AI-assisted analytics

More answers, fewer requests. AI your data team can trust.

Grounded Analytics helps data teams adopt AI-assisted analytics: AI that writes queries, creates analyses, and builds ML models, with quality checks built in, so your team answers more questions without hiring. A simple plan and hands-on help to fast-track your data needs.

Results

Solved before, for a real analytics team

In under a year, a mid-size analytics team went from no AI to AI on every project.

4xanalytics team productivity
90%fewer ad-hoc requests once the data chatbot was live
Near-instantanswers to simple questions from the data chatbot

Simple questions that the chatbot can answer are near-instantaneous. The team gets more time for complex, strategic projects, which also get a major productivity boost (4x).

Productivity measured in story points: estimates of the hours the work would take without AI, using the same system as before AI.

What is AI-assisted analytics?

Analytics where AI does the heavy lifting (writing queries, documentation, analyses and ML models) and people make the calls: they decide which questions matter, check the results and are responsible for data governance.

The Grounded approach

The data analytics team is the foundation

AI is only as good as the data behind it. The analytics team turns raw data into clean, agreed, standard tables, and everyone else builds on them.

There are two ways to set the team up: a lean team with an off-the-shelf AI tool, or a full AI-assisted team. Both work well. The right choice depends on your size and how much you want to customize.

Ready for AI agents

Creating AI agents unlocks the real power

A solid data foundation lets AI agents act, not just answer

Agents watch your data, spot problems, identify opportunities, and take the next step, but only on data that is clean, agreed and checked.

Examples

  • Data chatbotAnswers business questions in Slack or Microsoft Teams
  • Reduce customer churnDetects accounts at risk of leaving, such as falling usage or an approaching renewal, then drafts a check-in for the account owner and creates the follow-up task
  • Early identification of high-potential customersSpots customers with the potential to become big accounts, then assigns a dedicated contact and starts a white glove onboarding checklist
  • Flag fraud and unusual activityScans orders, payments and accounts for unusual patterns, then holds the order and opens a case with the evidence, for a person to decide

Agents act within the limits you set. Bigger actions wait for a person's approval.

Illustrative example: in a Slack-style chat, a stakeholder asks how many new customers were won last month by region. The data assistant answers with a bar chart, a table, and a list of the checks the answer passed.
Data chatbot: ask in Slack or Microsoft Teams, get a checked answer. Illustrative mock-up with a fictional company, people and figures. See the Microsoft Teams example and more on the AI agents page.

Not sure where to start?

You don't need to figure everything out first. A few short sessions are enough to kick-start your AI journey, so it takes weeks, not months.

Book a free 30-minute call to discuss where your team is today, and what the first step towards AI-assisted analytics could be.