One home for your data, your documents, and what they mean.
We pull your systems and files into one governed place, so finance, operations, and AI all read the same number.
Three versions of the same number
Most companies doing $5M to $50M in revenue run on five to fifteen systems: ERP, CRM, a property or reservation system, payment processors, spreadsheets, and shared drives.
Each team exports its own copy. Revenue, occupancy, or on-time rate ends up with three definitions, and the leadership meeting starts with an argument about whose number is right.
Contracts, manuals, and email threads hold the other half of the answer. Nothing can search them next to the numbers.
AI makes this worse, not better. A model pointed at messy data gives confident, wrong answers faster.
What's under the hood
Bronze. Raw data lands unchanged and can be replayed. Ingestion uses Lakeflow Connect or Auto Loader, batch or streaming as the source allows.
Silver. We validate, deduplicate, and model data around your real business entities: customer, property, booking, shipment, payment. Data-quality rules are written as pipeline expectations, so bad records are flagged or held back.
Gold. These are aggregated tables and metric views built for reporting. Each metric is defined once and reused in dashboards, notebooks, and AI tools.
Documents. Contracts, manuals, and policies are chunked and indexed for hybrid keyword and semantic search, governed by the same catalog as the tables.
Governance. Unity Catalog holds owners, access rules, tags, and lineage for every table and index. Everything is stored in open formats (Delta Lake, with Apache Iceberg compatibility available), so you are not locked into one vendor.
Signs you need one
Month-end takes too long
Someone spends days reconciling exports before the numbers can be shown.
Metrics don't match
Finance and operations report different figures for the same thing.
Reports live in one person's head
If your spreadsheet expert is out, the report doesn't get built.
AI pilots stall
A chatbot or agent test fails because nobody trusts the data underneath it.
The business steps
Map
We list your sources, who owns each one, and the five numbers leadership argues about most.
Agree
We write one definition for each of those numbers with the people who use them, and record it in a shared glossary.
Build
We load the data, clean it, and publish tables that match the agreed definitions.
Hand over
Your team gets the tables, the glossary, a cost baseline, and a roadmap. You can extend it with us, on your own, or with anyone else.
Deliverables
A source-system map with an owner named for each source.
Ingestion pipelines for the first slice of sources (bronze).
Cleaned, conformed tables (silver) and a business glossary.
Gold tables and metric views for your core reports.
A searchable index of your key documents, next to the tables.
Unity Catalog set up with owners, access rules, tags, and lineage.
A cost baseline and a written roadmap for the next sources.
The platform that defined the lakehouse
Databricks engineers described the lakehouse architecture in a 2021 research paper: open file formats with warehouse features like transactions, versioning, and governance on top. Apache Iceberg started at Netflix and Apache Hudi at Uber to solve the same problem. We build on that pattern at mid-market scale: medallion layers, Unity Catalog for governance, Lakeflow for pipelines, and AI Search for documents. It runs in the cloud account you already have.
A working first slice in 2 to 4 weeks
Timing depends on system access and your team's availability.
Map and agree
Source inventory, access, and definitions for the five contested numbers.
Land and refine
Bronze and silver for the first sources, with quality rules in place.
Serve and hand over
Gold tables, document search, catalog permissions, cost baseline, and roadmap.
Every table and document has an owner, an access rule, and a lineage trail back to its source. Access control, audit logs, tagging of sensitive fields, and data-quality monitoring are set up from day one, not added later. For a deeper security assessment, we bring in a trusted partner.
References
Armbrust, Ghodsi, Xin, and Zaharia, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics, CIDR 2021. https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf
What is the medallion lakehouse architecture? Databricks documentation. https://docs.databricks.com/aws/en/lakehouse/medallion
What is Unity Catalog? Databricks documentation. https://docs.databricks.com/aws/en/data-governance/unity-catalog/
Databricks AI Search (formerly Vector Search). https://docs.databricks.com/aws/en/generative-ai/vector-search
Bring the number everyone argues about.
In a free discovery session we map where it comes from and what a first slice would look like. Or try a question in the Lab first.