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Cultivated AI · Part Two

Mission Control

One screen. Ask the business.

We turn scattered telemetry into one nervous system, so leadership can ask the business a question in plain language, and act on the answer.

The workshop.

FormatFour two-hour sessions. Eight hours in all.
CohortSmall. Leadership plus the people who own the data.
CoverageOne warehouse, a semantic layer, plain-language querying, and showing up in AI answers.
OutcomeA pipeline plan, real questions answered, and a roadmap.
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The idea · why it works

A business you can ask.

Today your numbers live in Power BI, GobbleCube, production sheets, and supply-chain files. Scattered data can't be asked. Mission Control brings it into one place that knows what your words mean.

Aggregate

Every source, city, store, and platform feeding one governed warehouse on a daily automated dump.

Define

A semantic layer that encodes what fill rate, active SKU, and incremental lift mean, once, for everyone.

Ask

Leadership asks in plain English, cross-checks, and acts on a high-impact problem, no analyst in the loop.

Show up

The same AI lens turned outward, so the brand appears when shoppers ask AI what to buy.

Why the order matters: across published benchmarks, plain text-to-SQL on a real warehouse is right about a third of the time. Define your metrics once in a semantic layer and the same questions get answered correctly near every time. The pipeline is the project. The chat is the easy last mile.

Where your data climbs.

  1. ScatteredNumbers in many tools. Leaders wait on analysts for every answer. The rung to escape.
  2. CentralisedOne warehouse, fed daily. A single source of truth everyone trusts.
  3. DefinedA semantic layer encodes your business. Answers become trustworthy, not just fast.
  4. ConversationalLeadership asks and acts. The brand shows up in AI answers, too.
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The agenda · session by session

The four sessions.

Eight hours, split into four two-hour sessions, built around your real data.

  1. Session 1 · 2 hrsOne place for everything

    Inventory every source (Power BI, GobbleCube, production, supply chain). Design the central warehouse and a daily automated dump that lands it all in one place. Ask the first real question and see what's missing.

  2. Session 2 · 2 hrsTeach the machine your business

    Define your metrics once in a semantic layer. List the questions leadership actually asks, and seed an example library from queries your team already trusts. Build the trust surface that makes answers safe to act on.

  3. Session 3 · 2 hrsAsk the business

    Query the nervous system in plain language, where you already work. Then apply the AI lens to one high-impact problem end to end, with governance so the right people see the right data.

  4. Session 4 · 2 hrsShow up in the answer

    Ask AI "best protein chips in India" and see who it names. Learn how AI answers pick brands, build an AEO playbook for Baker's Dozen, and leave with a phased roadmap and owners.

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The takeaways

What you walk out with.

  1. 01

    A single source of truth

    A central-warehouse plan and a daily automated dump that ends the hunt across tools.

  2. 02

    A business that answers itself

    A semantic layer and a golden-questions list, so leadership can ask in plain language and trust what comes back.

  3. 03

    The AI lens on real problems

    One high-impact problem worked end to end with data and AI, as the template for the next.

  4. 04

    Show up when shoppers ask AI

    An AEO playbook so Baker's Dozen appears in AI answers, not just in search.

  5. 05

    A phased roadmap

    Owners, metrics, and sequence, the concrete path to India's most AI-forward D2C organisation.

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