AI Agent for 7-Platform Quick Commerce Intelligence | Actowiz

 


Industry

FMCG (Coffee, Snacks, Beverages, Personal Care)

Geography

Pan-India — 50+ cities, 15,000+ dark stores

Platforms Covered

Blinkit, Zepto, Swiggy Instamart, BigBasket, Amazon Now, Flipkart Minutes, JioMart

Data Coverage

Real-time pricing, stock levels, offers, discounts, delivery ETA, ad placements

Refresh Frequency

10-minute cycle on price/stock; daily on assortment

Delivery

REST API + Real-time dashboard + Slack/Email alerts

Client Overview

The client is a leading Indian FMCG company with significant presence across coffee, snacks, beverages, and personal-care categories. The brand sells through traditional retail, modern trade, and increasingly through India's booming quick commerce channel — which now drives 25-40% of urban FMCG revenue depending on category.

With 7 major quick commerce platforms competing aggressively across Indian metros, manual monitoring had become impossible. Stockouts on a competitor platform would trigger demand spikes on others within hours. Promotional pricing changes would ripple across the category in minutes. Ad campaigns underperforming on one platform would silently bleed budget while another platform showed strong ROAS. The client needed a real-time intelligence agent — not weekly reports.

Why Quick Commerce Demands a Real-Time Agent

Quick commerce moves faster than any retail format in India. Pricing changes hourly. Stock turns over multiple times a day. Dark stores activate and deactivate based on demand. A weekly PowerPoint dashboard is essentially a museum exhibit by the time it lands in inboxes.

Business Challenges

Before partnering with Actowiz, the client faced four interconnected operational gaps:

Challenge #1 — Real-Time Out-of-Stock Blindness

With over 15,000 dark stores across 7 platforms, knowing which SKU was OOS in which store at any moment was impossible manually. Stockouts on a competitor brand triggered demand the client couldn't capture; stockouts on the client's own SKUs went undetected for hours, costing direct sales.

Challenge #2 — Reactive, Not Predictive, Pricing

Competitor pricing changes were discovered after they had already shifted market share. The client repriced reactively — typically 24-48 hours behind. In a category where price elasticity peaks during festive and promotional windows, this lag translated directly into lost revenue.

Challenge #3 — Ad Spend Bleeding on Underperforming Platforms

Ad campaigns ran across all 7 platforms simultaneously, but performance varied dramatically by platform, city, and time of day. Without consolidated real-time ROAS visibility, underperforming ad sets continued spending budget for days before manual reviews caught them.

Challenge #4 — Fragmented Data Across 7 Platforms

Each platform had its own UI, refresh patterns, and dark-store structure. The client's team was juggling 7 dashboards, 7 reporting cycles, and 7 different ways of measuring 'pricing' — making cross-platform decisions slow and error-prone.

Pre-Project Impact (Quantified)

Before the AI agent, these challenges translated into measurable monthly losses: OOS Detection Delay

  • Estimated Monthly Revenue Loss: ₹38 L/month

  • Reactive Pricing Lag

    • Estimated Monthly Revenue Loss: ₹52 L/month

  • Ad Spend Waste

    • Estimated Monthly Revenue Loss: ₹28 L/month

  • Cross-Platform Errors

    • Estimated Monthly Revenue Loss: ₹15 L/month

Total estimated monthly impact: approximately ₹1.33 crore — annualised, over ₹16 crore in preventable losses. This was the business case for the AI agent.

Project Objectives

Together with Actowiz Solutions, the client defined five measurable objectives:

  • Detect out-of-stock events on client and competitor SKUs within 15 minutes across all 7 platforms

  • Track competitor pricing in real-time with automatic anomaly detection

  • Surface underperforming ad sets within hours, not days

  • Deliver a single unified intelligence layer replacing 7 separate dashboards

  • Enable autonomous AI-driven recommendations for pricing and ad-spend reallocation

Actowiz Solutions Approach

Actowiz built a 5-stage AI agent pipeline running on a continuous 10-minute cycle:

  • CAPTURE
    Multi-platform crawl across 7 Q-commerce + 15K dark stores

  • NORMALISE
    Unified SKU taxonomy across platforms

  • DETECT
    ML-based OOS, price anomalies, ad signals

  • DECIDE
    AI agent generates pricing & ad recommendations

  • ALERT
    Real-time Slack/email + dashboard + API

Stage 1 — Hyperlocal Multi-Platform Capture

Actowiz built dedicated crawlers for each of the 7 platforms, simulating customer pin codes across 50+ Indian cities and 15,000+ dark stores. Residential proxy infrastructure ensured sustained capture without disruption. Browser automation handled JavaScript-heavy Q-commerce frontends, while anti-bot defences were navigated through human-like behavioural patterns.

Stage 2 — Unified SKU Taxonomy

Each platform had its own SKU naming, pack-size conventions, and category structure. Actowiz built a canonical taxonomy mapping every SKU across all 7 platforms to a single master ID — so that 'Continental Espresso Coffee Powder 200g' on Blinkit, 'Continental Espresso 200gm' on Zepto, and 'Continental Coffee Espresso (200g)' on Instamart all resolved to one canonical SKU. This made true cross-platform comparison possible.

Stage 3 — ML-Based Detection Engine

Three ML models ran continuously: (a) an OOS classifier detecting stockouts within 10 minutes; (b) a price-anomaly detector flagging unusual competitor moves against historical baseline; (c) an ad-performance scorer ranking ad sets by ROAS in real time.

Stage 4 — Autonomous Recommendation Agent

An LLM-powered agent consumed detection outputs and generated specific, actionable recommendations: 'Reduce price on SKU-X in Bangalore Blinkit by ₹4 to match competitor'; 'Pause ad set 12 on Zepto — ROAS down 38% in 4 hours'; 'Increase stock allocation to JioMart Mumbai dark stores — demand spike detected'.

Stage 5 — Real-Time Alert & Delivery Layer

Alerts flowed to Slack channels, email digests, and a real-time dashboard. A REST API exposed all data and recommendations for integration into the client's pricing engine and ad platforms.

Sample Data Snapshot (Illustrative)

Example #1 — Real-Time Out-of-Stock Detection

Below is a 10-minute snapshot of OOS events detected across platforms for a single SKU (Coffee Powder 200g) in Mumbai:

  • 10:02 AM

    • Platform: Blinkit

    • Dark Store: Bandra West

    • Status: In Stock (42 units)

    • Action: Monitor

  • 10:02 AM

    • Platform: Zepto

    • Dark Store: Andheri East

    • Status: Low Stock (4 units)

    • Action: Alert sent

  • 10:05 AM

    • Platform: Instamart

    • Dark Store: Powai

    • Status: OUT OF STOCK

    • Action: Replenish alert

  • 10:08 AM

    • Platform: BigBasket

    • Dark Store: Worli

    • Status: In Stock (28 units)

    • Action: Monitor

  • 10:10 AM

    • Platform: Amazon Now

    • Dark Store: Lower Parel

    • Status: OUT OF STOCK

    • Action: Replenish alert

  • 10:12 AM

    • Platform: Flipkart Minutes

    • Dark Store: Malad

    • Status: Low Stock (6 units)

    • Action: Alert sent

  • 10:12 AM

    • Platform: JioMart

    • Dark Store: Goregaon

    • Status: In Stock (54 units)

    • Action: Monitor

Detected Pattern

3 of 7 platforms going OOS or low-stock in Mumbai within 10 minutes signals localised demand spike. AI agent auto-recommended emergency replenishment + price-hold (no discount) — protecting ₹2.4 L revenue over next 6 hours.

Example #2 — Real-Time Competitive Pricing

Cross-platform pricing snapshot for 200g Coffee Powder, Bangalore at 14:30:

Blinkit

  • Client SKU: ₹289

  • Competitor A: ₹279

  • Competitor B: ₹295

  • Price Gap: +₹10 over A

  • AI Recommendation: Hold — Premium positioning

Zepto

  • Client SKU: ₹285

  • Competitor A: ₹275

  • Competitor B: ₹289

  • Price Gap: +₹10 over A

  • AI Recommendation: Hold

Instamart

  • Client SKU: ₹289

  • Competitor A: ₹289

  • Competitor B: ₹299

  • Price Gap: Match A

  • AI Recommendation: Optimal

BigBasket

  • Client SKU: ₹279

  • Competitor A: ₹289

  • Competitor B: ₹285

  • Price Gap: −₹10 under A

  • AI Recommendation: Hold — Strong undercut

Amazon Now

  • Client SKU: ₹299

  • Competitor A: ₹289

  • Competitor B: ₹289

  • Price Gap: +₹10 over both

  • AI Recommendation: Reduce to ₹289

Flipkart Minutes

  • Client SKU: ₹289

  • Competitor A: ₹275

  • Competitor B: ₹295

  • Price Gap: +₹14 over A

  • AI Recommendation: Reduce to ₹279

JioMart

  • Client SKU: ₹275

  • Competitor A: ₹279

  • Competitor B: ₹285

  • Price Gap: −₹4 under A

  • AI Recommendation: Hold

The AI agent flagged Amazon Now and Flipkart Minutes pricing as misaligned. Repricing recommendations executed within 30 minutes saved approximately ₹1.8 L in lost sales over the next 24 hours.

Example #3 — Ad Spend ROAS Detection

4-hour ROAS snapshot across active ad campaigns:

  • Blinkit

    • Campaign: Festive_Coffee_Premium

    • 4hr Spend: ₹18,400

    • 4hr Revenue: ₹78,200

    • ROAS: 4.25×

    • AI Action: Increase budget +20%

  • Zepto

    • Campaign: Coffee_Morning_Boost

    • 4hr Spend: ₹12,800

    • 4hr Revenue: ₹14,300

    • ROAS: 1.12×

    • AI Action: PAUSE — Bleeding

  • Instamart

    • Campaign: Snack_Bundle_Push

    • 4hr Spend: ₹22,600

    • 4hr Revenue: ₹91,500

    • ROAS: 4.05×

    • AI Action: Hold

  • BigBasket

    • Campaign: Espresso_Search

    • 4hr Spend: ₹8,900

    • 4hr Revenue: ₹6,200

    • ROAS: 0.70×

    • AI Action: PAUSE — Critical

  • Amazon Now

    • Campaign: Coffee_Banner_HM

    • 4hr Spend: ₹16,200

    • 4hr Revenue: ₹52,800

    • ROAS: 3.26×

    • AI Action: Monitor

  • Flipkart Minutes

    • Campaign: Combo_Launch

    • 4hr Spend: ₹14,100

    • 4hr Revenue: ₹61,400

    • ROAS: 4.35×

    • AI Action: Increase budget +25%

  • JioMart

    • Campaign: Premium_Banner

    • 4hr Spend: ₹19,800

    • 4hr Revenue: ₹48,200

    • ROAS: 2.43×

    • AI Action: Optimise creative

AI Agent Auto-Action

2 underperforming campaigns paused within 15 minutes of detection. 2 high-ROAS campaigns received budget boost. Net impact: ₹2.17 L of preserved ad spend redirected to channels earning 4×+ return. Total 4-hour value: ₹7.4 L additional revenue.

Key Features Delivered

  • Multi-Platform Coverage

    • 7 platforms covered: Blinkit, Zepto, Instamart, BigBasket, Amazon Now, Flipkart Minutes, and JioMart

  • Hyperlocal Granularity

    • Pin-code level data capture across 50+ cities and 15,000+ dark stores

  • ⚡ 10-Minute Refresh

    • Continuous monitoring of pricing, stock availability, and offers every 10 minutes

  • ML-Based Detection

    • Out-of-stock classifier, price anomaly detector, and ROAS scorer operating 24×7

  • Autonomous Agent

    • LLM-powered actionable recommendations instead of dashboard-only insights

  • Multi-Channel Alerts

    • Notifications via Slack channels, email digests, real-time dashboards, and REST APIs

  • Unified SKU Taxonomy

    • Cross-platform SKU normalization enabling accurate product comparisons

  • Historical Trending

    • Data warehousing with up to 24 months of historical analysis and trend tracking

Business Impact

Six months after deployment, the AI agent delivered measurable, attributable impact:

  • Annual Revenue Uplift

    • ₹14 Cr

  • Faster OOS Response

    • 76% improvement

  • Ad ROAS Improvement

    • 42% improvement

  • Average OOS Detection Time

    • Reduced from 9 hours to 12 minutes

Impact Breakdown (6-Month Cumulative) OOS Recovery

  • Revenue Recovery (Cumulative 6 Months): ₹4.80 Cr

  • Pricing Optimisation

    • Revenue Recovery (Cumulative 6 Months): ₹6.20 Cr

  • Ad Spend Saved

    • Revenue Recovery (Cumulative 6 Months): ₹2.40 Cr

  • Cross-Platform Sync

    • Revenue Recovery (Cumulative 6 Months): ₹1.10 Cr

Total verified impact: ₹14.5 crore in cumulative revenue uplift over 6 months — an annualised run rate of approximately ₹29 crore against an initial business-case projection of ₹16 crore.

Operational Wins

  • OOS detection time: from 9 hours (manual reporting) to 12 minutes (AI agent)

  • Pricing decision lag: from 24-48 hours to under 30 minutes for 90% of cases

  • Ad spend efficiency: 42% ROAS improvement on Q-commerce ad budget

  • Team time saved: 28 hours/week previously spent on manual cross-platform monitoring, now eliminated

  • Replaced 7 separate platform dashboards with one unified intelligence layer

Client Testimonial

"Quick commerce moves faster than any retail channel we've ever competed in. Before Actowiz, we were always two steps behind — finding out about a stockout or a competitor price move after it had already cost us. The AI agent changed that fundamentally. Now we're responding in minutes, not days. The ₹14 crore uplift in six months is real money — but the strategic shift, from reactive to predictive, is worth even more."

— Head of Digital Commerce, Leading Indian FMCG Brand

Conclusion

Indian quick commerce is the fastest-moving retail format in the country — and arguably the world. With 7 major platforms competing across 15,000+ dark stores, traditional reporting cycles simply cannot keep pace. Stockouts, price changes, and ad performance shifts measured in hours, not days, demand intelligence measured in minutes, not weeks.

Actowiz Solutions delivered an AI agent that closed exactly that gap — capturing real-time multi-platform data, detecting events through ML, generating specific actionable recommendations through an LLM-powered agent layer, and delivering it all through alerts and APIs the client's teams could act on immediately. The result: ₹14 crore of measurable revenue uplift in 6 months, a 76% faster OOS response, and a 42% improvement in ad ROAS.

For Indian FMCG brands operating in quick commerce, the question is no longer whether to monitor the channel in real time, but how. The brands building real-time AI intelligence today are pulling away from those still on weekly dashboards — and the gap is widening fast.

Comments

Post a Comment

Popular posts from this blog

Rappi Menu and Rating Datasets - Monitoring Restaurant Performance

Image
  Introduction In the fast-growing food delivery market, restaurants need actionable insights to optimize menu offerings, pricing, and customer satisfaction. Actowiz Solutions helped a leading food chain monitor its performance on Rappi using Rappi Menu and Rating Datasets. By leveraging advanced scraping techniques, we could  Extract Rappi Food Delivery Data  for thousands of restaurants in real time. This allowed the client to track menu changes, promotions, and customer feedback efficiently. With structured and clean data, the restaurant chain gained visibility into performance trends, enabling better decision-making, improved customer satisfaction, and optimized operational strategies across multiple locations and categories. About the Client The client is a national restaurant chain serving urban customers in the food delivery sector. To remain competitive, they needed to track performance metrics on Rappi, including menu changes, pricing, and ratings. Using Scrape R...
Read more

How AI-Powered Web Scraping Delivered Unified Blinkit, Zepto, Zomato, Swiggy, and BigBasket Datasets through a Single API Integration

Image
Introduction In the fast-moving world of quick commerce and food delivery, reliable and real-time data drives smarter business decisions. Our client, a leading retail data analytics firm, required continuous access to multiple datasets and APIs — including Blinkit Datasets, Zepto Datasets , Zomato Food Delivery Dataset, Swiggy Food Delivery Dataset, and the Big Basket Grocery Data Scraping API. To achieve this, we implemented advanced AI-Powered Web Scraping and Mobile App Data Extraction Services that unified all delivery and grocery datasets into one structured and easily accessible data pipeline. Client Challenge The client wanted to build a single-source data ecosystem to monitor real-time price changes, product availability, and delivery trends across various apps. However, each platform presented different challenges: Blinkit and Zepto datasets used dynamic app-based structures. Zomato and Swiggy food delivery datasets contained frequently changing menus, ratings, and offers. B...
Read more

Scrape McCain FS Product Availability & Stock Status | Actowiz Solutions

Image
  Executive Summary Frozen food brands like McCain FS face frequent Out-of-Stock (OOS) issues, inconsistent distributor supply, and incomplete SKU visibility across B2B procurement platforms. Actowiz Solutions developed a  real-time scraping  and monitoring system to track SKU availability, pack-size accuracy, OOS duration, distributor gaps, and assortment completeness across Udaan, Amazon Business, BigBasket B2B, JumboTail, Swiggy Minis, and Zomato Procurement. The result: 34% reduction in OOS time, 21% improvement in SKU availability, and stronger distributor accountability. Introduction The frozen food service industry relies on consistent availability of fast-moving SKUs. McCain FS products like French Fries, Aloo Tikki, Cheese Shots, and Smiles are widely used by QSRs, cloud kitchens, cafés, hotels, and institutional buyers. But B2B platforms frequently show: Out-of-stock items Missing SKUs Incorrect pack sizes Distributor-level inconsistencies City-wise assortment g...
Read more