Build a Data Foundation
We design data platforms, pipelines, governance, analytics and machine-learning solutions that turn fragmented enterprise data into reliable reporting, predictive models and AI systems.
What we help you to build
You do not need a giant data transformation to create value. We can start with one reporting problem, one pipeline, one model or one governance gap and expand from there.
01
Data Governance & Quality
Define ownership, data standards, quality checks, lineage and access rules so teams know which data to trust and who is responsible for it.
02
Data Engineering & Platforms
Integrate source systems, build batch or real-time pipelines, and design warehouses, lakes or lakehouses for reliable access at scale.
One data foundation, multiple outcomes
We work across architecture, engineering, governance, analytics and ML so the handoffs between teams do not become the failure point. Start where the problem is today; add the rest only when it is needed.
03
Data Science & Machine Learning
Build forecasting, optimization, scoring, anomaly detection and other models around a measurable business decision or workflow.
04
Analytics & BI
Create semantic models, dashboards and operational reporting that give teams a consistent view of KPIs and business performance.
What good data looks like in practice
The goal is not more tables or dashboards. It is data that is understandable, reproducible, secure and useful in everyday decisions.
People should be able to trace an important metric or model input back to its source, understand how it was transformed, and know who owns the definition.
Traceable by design
Source → transformation → metric or model
People should be able to trace an important metric or model input back to its source, understand how it was transformed, and know who owns the definition.
Traceable by design
Source → transformation → metric or model
Measured
data quality
Freshness, completeness, accuracy and volume checks are explicit and monitored where they matter.
Security and access rules follow the data lifecycle, from ingestion and storage to BI, analytics and model use.
Security and access rules follow the data lifecycle, from ingestion and storage to BI, analytics and model use.
Models and reports are versioned, tested and reproducible so changes can be reviewed before they affect decisions.
Owned
definitions
Critical data products and KPIs have named owners instead of being maintained by convention.
Models and reports are versioned, tested and reproducible so changes can be reviewed before they affect decisions.
01
Start with the decisions
02
Map the data
03
Define trust criteria
04
Design the target architecture
05
Deliver in useful slices
06
Measure and improve
Start with the decisions, not the warehouse
Start with the decisions, not the warehouse
Which decisions, reports, models or workflows are limited by the data today? We define the user, outcome and measurable success criteria before discussing platforms.
Map the sources and ownership
Map the sources and ownership
Identify the systems, tables, files, APIs and documents involved; who owns them; how often they change; who may access them; and where definitions conflict.
Define what trusted data means
Define what trusted data means
Agree on critical data elements, freshness, completeness, accuracy, reconciliation and lineage requirements. Not every field needs the same quality bar.
Design for the target workload
Design for the target workload
Choose warehouse, lake, lakehouse, streaming, semantic and ML components around actual volumes, latency, skills, security and operating constraints — not a reference diagram.
Deliver a thin usable slice
Deliver a thin usable slice
Connect a limited set of sources and ship one useful report, data product, model or pipeline end to end. Validate it with users before scaling the platform.
Operate, measure and expand
Operate, measure and expand
Monitor data quality, pipeline reliability, usage, cost and model performance. Add sources and capabilities when the previous slice is stable and used.
Designed for production, not just dashboards
A data solution is finished only when people can operate it, change it safely, and understand what happens when something breaks.
Cloud, On-Prem & Hybrid
Design around your security, latency, data-residency and operating constraints instead of forcing every workload into one platform.
Data Contracts & Quality
Define schemas, ownership and critical quality checks so downstream reports, models and agents can fail visibly instead of silently drifting.
Documentation & Lineage
Document sources, transformations, business definitions and ownership so teams can understand how a number or feature was produced.
Security & Access Controls
Apply access rules close to the data, preserve source-system permissions where possible, and make sensitive-data handling explicit.
Performance & Cost Engineering
Tune storage, compute, queries and pipelines against real usage patterns so the platform stays fast enough and economically sustainable.
Monitoring & Incident Ownership
Monitor pipelines, freshness, quality and platform health, with clear owners and runbooks for the data products that matter.
Testing & CI/CD for Data
Version transformations and models, test changes before release, and make deployments repeatable across development and production environments.
AI & ML Readiness
Expose governed, observable data products that analytics, machine-learning systems and AI agents can use without bypassing ownership and access controls.
01
Architecture & Data Products
02
Governance, Catalog & Lineage
03
Data Quality & Observability
04
Data Engineering
05
Data Science & Machine Learning
06
Analytics & BI
Architecture & Data Products
Architecture & Data Products
Design the target data platform and the reusable data products it exposes. Define sources, contracts, storage, transformation, serving layers and ownership for each critical output.
Governance, Catalog & Lineage
Governance, Catalog & Lineage
Create a practical governance model around data ownership, glossary, catalog, classification, access and lineage. The goal is to make critical data discoverable and accountable, not to create governance for its own sake.
Data Quality & Observability
Data Quality & Observability
Implement checks for freshness, completeness, accuracy, schema drift and reconciliation, plus monitoring and incident ownership for critical pipelines and data products.
Data Engineering
Data Engineering
Build and operate pipelines for batch, streaming and API-based data. Integrate cloud, on-prem and SaaS sources, transform data, manage storage and expose it to BI, ML and AI workloads.
Data Science & Machine Learning
Data Science & Machine Learning
Develop forecasting, optimization, scoring, recommendation, anomaly detection and other models where prediction improves a business decision. Evaluation, reproducibility and monitoring are part of delivery.
Analytics & BI
Analytics & BI
Create semantic models, dashboards, self-service analytics and operational reporting around agreed KPI definitions. Focus on decisions and adoption, not dashboard volume.
Our success stories
View all case studies
.avif)
.png)
.png)
.png)
.png)
Stay informed
Explore our curated selection of recent news and articles that showcase our expertise in delivering tailored software solutions across various industries.
View all case studies
Tell us about your project needs
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Contact us and turn your project into a success story
Roman Reznikov
CEO & Partner
