Field Notes on Practical AI
Honest, opinionated writing about agentic AI, RAG, document intelligence, voice AI, and predictive analytics — what works in production, what does not, and how to tell the difference before you spend.
Production over demos
We are interested in systems your teams use on a Monday morning — not screenshots that impress on stage. That mindset drives our architecture choices and how we scope every engagement.
Outcomes before models
We start with the business outcome, the workflow, and the data. Model and vendor selection follow that, not the other way around. This keeps spend honest and timelines realistic.
Human-in-the-loop by default
AI systems should make people better at their jobs, not replace judgement. Approval points, audit trails, and graceful fallbacks are first-class design constraints for us.
Latest Articles
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How AI Agents Can Automate Repetitive Business Workflows
Where classic automation stops and where agentic systems start — combining reasoning, tools, and guardrails to handle the parts of work that need judgement.
RAG vs. Chatbot: What Businesses Should Actually Build
Most teams ask for a "chatbot" when they really need a retrieval system. A practical guide to choosing between RAG, conversational AI, and agentic workflows.
How Document AI Reduces Manual Operations Work
Identifying the document workflows where AI moves the needle, how to evaluate accuracy honestly, and how to design exception handling that protects the business.
From AI PoC to Production: What Companies Often Miss
Observability, evaluation, security, fallback logic, and stakeholder design — the unglamorous work that turns an impressive demo into a system your business depends on.
Why Human-in-the-Loop Matters in Enterprise AI
Approval points, audit trails, and graceful fallbacks are not afterthoughts. How to design AI systems that augment human judgement instead of replacing it.
Practical AI Use Cases for Small and Mid-Sized Businesses
A pragmatic shortlist of AI use cases that work well at SME scale — with realistic effort, data requirements, and expected outcomes.