DataJPS – All your data. Instantly clear.

The detective work nobody budgets for

Undocumented joins, forgotten dependencies and cross-system relationships that nobody wrote down tend to surface as defects late in delivery, once the assumptions built on top of them are already in production. Conventional discovery is manual, narrow and low-confidence: someone reads the schema, asks around, and hopes the documentation is still accurate.

We estimate analysts spend 60-80% of project time profiling data and tracing relationships – an internal figure, but one most data leaders will recognise.

DataJPS turns this manual investigation into an automated scan.

How it works, at a glance

Runs in your infrastructure. Relait is deployed inside your own environment and profiles your databases in place. Nothing is sent to DataJPS or any third party.
Fingerprints every column. Each column is reduced to a fingerprint of what it contains, and relationships are established from those alone.
Weighs the evidence. Relait weighs which columns genuinely hold the same information, how strongly, and in which direction – the same input always produces the same output.
Explore and decide. Query joins, lineage and identifiers at whatever confidence level suits the question, with history on every relationship.

See the mechanism in full at relait.io

What we build

The discovery engine. Relait maps how your data actually connects across systems – deterministic, reproducible, explainable, with history on every relationship.

Kindred, Relait's phonetic matching add-on, finds columns whose values agree in sound but not in spelling – candidates for identity-resolution rules that exact matching cannot surface.

Explore Relait

The conversational way in. Ask in plain English how your data connects, and Reeve answers from the evidence Relait has already assembled – grounded in the graph, not guessed.

Explore Reeve

Privacy is the architecture, not a feature

Relait runs entirely within your infrastructure – nothing is sent to DataJPS or any third party. It works on column-level aggregates and never joins back to individual source records. Relationships between columns become visible; the records behind them are never reconstructed. The guarantee is structural: it comes from the architecture, not from a policy promise.

Proof, not promises

A children's health service needed to understand how clinical data connected across systems – data that could not leave their on-premises environment. It did not. Relait profiled everything in place.

Across a typical data project, we model roughly a third off elapsed delivery time. It is a modelled figure – an expected order of magnitude, not a guarantee.

Built for regulated environments

Healthcare – clinical data never has to leave your environment.
Financial Services – an architectural guarantee your security review can test.
Government – citizen data mapped where it sits, without it moving.
Professional Services – evidence-based discovery that de-risks fixed-price delivery.