Aavya LabTech
Aavya LabTech

Retail SaaS Platform

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Retail & E-commerceSaaSData AnalyticsAWS

Real-Time Retail SaaS Management Platform

12 stores. 5 days to produce a sales report. Stockouts discovered only when customers walked out empty-handed. We built a cloud-native SaaS platform that made every SKU across every location visible in real time, automated demand forecasts 90 days out, and put actionable dashboards in front of every layer of the business — from store floor to executive suite.

40%
Fewer Stockouts
25%
Forecast Accuracy Improvement
5 days → live
Reporting Lag Eliminated
90 days
Demand Forecast Horizon

The Challenge

A retail chain operating 12 locations across three cities had outgrown its manual operations by at least two years. Purchasing was driven by experience and instinct — not data. Store managers emailed stock requests to a central buyer who had no real-time picture of what was selling. Regional leadership received performance reports 5 days after the fact, by which time the decisions those reports should have driven were already late. Stockouts and overstock were estimated to be costing 8–12% of monthly revenue.

Before vs. After

The platform didn't just digitise their spreadsheets — it replaced the decision-making process entirely.

Before
  • Spreadsheets and email chains managing 12-store inventory
  • 5-day lag on sales reporting — decisions made on stale data
  • Stockouts found only when customers complained in-store
  • Purchasing driven by gut feel with no demand signals
  • No shared visibility between store managers and executives
After
  • Live inventory dashboard across all 12 locations — updated in real time from POS
  • Sales and margin data available each morning, not 5 days later
  • Automated low-stock alerts and configurable reorder triggers
  • ML demand forecasts 30–90 days ahead at the individual SKU level
  • Unified executive dashboards across all stores, regions, and product categories

Core Use Cases & Implementation Scenarios

Review how Aavya LabTech designed and built the inventory visibility, credit ledgers, and forecasting modules:

01

Omnichannel E-Commerce Inventory & Catalog Synchronization

User Scenario

"A retail brand selling apparel online through a WooCommerce storefront and offline in physical branches needs real-time stock synchronization."

The Problem

Double-selling occurs when online customers purchase items that were just sold in-store. Catalog maintenance is also doubled, requiring manual data reentry across multiple databases.

Implementation

Establishes structured SKU patterns as a bridge. The webhook router (/api/woocommerce/webhook/{tenant_id}/orders) verifies HMAC signatures to receive checkout events from WooCommerce, updating inventory across locations in real-time.

02

Multidimensional Size × Color Variant Matrix & SKU Auto-Generation

User Scenario

"A retail manager introduces a new jacket style that comes in 8 colors and 6 sizes (48 unique variations) and needs to register them quickly."

The Problem

Generic storefront platforms enforce a hard 100-variant limit and force tedious manual SKU/barcode entries for every single color and size combination.

Implementation

Uses structured Product, ProductSKU, and AttributeValue models. The SKU generator (/api/sku) automatically outputs variants based on category, brand, color, and size permutations with price overrides.

03

Interactive WhatsApp Conversational Commerce

User Scenario

"A customer wants to browse the product catalog, check stock availability, and place an order directly via WhatsApp."

The Problem

Downloading large PDF catalogs is slow, and customers find mobile web checkout processes too complex on slow network connections.

Implementation

The WhatsAppBotService handles messages using an FSM (IDLE -> BROWSING -> CHECKOUT). It sends interactive lists of SKUs with live stock details. B2B buyers purchase via credit, while B2C uses UPI/COD links.

04

Credit Ledger (Udhari) & Tiered Pricing for Wholesale B2B Customers

User Scenario

"A garment manufacturer manages multiple wholesale distributors who purchase in bulk on different pricing tiers and expect credit payment terms (Udhari)."

The Problem

Standard e-commerce checkouts demand upfront credit card payments and fail to support dynamic bulk discounts, credit-limit enforcement, or Net-30/Net-60 payment terms.

Implementation

The B2B module (/api/b2b) tracks credit limits and tiers (Gold/Silver). The wholesale matrix applies bulk breaks, and checkout validates totals against available credit, tracking payment due dates.

05

AI-Driven Demand Forecasting & Smart Procurement Suggester

User Scenario

"A retail buyer needs to plan purchase orders for the upcoming season without overstocking or running out of hot items."

The Problem

Over-ordering leads to dead stock and tied-up capital, while under-ordering results in missed sales. Manual calculations fail to identify subtle seasonality trends.

Implementation

The forecasting service (/api/ai/forecast/{sku}) applies Facebook Prophet on historical sales data to predict SKU demand 7 to 90 days out. The suggestions engine (/api/ai/reorder-suggestions) auto-drafts procurement records.

06

Integrated UPI/Razorpay Payment Links on WhatsApp

User Scenario

"A WhatsApp retail customer chooses online payment and needs a secure way to pay immediately."

The Problem

Sharing static bank details requires manual verification of screenshots, leading to fulfillment delays and fraud risks.

Implementation

On WhatsApp checkout, the bot calls RazorpayService to create a single-use payment link with a 15-minute window. Payments verify via webhook, updating payment_status and pushing orders to dispatch queues.

07

WhatsApp Restock Watchlist (Back-in-Stock Alerts)

User Scenario

"A buyer searches for a popular SKU (e.g., MSHRT-NIKE-L-BLK) on WhatsApp but finds it out-of-stock."

The Problem

Customers walk away from out-of-stock items, and the merchant loses sales data on lost interest.

Implementation

Offering a NOTIFY option logs the user's phone and requested SKU in StockWatchlist. Once inventory replenish cycles run, the system scans logs and broadcasts WhatsApp alerts to waiting buyers.

08

POS Shift Cash Reconciliation & Role Permissions

User Scenario

"Store owners need cashier staff to handle checkout transactions without exposing backend financial reports or stock valuation data."

The Problem

Standard shared logins leak procurement cost margins, and lack of shift cash reconciliation makes audit trails impossible.

Implementation

Enforces RBAC (Owner, Manager, Cashier, Accountant). Cashiers open/close shifts with drawer verification counts. The close-shift workflow calculates drawer variance in audit logs, supporting quick PIN-based shared terminals.

Technology Stack

ReactNext.jsNode.jsPython (Facebook Prophet)WooCommerce APIWhatsApp Business APIRazorpay / UPIPostgreSQLRedis & CeleryAWS Cloud

What We Delivered

Every module below was scoped, built, and deployed as part of a single end-to-end engagement.

Multi-tenant cloud SaaS platform (AWS, 99.9% SLA)
Real-time POS data ingestion pipeline
SKU-level demand forecasting model (scikit-learn, 3 years training data)
Inventory management with automated reorder logic
Executive and store-manager analytics dashboards
Role-based access control (Store Staff / Manager / Executive)
Supplier and purchase order management module
Mobile-responsive interface for store floor use

Frequently Asked Questions

What features does a retail SaaS platform need?+

A robust retail SaaS platform requires real-time inventory management across all store locations, sales analytics and reporting dashboards, demand forecasting, POS system integration, customer purchase history tracking, supplier management, and role-based access for store managers and executives. Cloud-native architecture ensures scalability during peak seasons.

How does predictive demand forecasting work in retail?+

Predictive demand forecasting uses machine learning models trained on historical sales data, seasonality patterns, promotional calendars, and external signals like weather or local events to predict future product demand at the SKU level. This helps retailers optimise stock levels, reduce overstock and stockouts, and plan purchasing more accurately.

Can a SaaS retail platform integrate with existing POS systems?+

Yes. Modern retail SaaS platforms can integrate with major POS systems through REST APIs, webhooks, or middleware connectors. Aavya LabTech builds custom integration layers that sync transaction data in real time, ensuring your dashboards and inventory counts always reflect the latest in-store activity.

Still Running Retail on Spreadsheets?

Whether you manage 2 locations or 200, Aavya LabTech can build the inventory visibility, forecasting, and analytics platform your retail business needs to stop losing revenue to stockouts and start competing on data.

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