Futurelab builds AI for data analysis: plain-language questions over warehouse, ERP, CRM and spreadsheet data, answered with a chart, a written answer and the query that produced it. A semantic layer holds your metric definitions, access rules are respected, and reporting, anomaly alerts and feedback analytics run on the same foundations.
AI data analytics
We build AI for data analysis that lets leaders and teams ask questions of their business data in plain language, and get a chart, an answer and the query behind it, using your definitions and your access rules.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at PepsiCo · ITC · SBI · Syngenta
Instead of waiting days for a report, people ask the question they actually have. Pick one to see a sample answer, the chart and how it was worked out.
Illustrative answer: Down 12% against plan. Two distributors in Lucknow and Kanpur account for most of the gap, after a stock-out on the 500 ml pack in the second week.
Illustrative answer: Freight is 18% over budget, driven by diesel prices and more part-load trips. Marketing and travel are within 3% of plan.
Illustrative answer: Nagpur and Guwahati take over two days on average from order to dispatch, against a network average of 1.1 days.
Illustrative answer: Repeat purchase rose from 22% to 29% among customers who received the campaign, against 23% in a comparable group who did not.
Futurelab builds AI products that turn raw signals into answers. Client analytics builds start from what we learned shipping these; status is shown as it stands today.
Turns social conversation into analysis: sentiment, themes, creators and how influence moves.
Gathers, analyses and summarises market and competitor research with sources kept, now in beta.
AI-generated insight from survey responses, without digging through raw data.
AI data analytics is worth building when leaders wait on analysts for routine questions, and the same reports are rebuilt every week.
Managers get answers to everyday questions in minutes, without raising a ticket or waiting for the next review deck.
Routine pulls and weekly reports move to AI, so your data team spends its time on the analysis that changes decisions.
Everyone works from the same definitions of revenue, margin and active customer, so meetings stop debating whose number is right.
Every answer shows its working An AI that answers questions about your numbers must show the query it ran, the definitions it used and the data it looked at. If it cannot, nobody should act on it.
Each one sits on the data you already have, such as your warehouse, ERP, CRM, spreadsheets or BI tool, and respects who is allowed to see what.
Plain-language questions over your business data, with charts and answers.
Weekly and monthly reports written for you, with the story behind the numbers.
Be told when something unusual happens, before the month closes.
Make sense of reviews, surveys, tickets and call notes at scale.
Four steps, each with a clear output, so you can test answers against numbers you already know before anyone relies on them.
List the questions leaders and teams ask most, find where the data lives and agree the definitions that matter.
You get: A question set and metric definitions
Connect the sources, build a semantic layer of your metrics and set access rules by role.
You get: A working assistant on your data
Test it against questions with known answers and your existing reports, and fix definitions and joins until it matches.
You get: An accuracy check against your reports
Launch to a first group of leaders, track the questions asked and add new data and definitions as demand grows.
You get: A live assistant with owners
Embedded deployment with the full advisory team—custom AI tools that shortened planning cycles and improved coordination.
Technology leadership and product delivery—from AI Sachiv at state scale to meeting intelligence and learning platforms adopted as a digital public good.
AI data analytics lets people ask questions of business data in plain language and get answers, charts and explanations, instead of waiting for an analyst to build a report. It also automates routine reporting and flags unusual changes.
You ask a question, the AI translates it into a query over your data using agreed metric definitions, runs it with your access rights, and returns a chart and a written answer together with the query, so anyone can check the working.
Every answer shows the query and definitions it used. Before launch we test against questions with known answers and your existing reports, and we keep reviewing the questions people ask after launch.
Data warehouses such as BigQuery, Snowflake and Redshift, databases, ERP and CRM systems, spreadsheets and BI tools such as Power BI and Tableau.
No. Access rules from your systems are applied to every question, down to rows where needed, so a regional manager sees their region and finance sees finance.
No. Dashboards still track the metrics you watch every day, and analysts still do the deep work. AI handles the ad-hoc questions and routine reports that fill the gaps between them.
Tell us where your data lives and the questions your leaders keep asking. We will come back with a practical view of what to connect first.