Cultivated AI · Mission Control · before we begin
Mission Control is a build. The more we know about your sources, your real questions, and your access before Session 1, the more we get built in the room instead of scoped in the room. Honest answers beat tidy ones.
What's your role, and which decisions should your data be informing?
We aim Mission Control at the decisions you actually own.
When you need a number today, how do you get it? Open Power BI / ask an analyst / wait for a report
This tells us how far you are from "just ask."
How long does a typical "can someone pull me X" usually take to come back?
The lag is the cost we're removing.
This is the raw material of Session 1's inventory. The more concrete, the better.
What lives in Power BI today, and who maintains it?
Your existing semantic models are the fastest on-ramp, if they're clean.
What does GobbleCube give you (city, store, platform), and how do you access it?
We need to know how that data gets out and on what cadence.
Where do production and supply-chain numbers live, and is either connected to anything else?
These are the sources you mentioned wanting to plug in. We'll plan for them.
Does all of this come together anywhere already, even partially? A warehouse / a master sheet / nothing yet
Tells us whether we're choosing a warehouse or improving one.
Name five questions you'd ask your data every week if the answer were instant.
These become the golden questions that anchor the whole build.
Which questions do you not bother asking today because it's too slow to find out?
The unasked questions are often where the impact hides.
When two reports disagree, which one wins, and why?
This surfaces the metric-definition conflicts the semantic layer has to settle.
Name one high-impact problem where faster, better data would change the decision. Wastage / fill rate / promo ROI / ...
We work one of these end to end in Session 3, live.
For that problem, what does "good" look like in 90 days?
We point the roadmap at a result you'd recognise, not a demo.
Who should and should not see what, across cities, teams, and finance? Any sensitive data?
We inherit access rules from the warehouse, but you decide them.
What would have to be true for you to act on an answer from an AI, not just a human analyst?
That answer defines the trust surface we build in Session 2.
Ask ChatGPT or Gemini "best [your category] in India" right now. Does Baker's Dozen show up?
Do it before the workshop. Whatever you find is the starting line for Session 4.
Where do your shoppers actually discover and vet products? Reddit / YouTube / marketplaces / word of mouth
These are the sources AI answers lean on, so they're where we'll focus.
Who from the data side will be in the room, and can they arrange read access to the sources before Session 1?
Access sorted in advance is the single biggest thing that keeps Session 1 moving.
Anything else we should know to make these sessions productive?
Constraints, politics, past attempts. All useful.
Send your answers, and make sure the data owners can get us read access to Power BI, GobbleCube exports, and the production / supply-chain files. See the pre-requisites list.