Engineering decisions for AI-native products
Practical field guides for founders and CTOs deciding what to build, who to hire, and what must be true before an AI feature carries real business responsibility.
Guides for decisions that shape the system
Each guide gives you questions, tradeoffs, and a usable decision framework rather than a list of tools.
Production engineering
When an AI prototype needs production engineering
A consequence-based readiness test, three transition paths, and the controls to add before real users depend on the system.
Read the field guide →Hiring AI talent
How to evaluate an AI-native engineer
A role-first interview loop for separating tool fluency from the engineering judgment required to ship and operate dependable AI features.
Use the scorecard →Delivery strategy
Staff augmentation or scoped build: which model fits the work?
A decision framework based on ownership, management capacity, uncertainty, and the shape of the result your business needs.
Choose the delivery model →Engineering workflow
A responsible Claude Code and Cursor workflow for production teams
A controlled path from task definition to reviewed, tested, observable change without treating generated code as trusted code.
Use the workflow →AI unit economics
The operating cost of an AI feature: what to measure before you scale
A practical cost model that follows complete workflows and accepted outcomes instead of treating token price as the whole bill.
Build the cost model →AI architecture
RAG or fine-tuning: choose by what needs to change
A decision framework for current knowledge, citations, permissions, consistent behavior, latency, evaluation, and hybrid systems.
Choose the architecture →Turn the decision into delivery
Discuss your brief
Bring the product question and the hardest constraint
Botmer can help scope the production path or match the engineering role to the work your team needs to own.