Data foundations for AI
AI applications run on data that already sits in your ERP, MES, CRM, and the spreadsheets around them. Before anything is built, we work out how that data is structured, where each field comes from, what it means, and whether it holds up under use. That assessment usually changes the plan, because some use cases turn out to be ready and others have to wait for the source systems to catch up.
The six layers underneath an AI application
Before development starts, we map how the work moves through the operation and where the data behind it lives. Weakness in the lower layers shows up during the build as integration work nobody scoped, or as output the business will not sign off on. We assess all six at the start of an engagement and fix what a specific use case depends on, rather than rebuilding the whole stack first.
How the work moves through the operation: the steps, the handoffs, and the decisions people make at each one. We document this before automating any part of it.
Which systems hold which data and how they connect. We scope the integration work here, before a build starts depending on it.
The entities and their relationships: what counts as a customer, an asset, or a shipment in your systems, and how those records tie to each other.
Where each field comes from and how it is transformed on the way, so a number in a model output can be traced back to its source system.
Definitions, ownership, freshness, and quality for each field. Without it, two teams can pull the same measure from the same table and report different numbers.
One authoritative version of each core entity. Where customer or item lists have diverged across systems, they get reconciled before anything is built on them.
One modeled data layer
Source systems feed one operating data layer where the data is cleaned, joined, and tracked for lineage and metadata. Dashboards, models, and the applications we build read from that layer instead of from separate extracts, so a definition changed in one place changes everywhere it is used.
How we measure data quality
A field can be modeled correctly and still be unusable. Before anything is built on it, we profile the source data against six dimensions and report what we find, including the cases where a use case should wait until the source system improves.
Matching the technique to the problem
A recurring check against fixed criteria is usually a rules or SPC problem. Allocating constrained capacity is an optimization problem, and a number that drifts is a forecasting one. Learned and generative models go where there is no stable rule to write down and the pattern has to come from the data. Simpler techniques cost less to run and are easier for your team to maintain, so we start with the simplest one that moves the number.
Value against data readiness
Every candidate gets two scores: what the improvement is worth, and whether the source data can support it. Value is estimated with the business owner and finance, so the number is one they will recognize when the work is reviewed later. Readiness comes out of the data quality work above. Candidates then sort into four groups.
High value, and the data is ready. Fund it now.
High value, with data gaps. Prove it on a narrow slice before committing.
Worth doing, not yet feasible. Revisit when the data or readiness improves.
Low value, or handled without a model.
Bring us one use case you want to build.
We will look at the source data behind it and tell you what it would take to make that data usable.