AI Resource Hub
Practical guides, research, frameworks and templates from the team that designs and ships AI products at Neura Dynamics. Use them to work out where AI fits in your business, and how to build it well.
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The State of Agentic AI in Operations, 2026
Where AI agents are delivering value in operations teams today, where simple workflows still win, and what it takes to run agents reliably in production.
View ResourceRetrieval-Augmented Generation (RAG): A Practical Guide
How RAG gives a language model access to your own information, each stage of the pipeline, and the design choices that decide answer quality.
View ResourceAI Use-Case Prioritization Scorecard
Score up to three candidate AI use cases on value, feasibility, data readiness, risk and effort, adjust the weights, and see which one is the strongest candidate for a pilot.
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Zenva: AI Customer Service for Smarter Ecommerce Support
How we built an AI customer support platform for Shopify merchants that brings emails, order data and store policies into one workflow, with human agents reviewing every reply.
View ResourceExplore All Resources
The State of Agentic AI in Operations, 2026
Where AI agents are delivering value in operations teams today, where simple workflows still win, and what it takes to run agents reliably in production.
How Large Language Models Work: A Plain-Language Guide
What an LLM actually does when it answers a question, why it sometimes gets things wrong, and which tasks it is well suited to.
Tokens and Context Windows: What They Mean for Cost, Speed and Quality
How tokens and context windows work, how they drive cost and latency, and practical ways to fit the right information into a prompt.
Embeddings and Vector Search Explained
What embeddings are, how vector search finds related content by meaning, and how to decide whether you need a dedicated vector database.
Retrieval-Augmented Generation (RAG): A Practical Guide
How RAG gives a language model access to your own information, each stage of the pipeline, and the design choices that decide answer quality.
RAG vs Fine-Tuning: How to Choose the Right Approach
The difference between adding knowledge with retrieval and changing behaviour with fine-tuning, and a simple way to decide which one your problem needs.