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LLMs in B2B products — from idea to MVP
5 March 2026 · 5 min read
Planning AI features that actually save team time — instead of a decorative chat widget.
We start with the business problem: what should be faster, cheaper or more predictable after LLM rollout? Define metrics, inputs and model accountability (human-in-the-loop).
An MVP usually covers: one use case, integration with an existing system, prompt logging and quality evaluation on a document sample. Then we scale cost, caching and fine-tuning.
Code One brings product, backend and security together — so the MVP is board-ready and extensible, not slide-ware.
Key takeaways
- Start with the business problem and metric — not model selection.
- MVP = one use case + integration + quality evaluation on a data sample.
- Human-in-the-loop where errors have legal or financial impact.
- Prompt and model version logging is essential for audit and debugging.
- Plan inference cost in the product budget from day one.
In this article
Defining an AI MVP
One scenario (e.g. contract summary, ticket classification, onboarding assistant), clear model accountability, fallback on low confidence. Without that, a “chat on the website” fails board review.
Data and quality
Test set with expected outcomes, regression after prompt or model changes. Anonymisation before API calls when client policy requires it.
Scaling after MVP
Answer caching, smaller models for simple steps, batch processing, finops dashboard. Fine-tuning only when ROI is proven.
Next steps
- Describe one use case and KPI (e.g. −30% handling time).
- Pick data source and access rules.
- Build 30 examples for evaluation.
- Set monthly API budget and monitoring.