AI

A practical guide to writing prompts that produce useful, controlled outputs for real business work rather than clever toy demos.

A business-friendly explanation of retrieval-augmented generation and why it matters when your AI must work from company knowledge.

How to use AI to extract, validate, and route invoice information while keeping finance controls, approval logic, and exception handling intact.

A practical guide to turning meeting transcripts into useful outputs such as decisions, action items, and follow-up notes.

A plain-English guide to deciding which business data should not be sent to public LLM endpoints and what safer alternatives exist.

A realistic view of where AI is useful in accounting work and where human controls, policy interpretation, and exactness still dominate.

Opening — Why this matters now Search systems have grown fluent, but not necessarily intelligent. As enterprises drown in text—contracts, filings, emails, reports—the gap between what users mean and what systems match has become painfully visible. Keyword search still dominates operational systems, while embedding-based similarity often settles for crude averages. This paper challenges that quiet compromise. ...

Opening — Why this matters now Cosine similarity has enjoyed an unusually long reign. From TF‑IDF vectors to transformer embeddings, it remains the default lens through which we judge “semantic closeness.” Yet the more expressive our embedding models become, the more uncomfortable this default starts to feel. If modern representations are nonlinear, anisotropic, and structurally rich, why are we still evaluating them with a metric that only understands angles? ...

Opening — Why this matters now As AI systems inch closer to everyday human interaction, emotion is no longer a “nice-to-have” signal. It is a prerequisite. Voice assistants, mental‑health tools, call‑center analytics, and social robots all face the same bottleneck: understanding not just what was said, but how it was said. Speech Emotion Recognition (SER) has promised this capability for years, yet progress has been throttled by small datasets, brittle features, and heavyweight models that struggle to scale. ...

Opening — Why this matters now For decades, heuristic design has been a quiet tax on optimization. Every serious deployment of A* or tree search comes with a familiar cost: domain experts handcraft rules, tune parameters, and babysit edge cases. The process is expensive, slow, and brittle. Large Language Models promised automation—but until recently, mostly delivered clever greedy tricks for toy problems. ...