The Real Bottleneck in Shipping AI Products
A running list of what actually slows AI products down, from someone who has now shipped a handful of them.
The Model Is Usually Fine
Across ZaynichOne's agentic RAG, MedVoq's voice pipeline, and Klyq AI's marketing agents, the underlying model has rarely been the reason something did not work. The bottleneck shows up earlier: messy source data, unclear boundaries for what the AI is allowed to decide, and interfaces that do not make it obvious when the system is uncertain.
Grounding Is More Work Than It Looks
Every project that involved retrieval or extraction spent more engineering time on ingestion and structure than on the model call itself. Getting data into a shape the system can trust is unglamorous work, and it is also the work that determines whether the product is safe to ship.
The Approval Loop Is the Product
The pattern that shows up across every agentic system I have built, trading, marketing, pharma information, is the same: a human approval step is not a limitation bolted on for safety. It is usually the actual product, and the AI is the part that makes the human's job faster.
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