Technical reference documents and free practical tools for building AI solutions that work.
A practical technical reference for a catalog-grounded agent that understands customer needs, asks contextual questions and returns validated product recommendations.
A practical technical reference for discovering search opportunities, grounding bilingual content in approved evidence, validating every revision, publishing safely and monitoring performance.
An implementation-grade reference for multilingual store search that interprets natural language, calls grounded catalog tools, applies deterministic business rules, asks useful follow-ups and returns auditable ranked products.
Map your own quote process step by step: who does each step, how long it takes, and where AI genuinely helps.
Follow one request from start to finish and see how much of its life was real work and how much was waiting between steps.
Sort one week of customer questions into three types and know exactly which ones a system answers, which ones AI drafts for review, and which ones need a person.
Ten quick questions to answer before you hire. The result tells you whether the delay comes from headcount or from the way work flows.
Set clear approval limits for every request type, with a backup approver and a maximum response time, so small requests move the same day.
Separate three numbers people mix up: hours freed by automation, hours actually reused for better work, and real cash savings.
What to check before you automate invoice entry: volume, suppliers, formats, the exceptions that must reach the accountant, and a pilot on a small sample.
For every department: the activity metric to stop celebrating and the outcome metric that tells you whether automation actually helped.
Agree what must be included before a request moves to the next department, and log the exact reason whenever it comes back.
Run an AI pilot with an end date, a baseline and success criteria written up front, then decide: scale, adjust or stop.