Articles, experiments, and AI insights from the Haal Lab team. We write about the systems we ship — what worked, what didn't, and the reasoning behind the choices.
haal-lab.solutions / research
Jun 21, 2026
14 min
A field-tested walkthrough of the runtimes, quantization formats, and retrieval layers we deploy when an organization needs open-weight LLMs running entirely on their own hardware.
May 30, 2026
11 min
Cross-encoder rerankers are now table stakes in RAG pipelines, but they are not free. We break down the latency, recall, and cost tradeoffs across four production deployments.
May 12, 2026
9 min
Notes from deploying BGE-M3 across a multilingual legal corpus — what worked, what broke, and how we tuned dense, sparse, and ColBERT-style representations for real workloads.
Apr 18, 2026
13 min
Why we treat prompts and model choices like code — versioned, reviewed, and gated by automated evaluations on every commit. The harness we use, and how to bootstrap your own.
Mar 22, 2026
10 min
A short catalog of agent topologies — router, planner-executor, critic — with notes on which ones held up under real tool-call latency and failure modes.
Feb 14, 2026
12 min
Building AI on your own hardware eliminates some risks and introduces others. A practical threat model for on-prem LLM systems, including model supply chain and prompt-injection surfaces.
No newsletter spam, no growth funnels. Just technical writing on the AI systems we are actually building — sent when there is something worth reading.
Where to find our technical writing and what we cover.
Yes. We publish technical articles on the systems we build — what worked, what didn't, and the reasoning behind the choices. Topics include local LLM inference, reranking tradeoffs, BGE-M3 in production, evaluation-driven CI, agent orchestration patterns, and private AI threat modeling.
Our research articles are published on the Research page at haal-lab.solutions/research. We publish when we have something to say — no newsletter spam, no growth funnels.