One set of numbers everyone agrees on.
AI and reporting are only as good as the data under them. We build data platforms on a modern stack, with modelled data, a semantic layer and dashboards your executives actually use.
When you need it.
When two dashboards give two answers, people stop trusting both. The fix is shared definitions, tested data and a clear owner for each number.
- Different teams report different numbersRevenue, churn or headcount depend on who you ask.
- Reports are built in spreadsheetsAnalysts export, paste and fix data by hand each week.
- Data is slow to arriveDecisions wait on reports that are days old.
- AI projects stall on dataThe data AI needs is scattered, unclean or undocumented.
How it works.
The shape of what we build, from your data to the people who use it.
- SourcesApps, databases, files
- LoadInto your warehouse
- Modeldbt, tested
- Semantic layerShared definitions
- Dashboards & AIOne version of the truth
What we build.
The parts of a production system, each one tested and handed over.
Warehouse or lakehouse
A platform on Snowflake, Databricks or BigQuery, set up for your data volumes and access rules.
Data pipelines
Loading from your applications and databases on a schedule you can rely on.
Modelled data
dbt models with tests, so definitions live in code and errors are caught early.
Semantic layer
Shared metric definitions so every dashboard and tool uses the same numbers.
Dashboards
Reports designed with the people who use them, not just for them.
Data ready for AI
Clean, documented data that retrieval and AI features can build on.
What you get.
- A data platform on your chosen warehouse
- Tested data models with documented definitions
- A semantic layer for shared metrics
- Dashboards for the decisions that matter most
- Documentation and handover to your team
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Data platforms
- SnowflakeDatabricksBigQuery
- Modelling
- dbtSemantic layersData tests
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyWhich warehouse should we use?
It depends on your cloud, your team’s skills and your data. We work on Snowflake, Databricks and BigQuery and help you choose.
Can you work with the tools we already have?
Yes. We usually build on what you have and replace only what is getting in the way.
What is a semantic layer?
One place where business metrics are defined, like revenue or active customer, so every dashboard and tool calculates them the same way.
How does this help our AI plans?
AI features need clean, documented data. A good platform is often the first step before AI pays off.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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