Thought leadership and technical depth
Do you actually need a vector database?
Vector databases are the default answer for AI search — and often more than a project needs. When a dedicated vector store earns its place, and when your existing database will do.
What an AI feature really costs to run
A model call costs fractions of a cent — until you multiply it by real traffic. The costs that surprise teams are predictable if you look for them before launch, not after the first invoice.
Prompt engineering is not the hard part
Clever prompts get the attention. The things that actually decide whether an AI feature works in production are less glamorous — context, evaluation, guardrails, and cost.
RAG in production: retrieval is the hard part
Retrieval-augmented generation demos in an afternoon and disappoints in production. The model is rarely the problem — the retrieval is. What separates a demo from something people trust.
Adding AI to a product you have already shipped
You do not need to rebuild your product to add AI to it. A grounded approach to shipping AI features into an existing system — starting where the value is, and keeping the parts that need to be reliable, reliable.
What multi-tenant AI actually costs at 1M+ users
The infrastructure realities behind AI features that scale.
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