Reliable source records
Cleaned, connected records with stable IDs and documented field meanings, so reports and applications agree.
Data and AI foundations
We clean and connect the records a report, application, or model depends on, from duplicate IDs to undocumented calculations and access gaps.
ScopeA single pipeline, report, or model, up to a set of connected sources
Delivery timingWeeks for a well-defined first release when data access and definitions are agreed
How we check itWe check the first report or application against approved source records before extending the work.
Fix the inputs before adding AI
New dashboards and AI models inherit the problems in the records they use. We begin with the data one application needs. For example, a customer report may need consistent account IDs and agreed revenue definitions before any new reporting will help.
Good fit
- Organizations held back by operational data scattered across systems
- Teams whose calculations live in individual spreadsheets and dashboards
- Software products that need a dependable shared data model or event history
Not the right fit
- Data migrations with no report or application that needs the result
- Gathering more data before anyone knows how it will be used
- Writing data policies before any system needs them
What you get
Quality controls
Automatic checks that data is valid, matches its source, and is up to date, with a process for fixing failures.
First report or application
An approved data model or feed that supplies one specific report, application, or AI feature.
How it works
Start from one report
We trace the first report or application back to the records and definitions it depends on.
Repair only what it needs
We correct mismatched IDs, formats, missing values, and access rules for that use, without rebuilding everything else.
Check the results
We compare outputs with source records, test scheduled refreshes and recovery, and agree who owns each source.
Scope, cost, and ownership
What we need from you
- A specific report or application and people responsible for its sources
- Representative records and agreed access permissions
What affects cost
- Source quality and ID consistency across systems
- Data volume, refresh frequency, and how much history to keep
- Security and reconciliation effort
Technical scope
- Data modeling
- Warehouses and pipelines
- Data contracts (agreed formats between systems)
- Row-level access rules
- Datasets from user feedback and corrections
- Power BI
Support and maintenance
Each source needs someone who can correct its records and explain its fields. We also agree who responds when a data check fails.
Common questions
Is this a data-platform engagement?
Only when the work calls for one. A single pipeline or a repaired data model may be enough, so we scope the work around the report or application that needs the data.
Can you work with Power BI?
Yes. Existing Power BI reports often hold the business rules and review steps an AI project needs.