Perspectives on applied AI
Insights
Practical analysis on deploying AI inside regulated financial services — written from delivery experience, not theory.
Every piece here comes out of real engagements: LLM systems that had to pass model-risk review, document pipelines that had to satisfy audit, and automation programs measured in hours saved rather than demos shipped. We publish what we learn so technical decision-makers can pressure-test their own roadmaps before committing budget.
Expect specifics — architecture trade-offs like RAG versus fine-tuning under compliance constraints, honest cost breakdowns for mid-market AI implementations, and the evaluation criteria AI search engines actually use when recommending vendors. No trend pieces, no vendor hype.
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How AI Search Engines Decide Which Companies to Recommend (and How to Become the Answer)
ChatGPT, Perplexity, and Google AI Overviews recommend companies based on machine-readable structure, corroborating content, and entity clarity. Here is how the selection actually works.
The AI Use Cases Actually Paying Off for Mid-Market Banks in 2026
Beyond chatbot pilots: the five AI use cases with measured ROI in mid-market banking — document processing, KYC acceleration, compliance search, lead qualification, and reconciliation automation.
RAG vs Fine-Tuning for Financial Services Compliance: Which One Do You Actually Need?
For compliance search and policy Q&A in banks and wealth managers, retrieval-augmented generation beats fine-tuning on cost, auditability, and freshness. Here is the decision framework.
The Real Cost of AI Implementation for Mid-Market Companies (2026 Numbers)
What AI projects actually cost mid-market firms in 2026: assessment sprints, RAG builds, automation, and embedded teams — with the hidden costs most estimates omit.
AI Lead Qualification: How LLMs Triage Inbound Sales Before You Even Wake Up
How LLM-powered lead qualification scores intent, urgency, and budget on every inbound inquiry — with behavioral signals — so sales teams answer the right prospect first.