AI & ML product development
AI products fail less on model choice and more on the unglamorous parts: evaluation, guardrails, latency, and cost-per-request at scale. A team that has shipped LLM features knows the demo is the easy 20%.
What separates a good build from a costly one
Evaluation is the product
Without an eval harness you are shipping vibes. The teams worth hiring build measurement before they tune prompts.
Cost-per-call compounds
A feature that is cheap in the demo can be ruinous at 100k requests. Architecture should account for caching, routing, and model tiering early.
Guardrails and fallbacks
Prompt injection, hallucination, and timeout handling are table stakes for anything customer-facing.
Common questions
What does it cost to build an AI product?
AI/ML builds range widely — roughly $30k for a focused LLM feature to $150k+ for a data-platform-backed product. Inference cost is a separate ongoing line; design for it from the start.
Do I need to train my own model?
Almost never to start. Most shipped AI products wrap hosted models with strong retrieval, evaluation, and guardrails. Custom training is a later optimization, if ever.