Building High Impact AI Systems
Great AI systems don't start with models—they start with a clear problem and a path to impact. Here's how we think about it at Neatcraft.
Start with the outcome
Before choosing an LLM or a vector database, define what success looks like. Is it faster support resolution? Fewer manual reviews? Better search relevance? Tie the system to a metric that matters to the business.
Design for reliability first
In production, latency and correctness matter more than benchmark scores. Prefer:
- Retrieval you can debug — Use structured metadata and filters so you can trace why a document was retrieved.
- Fallbacks — When the model is unsure or the pipeline fails, have a clear fallback (e.g., human queue or cached answer).
- Observability — Log inputs, retrieval results, and model outputs so you can improve over time.
Iterate with real users
Ship a minimal version, measure behavior, and refine. High-impact systems usually go through several iterations before the architecture stabilizes. Keep the feedback loop short.
Building meaningful AI systems is as much about product and operations as it is about models. Focus on impact, reliability, and iteration, and the technical choices become clearer.