Service
Data & AI Engineering
Data platforms, pipelines, BI dashboards and AI agents that turn scattered business data into decisions.
Typical challenges
- Sales, ad and e-commerce data spread across systems with inconsistent definitions
- Manual Excel reporting is slow and error-prone
- Unsure where to start with LLMs and how to keep data safe
What we deliver
- Lakehouse platform design (Databricks / Delta Lake and similar)
- Automated ingestion, cleaning and harmonisation pipelines
- Management BI dashboards and KPI definitions
- LLM agents for documents, support and internal assistants
- Data-quality monitoring and alerting
Method
How we approach it
- STEP 01
Assess
inventory sources, metric definitions and key business questions
- STEP 02
Model
layered data model (Bronze / Silver / Gold) with shared definitions
- STEP 03
Pipelines
automated ingestion, cleaning and data-quality rules
- STEP 04
Apply
BI dashboards, AI agents and alerts
- STEP 05
Operate
monitoring, cost optimisation and knowledge transfer
Architecture
Reference architecture
01 · Sources
- ERP / CRM / e-commerce
- Advertising and marketing data
- Files, APIs and databases
02 · Bronze
- Incremental ingestion
- Raw data retained
- Schema evolution
03 · Silver
- Cleaned, deduplicated, joined
- Shared metric definitions
- Quality rules and quarantine tables
04 · Gold
- Metrics and features
- BI dashboards
- AI agents and retrieval
Typical stack
- Databricks
- Apache Spark
- Delta Lake
- Unity Catalog
- MLflow
- Python
- SQL
- Power BI
- LangGraph
Engagement models
Three ways to work with us
Start with an assessment, then extend into a project or an ongoing retainer.
Assessment
1–2 weeks to map the current state, goals and constraints, ending with an architecture plan, quote and milestones.
Fits: direction set, solution open
Project
Fixed scope and milestones, weekly iterations visible on staging, delivered with documentation and handover.
Fits: a clear build goal
Retainer
Monthly operations, monitoring, content updates, security upgrades and continuous improvement.
Fits: running reliably after launch
Proof
Related work
FAQ
Our data volume is small. Do we need a data platform?
Not necessarily. We start with a 1–2 week assessment and a lightweight setup, then grow with proven value.
Will AI agents leak company data?
Access follows data classification; sensitive data can stay in a private environment, and every call is logged.
Do we have to use Databricks?
No. Layered modelling, data quality and reproducible computation do not depend on one vendor: Databricks, managed cloud services or open-source Spark and Delta Lake are chosen by budget and compliance needs.
How do we know the project worked?
During the assessment we agree a few verifiable goals with the business, such as report freshness or manual work saved, and review each one after delivery.
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