AI Assistant over Nielsen Sales Data for a Global Beverage Company
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Client Overview
Our client, a global beverage company (name covered by an NDA), buys detailed retail sales data from Nielsen: tens of thousands of rows a month, dozens of columns, custom category codes, and methodological quirks you simply have to know about. In practice, two people on the analytics team understood this data. Every question from marketing or sales joined the queue for those two people, the answer came back days later as a slide deck, and much of the knowledge of how to read the data existed only in their heads. We built an AI assistant that answers questions straight from this data: with a number, a table, or a chart, always showing what the result was calculated from. An answer takes a minute, not days, and anyone in the company can ask for one.
Client Needs
Nielsen data understandable across the company
Answers in a minute, not a deck after a week
Analysts freed from routine questions
Every answer with its source
The company needed marketing and sales people to be able to ask questions about the data themselves, without knowing the file structure and without waiting for the analytics team. The answers had to be trustworthy: based only on the data, with the source of every number visible, because budget and promotion decisions are made on top of them. And the analytics team was to be relieved, not replaced.
Services Provided
Putting the data in order: We moved the data from Nielsen exports out of Excel and into a database, with automatic loading on every new delivery and quality checks at the door.
Writing down the knowledge about the data: The structure, the relationships between metrics, and the methodological quirks, previously known only to the analysts, were described once, in a form the AI understands. This description layer is maintained together with the data.
An assistant that answers from the data: A question asked in plain language is translated into a controlled query against the database. The assistant does not guess: it calculates on the data and answers with a number, a table, or a chart.
A source with every answer: Each result shows which data and which period it was calculated from, so an answer can be verified before it lands in a board presentation.
An analyst in the loop: Questions the assistant cannot safely translate into the data go to the analytics team. Each such case extends the data description, so over time the assistant answers more and more on its own.
Rollout in the teams: Marketing and sales got the chat in the tools they already use, along with training on what the assistant can and cannot do.
Scope of Work
We delivered the assistant end to end, from raw data files to a tool used daily by business teams.
Workshops with the analytics team to capture the structure, metric definitions, and quirks of the Nielsen data before any code was written.
Building the data pipeline: automatic loading of new deliveries into a database, with validation and quality checks replacing manual Excel handling.
Creating the data description layer that lets the AI translate business questions into correct, controlled queries.
Building the assistant itself: plain-language questions in, answers with numbers, tables, and charts out, each with its calculation source.
Validating the assistant against the analysts' own answers before opening it to marketing and sales teams.
Setting up the analyst-in-the-loop process, so unusual questions reach a person and each case improves the data description.
Technologies Used
Development Process
We started by working with the analytics team: their knowledge of the data, collected over years, was the most valuable thing to preserve. The assistant first ran alongside the existing process, with its answers compared against the analysts' answers, until the agreement was good enough to open it up to the wider company. The integration itself followed the way we connect AI agents to any existing environment. The assistant reaches the data only through a controlled set of tools and can do no more than it needs to answer questions, and every answer can be traced back to the query and the data it came from. Nothing in the company's existing systems had to change: the assistant showed up in the tools the teams already use every day. The rollout was designed for independence: the data description layer is maintained together with the data, unusual questions reach an analyst, and each such case teaches the assistant to answer more on its own.
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