One trusted source of truth for your numbers and your AI.
We connect your apps, databases and SaaS tools into a clean, modelled warehouse, then build the dashboards and vector data your team and your AI systems rely on.
The problem. Our approach.
As a business grows, its data ends up in many places: the product database, the CRM, billing, spreadsheets. Reports get built by hand, two teams quote different numbers for the same metric, and nobody fully trusts the dashboard.
We bring that data into one place with scheduled, monitored pipelines, model it with tested transformations, and agree on metric definitions with the people who use them. Dashboards are built on top of that shared model, not on one-off queries.
The same foundation serves AI. Agents and copilots are only as good as the context they retrieve, so we prepare clean, current data and vector indexes that keep AI systems grounded in what is actually true in your business.
Ideal for
- Teams where finance, sales and product report different numbers
- Managers spending hours each week building reports by hand
- Companies preparing their data for AI agents and RAG
- Businesses whose dashboards are slow or no longer trusted
Pipelines & ETL/ELT
Scheduled, monitored data flows from your apps, APIs and SaaS tools.
Data warehouses
A modelled warehouse on BigQuery, Snowflake or Postgres with tested dbt transformations.
Dashboards & reporting
Self-serve dashboards with agreed metric definitions, replacing manual spreadsheets.
Vector data for AI
Chunking, embeddings and vector stores that keep AI agents grounded in current data.
Where it makes an impact.
- Unifying sales, finance and product data
- Automating weekly and monthly reporting
- Preparing company data for RAG and AI agents
- Fixing slow, conflicting or untrusted reports
What you get
- 01Data architecture and source inventory
- 02Production pipelines with monitoring
- 03Modelled warehouse with dbt tests
- 04Dashboards and metric definitions
- 05Documentation and team handover
Tech we use
From first call to production.
A first set of pipelines and dashboards usually takes 4–8 weeks, with more sources and models added in later iterations.
Inventory sources & questions
We list your data sources and the business questions people need answered, and agree on the key metric definitions.
Build the pipelines
We set up scheduled, monitored ingestion from each source into the warehouse, with alerts when data is late or wrong.
Model & test
We transform raw data into clean, documented models with dbt, and add tests so broken data is caught before it reaches a report.
Deliver dashboards & AI context
We build dashboards on the shared model and, where needed, the embeddings and vector indexes your AI systems use.
Outcomes
What changes for your team.
- 01
One agreed set of numbers across teams
- 02
Reports that update themselves instead of being rebuilt by hand
- 03
Data problems caught by tests and alerts, not by a confused meeting
- 04
Clean, current context ready for AI agents and copilots
Data Engineering & Analytics, in depth.
Data Engineering & Analytics FAQ
Can't find what you're looking for? Send us a message and we'll get back to you within one business day.
Do we need a data warehouse?
Not always. If your reporting needs are simple, we'll start lighter. A warehouse pays off once data lives in several systems and reports start disagreeing.
Can you work with the BI tool we already use?
Yes. We model the data so it works with your existing tool, whether that's Looker Studio, Power BI, Metabase or another.
BigQuery, Snowflake or Postgres?
It depends on your data volume, existing cloud and budget. Postgres is often enough for smaller teams, while BigQuery or Snowflake make sense as volume and the number of users grow.
How do you handle sensitive data?
We limit access by role, mask or exclude personal fields that analysis doesn't need, and keep data within the region and accounts you choose.
How does this help with AI projects?
AI agents need clean, current and well-described data to give correct answers. The same pipelines and models that power your dashboards can feed the retrieval layer of your AI systems.
Pair it with the rest of the stack.
Start a project
Have a project in mind? Let's build it.
Tell us what you're working on. We'll reply within one business day with ideas, a plan and next steps.