Data Platform Built for AI. With AI.
Every vendor claims to solve your problem. But only the right combination of tools and their configuration does the job.
AI consumes both structured and unstructured data
Data Quality is a problem. And Governance is human-intensive.
There are many tools. Which one to pick?
Five layers, from raw source to business value
Ingestion — capture everything, structured or not
We connect to your data wherever it lives: legacy systems, cloud SaaS, databases, Google Drive, SharePoint, shared file systems. Structured or unstructured, if it matters to your business, we bring it in. Most platforms quietly ignore the unstructured half of your data.
Legacy systemsCloud SaaSDatabasesGoogle DriveSharePointFile systems
Freshness depends on your needs. Real time is possible, but not always needed — we work with streaming and batch both. Determining what your data & AI use cases actually need is key.
Storage — a single, modeled home for your data
Raw data lands in a structured, properly modeled foundation, not a lake it disappears into. This is the difference between “we have data somewhere” and “we know exactly what we have, and can build on it.”
Graph databaseVector databaseTime series
Different data requires different storage. A graph database for your ontology, a vector database for documents, time series for IoT. We decide together which systems serve your needs best.
Governance — control who can access what
Access control, PII handling, policy, data contracts and stewardship: decided at the source, not bolted on afterward. Now that AI agents can reach more of your data than any dashboard ever could, governance is not a compliance checkbox — it is what lets you move fast without losing control.
Access controlPII handlingData contractsLineageMetadataCatalog
Data quality is measured and improved automatically. Agents understand your data model, spot issues and fix them before they become a problem downstream. Lineage, metadata and a data catalog are key.
Semantic layer — one definition of the truth
“Revenue.” “Active customer.” “At-risk account.” Defined once, consistently, for every dashboard, analyst and AI agent that asks the question. No more three teams, three spreadsheets, three different answers.
RevenueActive customerAt-risk account
The semantic layer safeguards consistent output and can pull from different sources — both structured and unstructured.
Consumption — where business value is generated
We bring data to the fingertips of your experts. Let AI agents query it directly. Generate dashboards in seconds instead of sprint cycles. Forward data to internal and external systems. Retrieve from a vector database for RAG. Or structure data as features for ML models.
AI agentsDashboardsRAGML featuresSystem sync
This is where the platform stops being infrastructure and starts producing value.
Pick the right platform that your business requires
Vendor-neutral, on principle
We don’t resell Databricks, Microsoft, AWS or GCP capacity, so the architecture call is based on your use cases and team, not a partnership target.
Agents that keep the data engineering work scalable
We onboard agents on the platform to build the platform, structure the data, spot data quality issues, and fix them before you know it.
Migration done right and fast
Traditionally, Data Migration projects can take months to years. Our approach is straightforward, automated, and fast, delivering results in weeks.
We run it for you or you run it yourself. Both work for us.
Zero hassle to maintain and run the platform? We can run it for you. If you have a data team in place, we collaborate, jointly build the platform and ensure they run it.
100+ companies trusted us to build their data & AI layer since 2018
- Mobility & Transport
- LLM Systems
- Buildings & Industry
- LLM Systems
- Climate Intelligence
- Mobility & Transport
- ML Systems
- Energy & Electrification
- Data Systems
- Mobility & Transport
- ML Systems
- Buildings & Industry
- LLM Systems
- Mobility & Transport
- LLM Systems
- Mobility & Transport
- ML Systems
- Mobility & Transport
- ML Systems
- Climate Intelligence
- Other
- ML Systems
Frequently asked questions
Are you tied to Databricks, Microsoft, AWS or GCP?
No. We’re not a reseller or certified implementation partner for any single vendor. The Design Sprint compares your options on their merits for your situation, and we build on whichever platform comes out ahead. “Asset” here means our Design Sprint methodology and reusable accelerators — not one fixed technology stack.
We already have a data platform — can you just clean it up, instead of migrating?
Often, yes. Not every messy platform needs a rebuild. Part of the Design Sprint is telling you honestly when cleanup and consolidation beat a full migration.
What do we own after this?
The platform itself, running in your own cloud environment, plus the documentation and code your team needs to operate and extend it. Nothing depends on our infrastructure or a proprietary layer.
How do you handle governance, security and compliance requirements (e.g. GDPR)?
Access control, lineage and compliance requirements are scoped in the Design Sprint and built into the platform from day one — ask us about your specific requirements. [PLACEHOLDER — compliance specifics not yet confirmed internally]
How long does the Design Sprint and migration take?
The Design Sprint is 3 weeks. Migration timelines depend entirely on scope — we’ll size yours after the sprint. [PLACEHOLDER — typical migration timeline not yet confirmed internally]