.#ad I ran a client pricing project using Loop in Teamily 2.0 with Teamily AI—and stayed the decision-maker. Scout researched the market. The pricing strategist turned that research into a recommendation. I reviewed it, changed one decision (a flat $49 price, no second tier), and the agent updated the... recommendation to reflect my input. Then I triggered the same saved Loop again with new inputs for another client, a boutique coffee roastery, and reviewed a different pricing recommendation. One reusable workflow, with agents doing the research and me making the final calls. Try Teamily AI here: #TeamilyAIshow more

@Teamily_AI Did the strategist push tiers at first

@Teamily_AI Agents research you decide that balance works

@Teamily_AI Staying hands on builds client trust

@Teamily_AI Curious about the second recommendation

@Teamily_AI What inputs did you tweak for the coffee place

@Teamily_AI This looks solid for client pricing work

@Teamily_AI The control piece is what stands out

@Teamily_AI What other client types work well

@Teamily_AI Does it generate full pricing docs

@Teamily_AI Curious if it handles service businesses too

@Teamily_AI Staying in control feels important

@Teamily_AI Sounds practical for freelancers

@Teamily_AI How detailed was the research phase

@Teamily_AI How fast does the scout pull market data

@Teamily_AI Would this fit product pricing as well

@Teamily_AI Reusing one loop for different clients is smart

@Teamily_AI Did you change much after the first recommendation

@Teamily_AI Agents research you decide that balance works

@Teamily_AI Simple enough for daily use

@Teamily_AI Would try this for my own clients