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.
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
Transformation
Orchestration & DataOps
Ingestion & streaming
Governance & quality
BI & AI/ML
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”?
If every new use case costs disproportionately more than the last, if your AI initiatives keep stalling on “we don’t trust the data,” or if you can’t trace a number in a dashboard back to its source — you’ve outgrown your BI setup and you need a platform. A platform is what turns data work from a cost center into something that compounds.
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?
Our first measurable outcome typically lands in 8–12 weeks: a working slice of the platform delivering one high-value data product end-to-end, on the target stack. From there we extend iteratively — you never wait a year for the big reveal.
Can you work alongside our existing data team?
Yes — that’s our default. We embed with your team, transfer knowledge continuously, and leave you with a platform your people can extend and operate. We can also run an operate-with-you model for DataOps if that’s useful.
What about AI? Do we need a data platform before doing AI?
You can pilot AI without one. You can’t reliably ship AI in production without one. The vast majority of stalled enterprise AI programs we see fail at the data layer — stale, ungoverned, unlineaged data — not at the model layer. A platform is the fastest path to AI that actually survives legal review and scale.
Tell us where your data is today – we’ll map a way forward
Share your current stack, your headaches, or a use case you’re trying to unlock. We’ll come back with a candid read on what’s feasible, what it’d take, and whether we’re the right partner for it.
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Urmas Kobin
Proekspert AS
Tallinn headquarters
Sõpruse pst 157
13417 Tallinn
Estonia (EU)
Reg code 10331577
VAT no EE100185388
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