DATABRICKS SERVICES
Data & AI R&D Partner for E-commerce
Add a senior external engineering team to build new data and AI capabilities on Databricks, from Customer 360 and pricing to recommendations and demand forecasting. We work as an extension of your product and data organization, from the first hypothesis to production.
A data and AI engineering partner built for e-commerce
E-commerce context from day one
We work with the data behind customers, products, orders, pricing, inventory, clickstream and marketing.
Databricks from data to AI
One platform for governed data engineering, analytics, machine learning and production AI.
Senior engineers stay close
Architecture, experimentation and production decisions are led by experienced practitioners.
EXTERNAL DATA & AI R&D TEAM
Add delivery capacity without building every specialist role in-house
An external data and AI R&D team is a dedicated engineering partner that helps you investigate, build and productionize new capabilities while your internal leaders keep product context and priorities. Dateonic brings the data architecture, engineering and ML skills needed to move an e-commerce use case from the backlog to a working product.
What we deliver
An R&D scope built around your product roadmap
01 · VALIDATE
Test the right ideas
Turn a business problem into a testable hypothesis, inspect the available data and build the smallest useful proof before committing to a larger initiative.
02 · DATA PRODUCTS
Build the data behind the use case
Create reusable customer, product, order, pricing, inventory and behavioral data products instead of one-off datasets for a single model.
03 · ML & AI
Engineer models into products
Develop recommendations, propensity, demand forecasting, pricing support and GenAI components with evaluation built into the workflow.
04 · PRODUCTION
Make it run in the real business
Connect outputs to commerce, CRM, planning or analytics systems, then add deployment, monitoring, data and model lineage, access controls and operational ownership. Where regulatory requirements apply, we also build the technical evidence layer that supports AI governance and EU AI Act readiness.
How we work
A product rhythm from hypothesis to production
01 · ALIGN
Choose the problem worth solving
Define the user, business decision, success measure, available data and constraints before committing engineering capacity.
02 · SHAPE THE POD
Bring the right specialists
Bring the mix the work needs, such as data architecture, data engineering, ML engineering and Databricks platform expertise.
03 · BUILD & LEARN
Ship useful increments
Work through a shared backlog, test assumptions with real data and show progress in working software rather than long research documents.
04 · PRODUCTIZE
Move proven work into production
Harden the pipelines and models, integrate downstream systems, document decisions and transfer ownership at the agreed pace.
Selected Work
From fragmented retail data to operational AI
CASE STUDY · E-COMMERCE
Customer 360 for personalization at scale
Dateonic connected transactional, behavioral and product data on Databricks, creating a reusable foundation for recommendations and propensity models.
CASE STUDY · RETAIL & E-COMMERCE
Demand forecasting at SKU level
A Databricks-based forecasting platform scaled product-level predictions and connected the output to replenishment decisions.
Working with Dateonic
An external team that behaves like an R&D partner
One accountable R&D lead
A senior owner connects business priorities, architecture and the engineering backlog.
A pod shaped around the problem
The mix of data engineering, ML and platform expertise changes as the work moves from discovery to production.
Your team keeps the context
We work with product, merchandising, data and technology leaders, with shared decisions and transparent delivery.
Technical Leadership
R&D only matters when the result can reach production
Lukasz helps connect product questions with the architecture and engineering choices underneath them. That keeps experiments grounded in the data, governance and operating model they need to become dependable products.
FAQ
E-commerce Data & AI R&D questions, answered
QUESTION 01
What is an external Data & AI R&D team?
It is a dedicated engineering partner that works against your product roadmap to investigate, build and productionize new data and AI capabilities. Dateonic supplies the technical leadership and specialist mix, while your team retains the business context, priorities and product decisions.
QUESTION 02
How is this different from staff augmentation?
Staff augmentation usually fills named roles that you direct day to day. Dateonic’s R&D model is built around an agreed product backlog and shared outcomes, with technical leadership included. Your team sets the product priorities; the joint team shapes and delivers the engineering work.
QUESTION 03
Which e-commerce problems can the team work on?
Typical areas include Customer 360, product and merchandising intelligence, recommendation systems, propensity models, demand forecasting, inventory decisions, pricing support and AI applications grounded in company data. We start with the business decision and available data, not a predetermined model.
QUESTION 04
Can the R&D team work with our existing Databricks platform and people?
Yes. The team can build within an existing Databricks environment and work alongside internal product, data science, engineering and platform teams. We agree interfaces, engineering standards, decision rights and handover expectations before delivery begins. For relevant AI use cases, this can include ownership, access policies, data and model lineage, evaluation records, monitoring and audit logs in Unity Catalog and MLflow to support AI Act readiness. These capabilities do not replace legal classification or conformity assessment.