Sound Familiar?

Challenges we hear from FMCG & Distribution teams

We've built for this exact problem set. Here's what we typically hear before we start working together.

Your field sales team's CRM data is 3 days stale

Sales reps aren't filling in Salesforce between beat visits. You're making territory and target decisions on bad data.

Distributor ordering is still on WhatsApp and phone

Orders come in at midnight. Stock confirmations go out in the morning. By then, someone's already ordered from a competitor.

Demand forecasting is a spreadsheet exercise that's always wrong

You're either running out of fast-movers or stuck with slow-moving inventory that clogs the warehouse.

You can't see which retailer is growing and which is churning

Your team visits 200 outlets a week but you don't know which ones matter most to defend.

Live Builds

What we've built for FMCG & Distribution

Every case below is live in production. IP fully transferred to the client on delivery. We don't retain access or use your data for model training.

Field Sales CRM3.1

AI-Powered Field Sales CRM for 150+ Distributor Network

A national FMCG brand with 150+ distributors and 600+ field reps had a CRM that nobody actually used. Beat visit data was collected on paper, entered days later, and decisions were made on stale numbers. We built a mobile-first AI CRM that captures visits via voice, auto-generates beat summaries, flags non-performing outlets, and surfaces the top 3 priorities for each rep every morning.

Outcome: CRM adoption: 23% → 94%. Sales per rep improved 28%. Territory managers cut weekly report time from 4 hours to 15 minutes.

94%

CRM adoption (from 23%)

28%

Sales per rep improvement

15min

Weekly report (from 4hrs)

Voice captureMobile CRMBeat intelligenceRep prioritisation

WhatsApp Ordering3.2

WhatsApp-Native Order Management for 400+ Retailers

A regional distributor was processing 400+ daily orders over WhatsApp and phone — manual entry, frequent errors, delayed confirmations. We built a WhatsApp AI bot that accepts orders in natural language, confirms stock in real time, generates invoices, and pushes dispatch notifications — all without a human in the loop for standard orders.

Outcome: Order processing: 4 hours → 8 minutes. Order errors reduced 91%. Retailers placed 34% more orders because it was frictionless.

8min

Order processing (from 4hrs)

91%

Error reduction

34%

More orders placed

WhatsApp AINatural language orderingStock integrationAuto-invoicing

Demand Forecasting3.3

SKU-Level Demand Forecasting Across 500+ Outlets

A mid-size FMCG company was using 3-month rolling averages to plan production and procurement — always wrong, always compensating. We built a demand forecasting engine that ingests outlet-level POS data, weather patterns, local event calendars, and historical seasonality to generate SKU-level weekly forecasts per territory.

Outcome: Forecast accuracy: 61% → 89%. Stockout events down 68%. Overstock carrying cost reduced 40%.

89%

Forecast accuracy (from 61%)

68%

Fewer stockout events

40%

Less overstock cost

SKU forecastingPOS integrationSeasonality modellingTerritory intelligence

Scheme Intelligence3.4

Real-Time Distributor Performance & Scheme Compliance Tracker

A large FMCG brand with 200 distributors had no visibility into whether trade schemes were being passed down to retailers. Scheme payouts were being claimed but performance wasn't materialising. We built a real-time intelligence layer that tracks scheme uptake per distributor, flags non-compliant claim patterns, and auto-generates territory-level reports for the sales leadership team.

Outcome: Scheme ROI improved 44%. Fraudulent claims flagged before payout: 18% of total. Field force accountability scores up 60%.

44%

Scheme ROI improvement

18%

Claims flagged pre-payout

60%

Field force accountability

Scheme trackingFraud detectionDistributor scoringTrade intelligence

More SKUs. More routes. More reps. You need AI that scales with you.

Ready to build something like this?