"We pay for ChatGPT and nobody uses it": a 2-week AI adoption sprint.
The problem: a company buys ChatGPT Team or Gemini licences, runs one demo, and three months later only two people log in. Staff don't know what to use it for in their actual job.
The fix: short interviews to find the 5 tasks each role repeats every week, copy-ready prompts for exactly those tasks, and two hands-on sessions where people use them on real work.
Includes: a use-case library by role, a one-page "what not to paste into AI" guide, and a 30-day usage check-in. (Sample build to show the approach.)
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Bulk AI product copy inside Google Sheets: no more copy-paste hell.
The problem: a store with 500 products pastes each one into ChatGPT, copies the answer back, and loses a week. Formats drift, details get invented.
The fix: a Google Sheet where each row holds the product facts, and AI fills in title, description, bullets and SEO meta in a fixed format. A QA column flags anything that mentions a spec not in the source row.
Result: a full catalog drafted in an afternoon, with a human check built in. No code. (Sample build to show the approach.)
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Claude that writes in your brand voice, not "AI voice".
The problem: teams try Claude or ChatGPT for product copy and emails, but every draft sounds the same: "elevate", "seamless", "unlock". Editing takes longer than writing from scratch.
The fix: a Claude Project loaded with a one-page voice guide, 10 real examples of copy the brand loves, a banned-phrase list, and prompt templates for each content type.
Result: drafts that need a light polish, not a rewrite, and anyone on the team gets the same quality. (Sample build to show the approach.
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Custom GPT that stops ChatGPT from making up your policies.
The problem: support teams paste questions into ChatGPT and get confident answers that contradict their real refund, shipping or warranty rules.
The fix: a Custom GPT grounded only in the team's SOP docs. It quotes the exact policy section, says "not covered in our docs" instead of guessing, and flags edge cases for a human.
Includes: doc cleanup so the GPT can actually read them, instruction set, and a 30-question test sheet to catch wrong answers before rollout. (Sample build to show the approach.)
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