Enterprise AI & Search Notebook
Why I've joined the OpenCrawling project as Lead Architect, and what I want the platform to solve for scale-out ingestion.
IBM Watson Explorer on prem options are being withdrawn from the market, what are your options.
Benchmarking vLLM against Ollama for a multimodal knowledge graph extraction task.
How to stay confident in your model choices when the landscape changes every 90 days
OpenClaw's explosive growth exposed a hard truth- while AI agent orchestration has become trivially easy, the security implications of giving those agents broad system access remain an unsolved—and underestimated—enterprise problem.
The Epstein Files exposed a KYC blind spot that no sanctions list can fix — here's what modern market intelligence infrastructure needs to do differently.
Principles for thriving in the age of AI, drawn from a leadership program built at MC+A.
Why most AI projects fail and how a small, well-chartered project called Purrview succeeded.
Why context matters in RAG systems and how lack of it leads to failures.
Reflections on vibe coding, AI-assisted development, and what it means for software engineers.
Debunking the viral claim about OpenAI's model refusing to shut down.
How multi-agent systems can communicate effectively using an AI enterprise service bus.
Successful AI projects are focused on outcomes, not simply outputs. Using BDD to bridge that gap.
Integrating Adobe Experience Manager with a vector database for improved search and RAG.
How fine-tuned models and vision AI outperform general-purpose LLMs for objective coding in eDiscovery.
How to go beyond vector search with custom scoring, knowledge graphs, and learning to rank for job matching.
Understanding the differences between sparse and dense vectors and their applications in search.
How insight engines use your users' behavior to dramatically improve search and conversions.
A simple defect reporting template that drastically reduces triage time.
How search results should drive the next best action using signals and learn to rank.
A wish list for Google Cloud Search based on years of enterprise search experience.
A maturity model for enterprise search, identifying patterns and steps to improve.
Reflections after 11 years with the Google Search Appliance and what comes next.