Financial ServicesScale AI Without Rebuilding Your Data Stack

AI success depends on the right data foundation. DataOS unifies governance, access, context, and interoperability across your existing systems so teams can deploy AI faster and scale it with confidence.

Get AI Ready In Weeks

Recognized by experts. Proven by results.

Accelerate AI, Analytics & Operations with DataOS

Connect data from all your different siloes - legacy and new - so your business has the data it needs, the way it needs it in.

Governance

Embedded governance for AI and analytics with native access controls, automated lineage, and policy enforcement. Stay audit-ready for BCBS 239 and NCUA exams without manual reconstruction.

AI Enablement

Transform fragmented banking data into trusted, semantically consistent data products that support GenAI initiatives and accelerate enterprise AI deployment.

Analytics

Reduce time spent searching for data by up to 80% . Risk, finance, and compliance teams can answer questions faster without relying on engineering support or disconnected reporting systems.

AI/ML

Data scientists get clean, versioned, lineage-tracked data with business context packaged with the data. Every model input is traceable to source, and feature pipelines update automatically as upstream data changes.

Operations

Automate loan processing, compliance review, onboarding, and other operational workflows with governed data pipelines that reduce manual effort, delays, and data quality issues.

Proven Results

Case Study: A leading UK bank

Years of compliance backlog and disconnected data systems had slowed underwriting decisions across the organization. By deploying DataOS to unify claims, transaction, and external signal data, the bank enabled real-time claims decisions with no underwriting delays caused by data issues.

700x faster

160x faster

24x acceleration

Faster PII/SCPD tagging. A process that took a month now takes minutes.

Faster metadata cataloging. Weeks of manual work automated.

Acceleration in data profiling and quality checks.

Go from re-building pipelines to creating impact

Most banks spend 70–80% of their data engineering budget in maintenance that deliver the same information to the same teams, over and over. DataOS packages that work into reusable data products, governed, versioned, and ready to consume, so your teams stop reinventing the wheel and start focusing on innovation.
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One data product, built once. Consumed everywhere.

Get AI Ready In Weeks

Traditional Approach

Slow, costly, inefficent

With DataOS

Launch new use cases
80% faster

Within the first quarter, the average DataOS customer eliminates 60–70% of repetitive data engineering work, and reduces TAT by 80% for new development

Built for Regulated, Distributed Data Environments

Semantic Layer

Standardized business definitions, metrics, and relationships enforced across all data sources, so every report, dashboard, and AI model uses the same numbers and the same meaning, regardless of which system the data came from.

PII Classification

Automated discovery, tagging, and masking of sensitive customer data across the entire data estate — privacy and compliance controls built into the pipeline at source, not added at audit time.

AI-Ready Data

Clean, contextual, governed data products that LLMs, ML models, and analytics tools can consume directly. Structured for AI from the moment it enters the platform. No manual prep or transformation needed.

Data Quality

Continuous checks, anomaly detection, and lineage tracking across every data pipeline — errors are caught and quarantined at the source before they reach reports, models, or regulatory filings.

M&A Integration

Source-aware harmonization that unifies data from acquired companies across schemas and core systems, without waiting for the full consolidation. Business value from day one post-close, not month twelve.