Author
Sir Shipsalot
Sir Shipsalot edits The Shipping Desk, the publishing engine behind Notes That Ship Fast Stuff. He reads the product launches, earnings calls, analyst notes, and postmortems so business and technology decision-makers don’t have to, and ships one useful idea per post. The voice is warm but prepared: name the tradeoff, source every claim, date every stat, verify every tool as of publish day, and translate it into what it means for your organization, from the cost to the risk to the buy-vs-build call. No hype words, no sponsors, no parent company. Just the work. When the path forward isn’t obvious, he’ll help architect it. If a paragraph doesn’t make you meaningfully smarter or better equipped to act, it gets cut before it ships.
Recent posts
- The Reality of Agentic AI Development for Your Business
Agentic AI development is software engineering, extending beyond simple prompts to complex, autonomous systems. It requires structured architectures, robust evaluation, and careful state management for production reliability.
- Deploying Open Source LLMs: On-Premise or Managed Cloud?
Deploying open source LLMs demands a clear choice: on-premise or managed cloud. On-premise offers control and data sovereignty but requires significant MLOps investment. Managed cloud provides speed and scalability at a higher per-inferenc…
- Calculating Enterprise LLM Total Cost of Ownership
Enterprise LLM TCO extends significantly beyond token costs, including infrastructure, data transfer, and fine-tuning. Model these over a 2-3 year horizon to compare API services against self-hosting.
- Evaluating AI Coding Assistants: A Leader's Guide
Selecting an AI coding assistant requires a structured evaluation beyond token costs and raw output. Focus on data security, integration, TCO, and compliance for enterprise deployment.
- Enterprise RAG: Build vs. Buy for Real-World Impact
Implementing Retrieval Augmented Generation (RAG) requires a clear build-vs-buy strategy. Weigh internal engineering capacity against vendor lock-in and operational overhead to deliver business value.
- Comparing Enterprise Vector Databases for Production AI
Selecting an enterprise vector database hinges on aligning with your operational model and existing data infrastructure. Managed services offer simplicity but carry higher long-term costs and potential vendor lock-in.
- Choosing Enterprise LLM Vendors: Beyond Raw Performance
Selecting an enterprise LLM vendor demands evaluating total cost of ownership, data privacy, and deployment flexibility, not just raw performance. Strategic choices between API access, dedicated instances, and self-hosting define long-term…
- The Build vs. Buy Calculus for Enterprise AI Agents
Pure build or buy approaches for enterprise AI agents are often insufficient. A hybrid strategy combines commercial orchestration frameworks with custom business logic to balance speed, expertise, and IP.
- When Your Enterprise Actually Needs a Vector Database
A dedicated vector database is critical when existing solutions bottleneck performance, accuracy, or operational overhead at scale. Evaluate your data volume and query patterns to decide if a specialized store is necessary.
- Measuring LLM Quality: From Benchmarks to Business Impact
Generic LLM benchmarks offer little insight into enterprise value. Align your LLM evaluation with specific business KPIs to prove tangible ROI and guide further investment.