Nebula Block Blog

Enterprises keep asking the same question in slightly different words: should we fine-tune a model on our data, or should we build retrieval on top of a general-purpose model? The honest answer is that fine-tuning is rarely the right first move for enterprise knowledge problems, and understanding why comes down

Most enterprise AI programs start in the same place: retrieval-augmented generation, pointed at internal documents, answering employee or customer questions with grounded, cited responses. It's a good starting point because the failure mode is contained — a wrong answer is embarrassing, not catastrophic. But nearly every team that ships

Learn how to deploy multi-agent AI systems on reserved GPU instances. This guide covers architecture design, cost optimization strategies, horizontal and vertical scaling, agent orchestration frameworks, and best practices for running reliable, production-grade multi-agent workloads without overspending on compute The Core Problem & Solution * The Challenge: Single-agent systems quickly hit

Most teams pick AI infrastructure based on price and performance. Then legal gets involved, and the conversation changes fast. That's because data residency AI infrastructure decisions aren't just technical — they're legal ones. Where your training data sits, where your model runs, and where your

Building multi-agent AI platforms? Discover the 4 core architectural patterns -from Orchestrators to Critic-Actor loops - and learn how leveraging reserved GPU instances can slash scaling costs by up to 20x while permanently eliminating real-time inference bottlenecks. Architectural Patterns - Compute Infrastructure Strategies The Core Problem & Solution * The Challenge:

Most explanations of an ML workflow stop at the model card. That's the easy part. The real work — turning raw files into usable data, training a model on actual hardware, checking it's good enough, deploying it somewhere your product can call, and watching what happens once

If you're building an AI product in Canada, you've probably hit this problem: your inference API lives in the US, your GPU rental is on a US hyperscaler, and your storage bucket sits wherever egress pricing was cheapest that month. Each piece works fine on its

Privacy Impact Assessments were designed in a world where data risk lived in databases. You mapped what personal data you collected, where it was stored, who could access it, and how long you kept it. Simple enough — if your tech stack was simple. AI workloads broke that model. And the

Retrieval-Augmented Generation (RAG) is one of the most practical AI patterns available right now. Instead of relying solely on what a model learned during training, RAG lets you attach your own documents, databases, and knowledge bases to the model — giving it accurate, current, proprietary information to work with. The catch:

There's a problem in AI infrastructure that doesn't get talked about enough: power. Everyone's racing to get their hands on NVIDIA's new Blackwell Ultra GPU, the B300, and for good reason — it's a serious leap forward for FP4 inference and

No computer science degree required — just a clear explanation of one of the most important concepts in AI task orchestration If you've spent any time reading about AI agents, workflow automation, or orchestration platforms, you've probably encountered the term "dependency graph." It gets used