We design and implement pragmatic analytics platforms on DuckDB, ClickHouse, dbt, and Iceberg - so finance and operations teams get faster reporting, lower warehouse spend, and a cleaner path to AI‑assisted analytics. Results in weeks, not months.
Manual Reporting
Analysts spend hours copy-pasting between Excel, ERP, and CRM instead of analyzing data.
Rising Warehouse Bills
Cloud warehouse costs keep growing faster than your data volume - and the budget conversation gets harder each quarter.
Disconnected Data Sources
ERP, CRM, spreadsheets, and ad-hoc exports live in separate silos. No single source of truth for the business.
Slow Month-End Close
It takes days to produce financial or operational reports that leadership needs for decisions - too slow, too manual.
All your data in one place, dashboards in seconds
Modern lakehouse architecture that replaces scattered databases and spreadsheets. Real-time analytics on an open-source stack - significantly lower cost than managed cloud warehouses.
Ask your data questions in plain language
AI-First approach to data analysis. LLM agents that understand your business context, answer questions about your data, and generate insights automatically.
CI/CD for data pipelines, AI quality control
Automated data pipelines with version control, testing, and AI-powered anomaly detection. Infrastructure as Code for reproducible, auditable data systems.
Painless transition from Excel, legacy DWH, or Snowflake
Zero-downtime migration from spreadsheets, legacy databases, or expensive cloud warehouses to a modern open-source Lakehouse. No vendor lock-in.
Governed metrics, reliable dashboards, self-serve reporting
dbt-based transformation layer with built-in tests, documentation, and version control. Connect your BI tool to a single source of truth instead of ad-hoc queries.
We analyze your current data setup, identify bottlenecks, and estimate ROI.
We design a Lakehouse architecture tailored to your data volume, budget, and cloud.
We build and deploy using Infrastructure as Code - reproducible, versioned, automated.
Ongoing monitoring, optimization, and AI-powered anomaly detection for your data systems.
Most projects begin with an audit or pilot. Scope, number of data sources, and rollout constraints determine final cost and timeline. Our open-source stack (DuckDB, ClickHouse, Iceberg) runs at a fraction of managed warehouse costs - we share specific estimates after the discovery call.
MVP with core dashboards is ready in 4-8 weeks. Full implementation with AI analytics and automated pipelines takes 3-6 months. You start seeing value from week 2.
We start with a free data audit call to understand your current setup, data volumes, and business goals. Then we propose an architecture and migration plan. Migration happens incrementally - your team keeps working in Excel until the new system is validated.
We use open-source technologies (DuckDB, ClickHouse, Apache Iceberg) with no per-user licensing. Compute is the biggest cost driver: DuckDB is free, and ClickHouse has predictable pricing. The result is significantly lower total cost - exact savings depend on your workload and data volume.
AI agents connect to your data and answer business questions in plain language. Instead of writing SQL or waiting for analyst reports, executives ask questions directly and get answers with charts in seconds.
Yes. We run the new Lakehouse in parallel with your existing systems. Data flows to both until validation is complete. Then we switch over with zero downtime.
Yes. We have Terraform modules for all three cloud providers, plus on-premise and hybrid deployments. The same open-source stack works everywhere - no vendor lock-in.
A strong first phase should change the reporting workflow, not just the tool list. These are the practical outcomes we usually aim to deliver before a full-scale rollout.
Month-end and operational reporting moves from spreadsheet assembly to scheduled dashboards and validated metrics.
ERP, CRM, exports, and spreadsheets are mapped into a single analytical layer with clearer metric definitions.
Recurring copy-paste, reconciliation, and refresh tasks are automated so analysts spend more time on analysis.
Old and new reporting paths run in parallel until stakeholders sign off on validated outputs.
Typical first phase: audit, target architecture, initial pipelines, validated dashboards, and a rollout plan.
Exact outcomes depend on data quality, source complexity, and rollout constraints.
CTO & Co-Founder
Directly involved in audit, architecture, and rollout planning. The engagement is led by a senior practitioner, not handed off to a junior delivery team after the first call.
15+ years in data engineering and analytics
Reporting migrations and warehouse cost reduction
Audit to working MVP in 4-8 weeks
A Data Lakehouse combines the best of data warehouses and data lakes into one architecture. Learn why companies are switching to open-source analytics.
A practical comparison of ClickHouse, DuckDB, and Snowflake: architecture trade-offs, cost models, and use case recommendations. Updated for 2026.
An anonymized client case study: migrating from Excel and legacy databases to a Data Lakehouse. Faster reporting, less manual work, lower infrastructure costs.
Book a free 30-minute discovery call. We'll review your current data setup, identify reporting bottlenecks, and outline realistic next steps.