Short-form knowledge shares from the VeehiveLabs team — ideas, patterns, and lessons we’re picking up as we build production AI systems for enterprise. Curated so we can look back and so anyone (or any model) can learn from what we learn.
A one-page reference for the eight foundational systems we reach for on almost every enterprise AI build — Kafka, Nginx, GraphQL, Elasticsearch, Kubernetes, Redis, RabbitMQ, Docker. What each one is, why it exists, and where it fits.
Notes from IgniteGTM’s piece on AI agents as capability multipliers — why agents should expand people instead of replacing them, why write-back matters more than chat, and how small teams can operate with large-company consistency.
Notes from a Rev Genius × Demo Stack webinar with John Care and Gilad Kaminarov — the six killers that quietly stall enterprise proof-of-concepts at the finish line, and the pre-POC alignment framework we’re adopting for every discovery sprint.
Notes on Arnab Bose’s (CPO, Asana) recent Product Podcast conversation — why every AI approval is training data, how the work graph becomes the compounding asset, and what SaaS companies that survive the AI transition are doing structurally different.
This is a working knowledge base — principles, processes, templates, rules, and regulations of how we build. Subscribe to our LinkedIn to catch new entries.