DATABRICKS SERVICES · [SERVICE CATEGORY]
Build AI on data your e-commerce business can trust
Turn your unified data foundation into recommendations, personalization, propensity models and AI-powered decision making.
Dateonic builds production AI and ML workloads directly on Databricks — using governed customer, product, transaction and behavioral data.
AI grounded in your business data
Recommendations
Build recommendation engines using customer behavior, transactions, product information and preferences.
Personalization
Use changing customer signals to deliver more relevant experiences across the e-commerce journey.
Predictive models
Build propensity-to-buy, churn, demand and other models directly on the data foundation.
AI for E-commerce
Recommendations are only as good as the data behind them
Personalization requires more than a model. It requires a current, connected view of customers, products and behavior.
The retail material highlights the importance of combining transactions, demographics, preferences, clickstream and digital journey data to create Customer 360 and power omnichannel personalization.
We build the data and ML foundation required to train, refresh and serve these models at scale.
What we deliver
What We Build
01 · [THEME]
INGEST
Connect commerce platforms, ERP, CRM, POS, PIM, marketing and other operational sources into a consistent data architecture.
02 · [THEME]
STREATM
Ingest website, mobile and transactional events so customer behavior and operational changes can become available without waiting for nightly batch processing.
03 · [THEME]
MODEL
Build reusable models around customers, products, orders, inventory and other business domains so downstream teams don’t repeatedly reconstruct the same data.
04 · [THEME]
SERVE
Create curated data layers that can power Databricks SQL, BI, machine learning, applications and AI from the same underlying platform.
How we work
A controlled path from fragmented sources to one platform
01 · DISCOVER
Map your e-commerce data
Identify source systems, domains, data owners, dependencies, latency requirements and critical business workloads
02 · DESIGN
Define the target architecture
Design the Databricks architecture, data domains, ingestion patterns, storage layers and operating model.
03 · BUILD
Connect and model the data
Build pipelines and reusable data products for customer, product, order, inventory and behavioral data.
04 · ENABLE
Make the data usable
Connect analytics, BI, ML and AI workloads and enable your teams to build on the new foundation.
Selected Work · Optional
Real-Time E-commerce Data
CASE STUDY
Near real-time inventory visibility and a unified data foundation for analytics and AI.
Dateonic unified POS, e-commerce, ERP and warehouse data on Databricks, enabling real-time inventory insights, consistent reporting and a scalable foundation for forecasting and AI.
CASE STUDY
Customer Intelligence for Premium Retail
A unified customer view with actionable insights for more personalized engagement.
Technical Leadership
Every signal behind your e-commerce business, gathered in one place.
E-commerce businesses generate enormous amounts of data across customers, products, orders, clickstream and inventory. The challenge isn’t collecting more data — it’s connecting it.
At Dateonic, we help e-commerce companies bring these signals together on Databricks and turn them into a trusted foundation for analytics, AI and better decisions.
FAQ
E-commerce data foundation questions, answered
QUESTION 01
Do we need to replace our existing e-commerce systems?
No. The goal is not to replace Shopify, Magento, Salesforce, ERP, PIM or other operational systems.
We connect the data they generate into a shared Databricks foundation, so analytics, AI and downstream applications can work across the full business without replacing the systems that run it.
QUESTION 02
Which data should we bring into the platform first?
Start with the data required for the highest-value decisions. For most e-commerce companies, that means some combination of customer, product, orders, clickstream and inventory data.
We then expand the foundation in waves rather than attempting to centralize every source before delivering any value.
QUESTION 03
Do we need real-time data everywhere?
No. Real-time architecture should follow the decision, not the technology.
Clickstream personalization or inventory availability may require low latency, while financial reporting or historical analysis may be perfectly suited to scheduled processing.
QUESTION 04
What is the difference between this and a traditional data warehouse?
The goal is broader than central reporting. The platform needs to support batch and streaming data, structured and semi-structured sources, analytics, machine learning and AI from the same foundation.
That means you are not building one platform for BI and then separate systems for real-time data and AI.