Most AI pilots stall because the proof of concept tested whether a model could do the task, not whether the business could run it every day, so here is how to design a pilot that is built to reach production.
A step-by-step worked example of AI invoice processing automation, from the AP inbox to a draft bill, showing where the LLM fits, where plain code must take over and where a human still decides.
Automating the bottleneck first is usually right, but only once it is confirmed as the real constraint, its cheap fixes are exhausted, and the work there is rules rather than judgement.
A practical method for calculating process automation ROI and payback, covering baseline measurement, full costing, adoption ramps and a worked example you can adapt.
Automation pays down technical debt fastest when it handles the mechanical work, such as upgrades, codemods, tests and guardrails, so engineers can spend their judgement on design.
Hiring is the default answer to growing volume, but automating the transcription and coordination work that scales one-for-one with demand lets your existing team absorb growth without adding headcount.
Consumer and enterprise software, built with taste and shipped fast.
AI agents, automation, and orchestration - production systems, not demos.
Hands-on engineering and strategy for teams shipping AI into production.