your team can iterate on without firefighting.
What we deliver
- Model serving with vLLM, TGI, and Triton
- GPU scheduling, batching, and memory tuning
- Observability: traces, metrics, evals, drift
- Evaluation-driven CI/CD for prompts and models
- Cost and throughput optimization
Stack
vLLMTritonPrometheusOpenTelemetryKubernetesTerraform
Engagement
How we work
A four-stage engagement model designed to de-risk AI projects — and to leave your team with a system they can operate and extend.
Architecture
We design the system end-to-end — model choices, retrieval strategy, infrastructure, evaluation harness — and pressure-test it against your real workloads before committing.
Build
Engineering in small, demonstrable increments. You see working software early and often, with evaluation reports attached to every milestone.
Deploy
We ship to your environment — cloud, on-prem, or air-gapped — with the observability, runbooks, and documentation your team needs to operate it confidently.
Want a deeper walkthrough?
We can map any of these capabilities to your specific use case in a 45-minute call.
Glossary
AI engineering terms, defined.
The vocabulary we use across this site — defined plainly so anyone evaluating AI systems can follow along.
- RAG
- Retrieval-Augmented Generation — an architecture that grounds LLM responses in your own documents by retrieving relevant passages and feeding them as context.
- LLM
- Large Language Model — a neural network trained on large text corpora that generates text, answers questions, and performs natural language tasks.
- GGUF
- GPT-Generated Unified Format — a file format for storing quantized language models so they can run efficiently on consumer hardware.
- BGE-M3
- A multilingual embedding model that produces dense, sparse, and ColBERT-style representations in a single pass, used for semantic search.
- Reranking
- A second-stage retrieval step that uses a more expensive model (usually a cross-encoder) to re-score and reorder the top results for higher precision.
- Vector Database
- A database optimized for storing and querying high-dimensional vectors (embeddings), used for semantic search and RAG.
- Open-weight model
- A language model whose weights are publicly available for download and local execution — such as Llama, Mistral, or Qwen.
- Air-gapped
- A deployment with no network connection to the outside world — used in regulated environments where data cannot leave the premises.
- Fine-tuning
- The process of further training a pre-trained model on domain-specific data to specialize its behavior.
- Embedding
- A numerical vector representation of text that captures semantic meaning, enabling similarity search.
- Cross-encoder
- A model that takes a query and a document together and outputs a single relevance score — slower than bi-encoder retrieval but more accurate.
- Agent orchestration
- Coordinating multiple LLM calls, tool uses, and reasoning steps to accomplish a complex task autonomously.
FAQ
Questions about our capabilities
Common questions about local AI, RAG, BGE-M3, air-gapped deployment, and AI infrastructure.
What is a local AI system?
A local AI system runs entirely on your own hardware — workstations, on-prem servers, or air-gapped clusters — without sending data to a cloud API. Haal Lab builds local AI systems using open-weight models in GGUF format, with llama.cpp and vLLM runtimes, and CUDA acceleration where GPUs are available.
What is a RAG system and does Haal Lab build them?
RAG (Retrieval-Augmented Generation) is an architecture that grounds language model responses in your own documents. A RAG system retrieves relevant passages from a knowledge base, then feeds them to the LLM as context. Haal Lab builds production RAG systems with hybrid retrieval (BM25 + dense embeddings), cross-encoder reranking, and source attribution.
What is BGE-M3 and why does Haal Lab use it?
BGE-M3 is a multilingual embedding model that produces dense, sparse, and ColBERT-style representations in a single pass. Haal Lab uses BGE-M3 for production retrieval because it handles multilingual corpora (such as legal documents across jurisdictions) and supports multi-vector indexing for higher recall.
Can Haal Lab deploy AI on air-gapped infrastructure?
Yes. We build air-gapped deployments for regulated environments — healthcare, finance, government, and legal. The entire stack (models, runtime, retrieval layer, application) runs inside your network with no outbound calls. We use offline model registries and version control to keep the system maintainable.
What is AI infrastructure engineering?
AI infrastructure engineering is the practice of building the serving, scaling, and observability layer that makes AI systems run reliably in production. Haal Lab builds infrastructure around vLLM, Triton, and Kubernetes — including GPU scheduling, batching, memory tuning, and evaluation-driven CI/CD for prompts and models.
How long does an AI engagement with Haal Lab take?
It depends on scope. A focused prototype can ship in 4–6 weeks. A production system with infrastructure, evaluation, and observability typically takes 3–6 months. Discovery (1–2 weeks) gives us enough context to give you a concrete timeline before any commitment.
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