These are illustrative scenarios based on common SMB operations — not specific past clients. Each shows the bottleneck, the architecture we'd reach for, and the tradeoffs behind the call.
Support team answered the same product questions every day while customers waited hours for replies.
RAG assistant trained on the product docs and past tickets. Citations on every answer; unanswered queries route to a human with full context.
Custom retrieval and evals matter more than the model — that's why a focused build beats a generic chatbot.
Operations team spent half their day moving data between tools and writing the same updates.
An agent with a small set of tools — read CRM, draft updates, send for human approval. Every action is logged and reversible.
The win wasn't a smarter model — it was a tight scope, real tools, and human approval on the things that matter.
Product team wanted an AI assistant inside the app but had no infrastructure to call models safely.
Model gateway with provider routing, cost limits, and prompt versioning. A small SDK lets product engineers ship AI features without re-inventing the plumbing.
Good AI infra is invisible. The team ships features; the platform handles failure, cost, and observability.
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