Building a data-driven operating system for e-commerce.
Palkstreet is developing an internal technology stack that brings together operational data, business analytics, machine learning, and automation — to help us understand our own brands more deeply and make better decisions.
Data infrastructure & business analytics.
Data Infrastructure
Reliable data is the foundation of our technology direction. We're developing pipelines and systems to consolidate the operational and commercial data our brands generate into a structured environment we can analyze.
- Sales, orders, and inventory data
- Product/catalog and pricing data
- Advertising and marketing performance data
- Returns and profitability data
Business Analytics
From raw data to actionable business intelligence. We're building analytics to move from manually reviewing marketplace reports toward a more continuous, data-driven view of the business.
- Sales, product, and cross-brand performance
- Advertising efficiency and channel performance
- Inventory, returns, and pricing analytics
Applying machine learning to real operating problems.
Our brands have genuinely different demand patterns. Idolkart, in devotional products, can see sharp demand spikes around festivals such as Diwali, Navratri, and Ganesh Chaturthi. Ekartpet's pet-care products are largely consumables, which tend to follow more regular, repeat-purchase cycles. These are real, brand-specific problems — not a generic case for "adding AI" — and they're what our forecasting and predictive analytics work is aimed at.
Machine Learning
We're exploring machine learning applications against our own operational data — this work is early-stage, and nothing described here is a deployed or commercially available system.
- Demand and inventory forecasting
- Product performance prediction
- Pricing intelligence
- Pattern and anomaly detection
Predictive Analytics
Historical business data can help identify trends and support forward-looking decisions — for example, anticipating festival-driven demand for Idolkart or repeat-purchase timing for Ekartpet. We're exploring how to use it for planning across our brands.
- Demand trends and inventory requirements
- Product performance outlook
- Pricing and operational planning
Automation & AI-assisted decision making.
Automation
Analytics and machine learning are intended to feed automated workflows — reducing manual, repetitive work as the number of brands and channels grows.
- Automated reporting and performance monitoring
- Listing and content update workflows
- Alerts and decision-support workflows
AI-Assisted Decision Making
We're exploring how AI can make complex business data easier to understand and act on — an area of active development and exploration, not a finished product.
- Automated insight generation
- Identifying and explaining unusual performance changes
- Summarizing large operational datasets
How we're building this, step by step.
Collecting, structuring, and organizing business data across our brands.
Building dashboards, metrics, and business intelligence on top of that data.
Developing predictive models and intelligent analysis for forecasting and pricing.
Turning insights into automated workflows and decision support.
Real business problems, not technology for its own sake.
Business challenge
Multiple brands, products, channels, and operational datasets to manage.
Data
Centralize and structure the information.
Analytics
Understand performance and identify patterns.
Machine learning
Forecast and predict where it's appropriate to do so.
AI
Turn complex information into understandable insights.
Automation
Reduce repetitive work and accelerate decisions.
Cloud infrastructure for this work.
As we build out our data, analytics, and automation systems, we plan to run this technology on AWS. Services we're evaluating include Amazon S3 for centralized data storage, AWS Glue for data pipelines, Amazon QuickSight for business intelligence dashboards, Amazon SageMaker for machine learning model development, and AWS Lambda for automation workflows. We haven't migrated this work onto AWS yet, but these are the services we intend to build on as these systems come online.
Complexity is the problem we're solving for.
Every additional brand, marketplace, and channel adds operational surface area — more listings to manage, more inventory to track, more data to reconcile. Rather than scaling that complexity with more manual work, we're investing in internal systems designed to handle it directly.
Technology in service of operations.
Our technology work exists to support the brands described on our Our Businesses page — it isn't a separate product line. As these internal tools mature, we expect them to directly improve how efficiently and reliably our brands operate.
Questions about our technology work?
We're glad to talk with partners, platforms, and collaborators about what we're building.
Contact us