The data platform that evaluates datasets for AI readiness — and turns the evaluation into recommendations, an executable action plan, and a quantified ROI.
Built for life sciences · Your data never moves · Zero egress
Clean data is not ready data. A dataset can pass every hygiene check and still be the wrong shape for the model, silent on provenance, ambiguous to a machine, or out of compliance. Teams discover the gap in production — the most expensive place to find it.
…until they fail in production.
for autonomous and AI-assisted decisions.
Databricks, Snowflake, Veeva — never assessed together.
Which fixes actually move readiness?
“Everyone says their data is AI-ready. Almost no one has measured it.”
DATA Compass evaluates datasets for AI readiness across four dimensions, grounded in FAIR — then turns every finding into a specific, executable recommendation and a quantified ROI, so AI investments succeed instead of stalling in a proof-of-concept.
Score the dataset across four dimensions of readiness, grounded in FAIR.
Bridge AI turns findings into prioritized, causal recommendations.
Every recommendation ships an executable remediation — an action plan, not a report.
Quantify the readiness lift and the ROI of each fix.
Everyone sells FAIR and stops. Compass treats FAIR as the foundation — then measures the four dimensions of readiness FAIR enables but does not guarantee, with Bridge AI synthesizing it all into what to do next.
FAIR makes data legible to machines and people — findable, accessible, interoperable, reusable. That is necessary, and it is not sufficient. A perfectly FAIR dataset can still be statistically broken, wrong for your use, non-compliant, or semantically ambiguous. Compass treats FAIR as the substrate and measures what comes next.
“We treat FAIR as table stakes — and measure the readiness it can't.”
Compass is an API overlay, not a data lake. It connects to your warehouse, runs read-only queries through your own compute, and analyzes the results in memory. No ETL, no copies, no new storage. Source data never leaves your environment — the answer to the first question every pharma security review asks.
Compass doesn't take the data's word for it. Every FAIR assessment triangulates three independent reads. Where they agree, you have a finding you can defend. Where they diverge, the divergence itself is the insight — and it's named, not hand-waved.
Whether a machine can resolve the meaning of your data unambiguously, against the ontology you actually use. Compass reads your private ontologies, infers which columns map to which classes, and scores the alignment.
Whether the numbers hold up before a model ever touches them. Compass runs two engines — data quality and ML readiness — across 50+ algorithms, from distribution and anomaly detection to bias, class separability, and feature sufficiency.
A dataset that's clean, sound, and FAIR can still be wrong for the job in front of you. Contextual validity scores fit-for-purpose against a declared use — using customer-authoritative Domain Packs that encode your sub-domains, archetypes, and rules.
In regulated work, readiness includes defensibility to a regulator. Compass scores governance evidence against the frameworks pharma actually answers to — line by line, with citations — so compliance is measured, not asserted.
Four dimensions produce a lot of signal. Bridge AI is the synthesis layer: it runs causal analysis across the findings, separates root causes from symptoms, and writes prioritized recommendations in plain language — each one traced back to the evidence that produced it. No black-box conclusions; every claim cites its source.
Why the score is what it is — and what fixing it unlocks.
With an effort estimate on every action.
From finding to source — auditable end to end.
“From four-dimensional signal to a short list of things worth doing.”
Compass is not a scorecard that rots on SharePoint. Every recommendation routes to a structured, executable action plan — an auto-generated remediation script you can read, edit, and run. The deliverable is momentum, not a verdict.
A Python script per finding — not a to-do line.
No re-keying scores into a separate tracker.
The whole chain survives an audit.
Every gap carries a cost and every fix carries a return. Compass quantifies the business value of closing each gap — grounded in the Pistoia Alliance FAIR business-value model — so a data-readiness program competes for budget on the same terms as everything else: ROI.
Rank the backlog by impact, not by who shouted loudest.
Level-0 outcomes decomposed into measurable benefits.
The readiness program earns its line item.
“The first FAIR conversation a CFO will sit through.”
Readiness isn't a yes/no. Compass tells you which level of AI autonomy your data can support today — and exactly what to fix to climb to the next one.
Native connectors for enterprise data platforms. Assess data in place — without extraction or movement.
Unity Catalog browser, SQL profiling, and MLflow model assessment via PAT or OAuth2.
Database and schema browser with SQL API profiling for warehouses and data shares.
QMS, CDMS, and RIM module adapters with OAuth2 SSO and Direct Data extraction.
SPARQL querying, ICV constraint validation, and reasoning-powered ontology analysis.
Drag-and-drop CSV, Excel, Parquet, JSON — or a REST API for CI/CD and pipelines.
Get a full data-readiness assessment in minutes. No code changes, no data movement, no black boxes.
Or download the self-hosted installer and assess your first dataset in under five minutes. No signup required.