Futurelab builds AI for retailers and FMCG brands: shelf photos read for gaps, share of shelf, planogram, price tags and competitor activity; demand forecasts by store and SKU; assistants for store staff and field reps; and customer insight from reviews, social and loyalty data. Our SalesEasy field assistant works in eight Indian languages.
AI for retail and consumer brands
We build AI for retailers and FMCG brands: shelf checks from a phone photo, demand forecasts that cut stockouts, assistants for store staff and field teams, and customer insight from what shoppers actually say. Built in India, for Indian retail.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at Coca-Cola · PepsiCo · ITC · Patanjali · Love in Store
A store visit used to mean a clipboard. Now a rep snaps the shelf and AI reads it in seconds. Pick what to look for and see it on the shelf.
Empty facings are found and matched to the product that should be there, so the rep can place an order on the spot.
Your facings are counted against competitors on the same shelf, and tracked visit by visit and store by store.
Products on the wrong shelf or in the wrong order are compared with the agreed planogram and flagged with the fix.
Missing or wrong price labels and offer tags are read and checked against the current price list and scheme.
New competitor packs, displays and promotions are spotted and shared with the team, so nobody hears about them last.
These are products Futurelab built for retail and consumer teams. Status is shown as it stands today.
Our field assistant for retail and FMCG reps: shelf photo scores, competitor sightings, store-level dashboards and voice input in eight Indian languages.
Social listening for consumer brands: what shoppers think, the themes they return to and the creators who move them.
Market and competitor intelligence: trends, audience insights and gaps in your category.
AI for retail is worth building when it closes the gap between what head office plans and what actually happens in thousands of stores.
Gaps on the shelf and in the stockroom are spotted earlier, and replenishment follows real demand rather than last month’s guess.
Every visit produces the same reliable audit, so managers see what is happening on shelves across regions, not just in the stores they visit.
Sales, shelf, competitor and customer signals come together, so pricing, promotions and range decisions rest on evidence.
Built for the trade, not the boardroom Retail AI only works if a rep in a kirana store and a manager in a modern trade outlet can use it in thirty seconds, in their own language, on a basic phone.
Each one connects to the systems you already run, such as your DMS, POS, ERP and loyalty platform, and is piloted in a handful of stores or one region first.
Make every store visit count, in general and modern trade.
Stock what will sell, where it will sell.
Understand shoppers and speak to each one better.
Answers and guidance for store teams, on their phones.
Four steps, each with a clear output, so you see results in real stores before you scale.
Spend time with reps, store staff and category teams, and pick the problem with the clearest cost, such as stockouts or poor shelf execution.
You get: A scoped use case and baseline
Connect sales, stock and store data, train on your products and packs, and build the tool for the people who will use it.
You get: A working tool on your products
Run it in a set of stores or one region, measure against the baseline and fix what does not work on the floor.
You get: Pilot results by store and region
Roll out region by region with training and support, and add the next use case on the same data.
You get: A live tool with owners and reporting
The workshop exposed us to the possibilities of AI in professional work setups and in the retail industry. It opened our eyes to practical applications we hadn't considered.
Aditya Goel, Co-Founder
A practical programme that made AI tools and workflows easier to apply.
Vikram Varde, Senior Director
Futurelab’s AI workshop sparked fresh thinking across our team and made emerging tech feel approachable and exciting. It set the foundation for Intelligent integration of AI into our work
Anju Joseph, Managing Partner
The conversations were clear-eyed, practical, and inspiring. Exactly what India's agri and food industry needs right now.
Sanjeev Asthana, CEO
13+ years in marketing and growth leadership at Google, Paytm, Airtel and Amul.
13+ years across business strategy and technology transformation at Amul, Wipro, PepsiCo and Mondelez.
The most valuable uses are retail execution (reading shelf photos for gaps, share and planogram), demand forecasting and replenishment, customer insight and personalisation, and assistants that answer product and policy questions for store staff and field teams.
Yes. Shelf recognition works from phone photos in normal store lighting. It is trained on your own packs and your competitors’ so it can tell facings apart, and accuracy is checked in a pilot before it is used to measure stores.
Yes. Tools are designed for reps visiting small stores: quick to use, in local languages, on basic phones and patchy networks, with orders suggested from what is missing on the shelf.
Typically your DMS, ERP, POS and loyalty platforms, plus sales force automation apps. We use the data you already collect and write results back to the systems your teams use.
Our own SalesEasy is an AI field assistant for retail and FMCG reps, with shelf photo scoring, competitor sightings, store dashboards and voice input in eight Indian languages. Our founders also come from Indian FMCG, including Amul, PepsiCo and Mondelez.
Pick one costly problem, such as stockouts or weak shelf execution, measure a baseline, and pilot in a set of stores or one region. Scale only once results hold up in real stores.
Tell us how you sell, through which channels and where the gaps show up. We will come back with a practical view of what to pilot first.