Data & AI Engineering

Develop data platforms around real operational requirements

We design and build platforms that make information from ERP, CRM, production and other sources available to analytics and applications. Architecture, data contracts, quality, access and ongoing operations are engineered together.

Discuss the current situation

The operational problem

Fragmented source systems, unreliable extracts and conflicting definitions prevent companies from using their data. A new platform alone does not resolve ownership, data contracts or operational failures.

We first identify which applications and decisions consume the data, the actual freshness requirements and accountable teams. Only then do we choose between extending existing systems, a warehouse, a lakehouse or distributed data products.

What we engineer

Architecture and data products

Design warehouse, lakehouse or distributed data products around actual access patterns, change rates and lifecycle costs.

Source integration and contracts

Connect APIs, CDC, events and batch feeds with versioned schemas, retries and traceable lineage.

Models and serving layers

Build stable business entities, history, semantic models and interfaces for BI and applications.

Security, quality and ownership

Enforce permissions, sensitive-data handling, quality tests and clear responsibility.

Operations and observability

Monitor freshness, job failures, query performance and infrastructure cost.

Methods and trade-offs

Batch and streaming are alternatives driven by process requirements, not maturity labels. A reliable daily batch may outperform fragile real-time pipelines.

Warehouse, lakehouse and federation have different trade-offs. Data contracts, idempotency, access, deletion and change control are more important than a fashionable architecture.

From evaluation to production

Define the consumer

Clarify business need, frequency, confidentiality and decision-critical metrics.

Inspect the landscape

Review sources, identifiers, volumes, interfaces, quality and existing investments.

Design the target

Select storage, processing, access patterns and a phased migration path.

Deliver a first data product

Implement one measurable data flow with tests, monitoring and ownership.

Expand and operate

Add sources iteratively and review quality, costs and adoption.

Frequently asked questions

Do we need a central platform?

Not necessarily. Distributed data products or improved existing integrations can be sufficient if they meet quality, cost and access requirements.

Warehouse or lakehouse?

Choose against real query patterns, formats, governance and operational costs, not terminology.

How do you avoid another data silo?

With shared contracts, ownership, documented interfaces and quality checks.

Where should we begin?

With a bounded data product that has a measurable consumer and manageable dependencies.

Technical discovery

Discuss the system and implementation constraints

We review the existing systems, technical constraints and a measurable first delivery step.