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  • AI-ready data platforms

AI-ready data platforms, engineered from the ground up

Modern, cloud-native data platforms that turn raw operational data into reliable decisions – and give your AI, analytics, and automation a foundation they can actually trust.

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Device security

Cloud connectivity

Apps and portals

Why a data platform

Dashboards tell you what happened. A platform lets you act on it.

Point-solution BI gets your team through the quarter. A real data platform compounds: every new dataset, model, and product lands on the same trustworthy foundation – and gets cheaper, faster, and safer to deliver.

Decisions in hours, not weekst

One governed source of truth across finance, operations, and product – so executives, analysts, and ops teams stop arguing about whose number is right.

AI that actually ships

Clean, lineage-aware data with the access controls your legal team signs off on. The difference between a demo and production AI is almost always the platform underneath it.

Scales with the business

Built on modern cloud stacks – Snowflake, Databricks, Microsoft Fabric – with DataOps from day one. New markets, products, and acquisitions plug in; they don’t require a rebuild.

What we build

End-to-end data platform capabilities

We design and build every layer of the modern data stack – and, just as importantly, the operating model around it.

Cloud data warehouse & lakehouse

Greenfield builds or migrations on Snowflake, Databricks, and Microsoft Fabric (Warehouse & OneLake). Dimensional, Data Vault, or medallion – modeled for how your business actually asks questions.

Ingestion & streaming

Batch, CDC, and real-time pipelines from operational systems, IoT fleets, SaaS APIs, and partner feeds. Kafka, Event Hubs, Fivetran, Airbyte, Fabric Eventstream & Dataflows Gen2, custom connectors.

Transformation & modeling

dbt-based transformations with tests, docs, and lineage – plus Fabric notebooks and Spark where it fits. A semantic layer your business units can trust (dbt metrics, Fabric semantic models) and your data team can actually maintain.

DataOps & orchestration

Airflow, Dagster, Fabric Data Factory, CI/CD, environments, monitoring, cost controls. We treat the platform like production software – because it is.

Governance, quality & lineage

Batch, CDC, and real-time pipelines from operational systems, IoT fleets, SaaS APIs, and partner feeds. Kafka, Event Hubs, Fivetran, Airbyte, Fabric Eventstream & Dataflows Gen2, custom connectors.

AI & ML enablement

Feature stores, vector stores, RAG foundations, MLOps, and Fabric Data Science / Copilot where it accelerates delivery. The platform decisions that determine whether your AI initiatives scale – or stall.

Reference architecture

Building blocks, assembled around your stack

We don’t ship a fixed stack. We design top-down – starting from the outcome you need to deliver, then tracing upstream through serve, transform, store, and ingest, back to the source. Every layer is a deliberate choice, made in service of the value above it.

Consume

BI & dashboards

Power BI (Fabric), Tableau, SAP BusinessObjects, SAP Analytics Cloud

Embedded analytics

Power BI Embedded, in-product data

AI & ML apps

RAG, forecasting, Fabric Copilot

Reverse ETL

Operational sync

Serve

Semantic layer

dbt metrics, Cube, Fabric semantic models

Feature / vector store

Feast, pgvector

APIs & data products

GraphQL, REST, Fabric SQL endpoints

Access & masking

Row/column policies

Transform

dbt transformations

Tested, versioned

Orchestration

Airflow, Dagster, Fabric Data Factory

Notebooks & Spark

Databricks, Fabric notebooks

Quality & observability

Great Expectations, OpenLineage, Monte Carlo

Store

Cloud DWH / Lakehouse

Snowflake, Databricks, Fabric (Warehouse & Lakehouse)

Catalog & lineage

Unity Catalog, Microsoft Purview, Collibra

Storage tiers

OneLake, S3, ADLS, GCS

Security & governance

IAM, encryption, audit

Ingest

Batch & CDC

Fivetran, Airbyte, Fabric Dataflows Gen2

Streaming

Kafka, Event Hubs, Fabric Eventstream

IoT & edge

MQTT, IoT Hub, Real-Time Intelligence

SaaS & APIs

Custom connectors, Fabric shortcuts

AI-ready by design

Every platform
we build is AI-ready
from day one

The difference between an AI demo and AI in production is almost always the platform underneath it. We build that foundation in from the start – clean, lineage-aware data, governed access, and the serving infrastructure your models and Copilots need.

Governed, lineage-aware data

Every model input is traceable to source – the bar enterprise AI actually has to clear.

Vector & feature stores

RAG foundations, embeddings, and feature pipelines built into the platform, not bolted on later.

Copilot-ready

Semantic layer and access policies that make Fabric Copilot and similar tools safe to switch on.

How we work

From platform assessment
to a production data ecosystem

Four phases, delivered incrementally. We don’t disappear for nine months and come back with a big-bang reveal – each phase ends with something useful in your hands.

Step 1

Assess & align

We map your current state – systems, data, reporting, pain points – and translate it into clear platform outcomes.

  • Current-state audit
  • Use-case prioritization
  • Business value hypotheses
  • Go / no-go recommendation

Step 2

Architect

We design a target architecture fit for your regulatory, cost, and organizational context – with an unbiased tech selection.

  • Target reference architecture
  • Tech evaluation & PoCs
  • Total cost of ownership model
  • Delivery roadmap

Step 3

Build the MVP platform

We stand up the core platform around a high-value use case, end to end – ingestion, storage, modeling, serving, governance.

  • Platform foundations
  • First 2–3 data products
  • DataOps & CI/CD
  • Team enablement

Step 4

Scale & operate

We extend the platform across new domains, embed governance, and – if helpful – operate it with you.

  • Domain onboarding
  • Data governance at scale
  • Managed DataOps / SRE
  • AI/ML readiness

Tech we build on

Modern stacks – without the religious wars

We’re deeply hands-on across the leading cloud data stacks. We recommend what fits your context, not what’s on our rate card.

Cloud DWH & Lakehouse

Snowflake Databricks OneLake Microsoft Fabric · Warehouse Microsoft Fabric · Lakehouse

Transformation

dbt Spark SQL PySpark Data Vault 2.0 Fabric notebooks Fabric Dataflows Gen2

Orchestration & DataOps

Airflow Dagster Azure Data Factory Fabric Data Factory Fabric pipelines GitHub Actions Terraform

Ingestion & streaming

Kafka Event Hubs Fivetran Airbyte Debezium (CDC) OneLake shortcuts Fabric Real-Time Intelligence

Governance & quality

Unity Catalog Microsoft Purview Fabric OneLake catalog Collibra OpenLineage Great Expectations

BI & AI/ML

Power BI Power BI in Fabric Fabric Copilot Tableau SAP Analytics Cloud PyTorch TensorFlow MLflow Fabric Data Science

FAQ

Questions data & business leaders ask us

We design and build every layer of the modern data stack – and, just as importantly, the operating model around it.

We already have BI and a warehouse. Why would we need a “platform”?

Snowflake, Databricks, Microsoft Fabric – which should we pick?

Snowflake, Databricks, Microsoft Fabric – which should we pick?

It depends on your workloads, your existing stack, your team’s skills, and your regulatory context. We’ve delivered production platforms on all three and we’re not incentivized to pick one. Our Assess & Architect phases are explicitly designed to make this call on evidence, not vendor marketing.

How long until we see value?

It depends on your workloads, your existing stack, your team’s skills, and your regulatory context. We’ve delivered production platforms on all three and we’re not incentivized to pick one. Our Assess & Architect phases are explicitly designed to make this call on evidence, not vendor marketing.

Can you work alongside our existing data team?

It depends on your workloads, your existing stack, your team’s skills, and your regulatory context. We’ve delivered production platforms on all three and we’re not incentivized to pick one. Our Assess & Architect phases are explicitly designed to make this call on evidence, not vendor marketing.

What about AI? Do we need a data platform before doing AI?

It depends on your workloads, your existing stack, your team’s skills, and your regulatory context. We’ve delivered production platforms on all three and we’re not incentivized to pick one. Our Assess & Architect phases are explicitly designed to make this call on evidence, not vendor marketing.

Case studies

Trusted by leading manufacturers and operators across Europe. These solutions support mission‑critical industrial testing environments where precision, repeatability, and scalable execution are essential.

Factory test automation upgrade – migrating to NI TestStand

See full case study

Technologies

LabVIEW, NI TestStand, custom database, SQL, legacy code stabilization, operator UI improvements.

Client challenge/business need

This manufacturer needed to upgrade factory test automation built on legacy hardware and custom LabVIEW systems – ensuring reliable end-of-line testing, reduced downtime, and scalable production growth.

Solution at a glance

Proekspert mapped, modernized, and migrated core automated tests to NI TestStand, reusing LabVIEW assets and overhauling databases for transparency and direct access. The team delivered UI upgrades for operator efficiency and stabilized integration – enabling step-by-step migration with no production disruption.

Results

  • Lower risk of failure. Critical tests now run on a supported, stable automation platform – no more legacy hardware headaches or hidden bugs.
  • Higher operator productivity. A streamlined, more intuitive UI and transparent data access speed up daily work and troubleshooting, with less retraining.
  • Ready to scale. The test platform can now support new products and sites quickly – without carrying old technical debt forward

Long term automated QA partnership with Danfoss

Project duration

Strategic partnership, ongoing since 2008

Technologies

LabVIEW, NI TestStand, custom database, SQL, legacy code stabilization, operator UI improvements

Client challenge/business need

Danfoss runs large scale automated testing across development and production for its frequency drive portfolio. Quality assurance is a permanent, mission critical function that must remain reliable as products, firmware, and global sites evolve. The challenge is sustaining and evolving this test infrastructure without disrupting releases or operations.

Solution at a glance

Since 2008, Proekspert has operated and continuously evolved Danfoss’s automated QA environment. We modernize and maintain NI TestStand based workflows, reuse and stabilize LabVIEW assets, improve databases for transparency, and refine operator UIs – ensuring the test platform evolves safely alongside products without interrupting production.

Results

  • Lower operational risk. Critical drive tests run on a stable, supported automation platform that is continuously maintained, improving test reliability and confidence across releases.
  • Sustained operator productivity. Clear operator UIs and transparent access to test data support efficient daily work, faster troubleshooting, and consistent execution across global test environments.
  • Built to scale long term. A mature automated test platform supports new drive models, firmware generations, and production sites without introducing legacy constraints or maintenance risk.

Let’s discuss how we can streamline your testing processes

With 20+ years of experience, Proekspert delivers custom tools and expert support to enhance testing workflows. We design tailored solutions for LabVIEW and TestStand, creating scalable setups built for precision and growth. Leave your details below to get started.

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Jukka Antero Halttunen

Embedded Software and Systems Lead
EST, ENG, FIN
Jukka Halttunen +372 5621 6173

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