Quiet day, useful routing work

System: build a simple voice and chat follow-up workflow for stale leads. Customer: small production companies, venues, agencies, and local service firms with unanswered inquiries. Offer: they send a lead sheet, receive a cleaned contact list, follow-up script, qualification questions, and a human handoff rule. Price: ¥55,000 setup, then ¥25,000 per monthly refresh. Existing assets: Grey Group OS, Goodsense sales patterns, SET production discipline, and Street Attack Japan sponsor follow-up logic. AI workflow: ChatGPT drafts scripts and lead categories, an OpenAI API pipeline prepares messages, and a human approves outbound contact. First action: export 20 old inquiries and write one revival script today. Repeatability: the same sheet, script, and handoff template can run monthly. Effort: one hour. Expected value: recovered leads and paid setup work from clients who already have demand but poor follow-up.

System: create a paid proposal and deck upgrade workflow. Customer: founders, agencies, creators, and Japan-facing businesses that need a sharper commercial pitch quickly. Offer: they provide a rough brief or deck and receive a tighter narrative, revised section order, missing-asset checklist, and client-ready wording. Price: ¥33,000 per upgrade. Existing assets: Grey Group OS proposal habits, Goodsense positioning work, SET project framing, and Adrian’s Japan-English business workflow. AI workflow: Codex builds the repeatable formatter and checklist utility, while ChatGPT rewrites the commercial narrative. First action: choose one old proposal and turn it into the before-and-after sample. Repeatability: the same prompt chain and checklist can process every new pitch. Effort: one hour. Expected value: fast service revenue and stronger conversion on proposals Adrian was already preparing.

What to build: a lightweight repo log that records each agent run, model lane, task type, human review time, and whether the pull request was accepted. Why now: GitHub is explicitly comparing raw API access with Copilot’s workflow, policy, and harness value, so the decision needs evidence. Effort: half day. Expected impact: lower waste and better model routing for coding work. Dependencies: active GitHub repositories, regular Copilot or API usage, and a simple review habit after each agent run.

What to build: a small internal tool that turns a rough project brief into a proposal outline, asset checklist, timeline assumptions, and unresolved questions. Why now: OpenAI says its creative team uses Codex to build custom creative tools and prototype faster, which matches this kind of narrow production utility. Effort: half day. Expected impact: faster proposal prep and more consistent output quality. Dependencies: an existing brief format, Codex access, and one finished proposal to use as the reference standard.