Bad data doesn’t just produce bad reports. It blocks AI, slows decisions, creates compliance exposure, and erodes confidence across every team that depends on it. We build the governance frameworks, data quality systems, and master data foundations that make your data an asset you can actually rely on.
Before
Analyst teams manually reviewed ~28K trades per week hunting for compliance risks. Rules-based alerts generated high volumes of low-priority noise.
FIS · Financial Services
→
After
AI automatically classifies 100% of compliance risks. Multivariable risk scoring isolates legitimate threats. Analysts focus on what matters.
~98.5% reduction in manual review
Before
Marketers manually wrote copy and product descriptions across seasons, personas, and thousands of SKUs — one at a time.
Under Armour · Retail
→
After
An AI generation pipeline creates editable product detail page content and optimized copy on demand, at scale.
86% faster · 6 weeks → 6 days
Before
Locking in forecast edits and carrying over promotional elements drove delays of over 48 hours per planning cycle.
Conagra Brands · CPG
→
After
Automated pipelines with dynamic tables and Snowpark eliminate manual carryover. Forecasts process every cycle without delay.
26% faster forecast processing
Systems
Data you can trust. At enterprise scale.
Every automation follows a specific architecture. Data flows in, AI processes it in real time, and the right output reaches the right person, agent, or system. Each implementation is tailored to the client's data and workflow.
Data Governance Architectures →
Not a one-time audit
Governance Built Into the Lifecycle
We don’t deliver a governance report and walk away. Every engagement operationalizes certification standards, ownership models, and quality checks directly into the data delivery pipeline - so governance compounds over time.
Not generic frameworks
Domain-Specific, Business-Aligned
Governance frameworks that don’t map to real business domains don’t get adopted. We build around your specific domains, assign real ownership, and train the people responsible for sustaining it.
Not siloed metadata
End-to-End Lineage and Trust Signals
We don't deploy a generic tool. Every automation is designed around your specific data sources, business rules, and operational needs.
Not theoretical
Production Governance at Scale
From regulated banking environments to global CPG operations to healthcare diagnostics networks. Every framework we deliver runs in production, handles real data volumes, and meets real compliance requirements.
"We felt like we were driving a Volkswagen before, now it feels like we're driving a Rolls Royce."
Jyothi Chennu, Chief Technology Officer · Texas Capital Bank
Success
Real Results Across Industries.
Not presentations. Not projections. These are production governance systems operating live for global enterprises at scale.
72%
Reduction in implementation efforts
Texas Capital Bank
5 months
To end-to-end data governance
Texas Capital Bank
762
Governance-approved metrics centralized
Perdue Farms
Client results
More results, more industries.
Trusted data and governance frameworks delivered across banking, CPG, healthcare, financial services, and wealth management.
Client
Governance type
Result
Texas Capital Bank
Banking
Three siloed data sources consolidated into Snowflake Data Vault. Modern data architecture implemented. Data quality maturity built. End-to-end governance delivered.
72% reductionin implementation efforts · End-to-end governance in 5 months
Perdue Farms
CPG
49 Essbase cubes converted to relational facts and dimensions on Snowflake. Centralized data model with governance-approved metrics across a major CPG operation.
Snowflake-native MDM application unifying 30+ siloed financial instrument data sources. Business-user control over mastering rules delivered.
40% efficiency · 65% cost reductionin data management operations
Kraft Heinz
CPG · Global Food and Beverage
Domain-oriented data product model in Snowflake and Collibra. Certification standards operationalized. Collibra as system of record for metadata, lineage, and ownership.
Structural proof
Fully certified data products delivered. Repeatable governance blueprint established for all future domains.
Sonora Quest Laboratories
Healthcare Diagnostics
Data Governance Council established and guided through year one. Client and patient domains implemented. Business glossary, data catalog, and data literacy training operationalized.
Structural proof
Full governance framework delivered. Data literacy programs deployed enterprise-wide. Governance adoption sustained.
BetaNXT
Wealth Management Technology
Modern data architecture on zero trust infrastructure and Data Vault 2.0. End-to-end governance overlay via data.world. Enterprise-wide tagging and masking policies implemented.
Structural proof
Multi-lane plug-and-play data sharing platform delivered. Enterprise tagging and masking policies ensuring compliance and security.
Vistra Energy
Energy
Alation data catalog implemented for metadata management and data discoverability. Governance adoption program delivered across the organization.
Structural proof
Data catalog operationalized. Governance adoption and discoverability improved enterprise-wide.
Take a Look
Banking
Data Governance Transformation at Texas Capital Bank
▶
CPG
Certified Data Products at Kraft Heinz
▶
The Data Innovation Journey
Stage 01
Chaos
→
Stage 02
Order
→
Stage 03
Insight
→
Stage 04
Innovation
Don’t Get Left Behind
Stage 01
Chaos
Data is scattered across siloed, legacy systems. No unified visibility. No reliable insight. No foundation for intelligence, automation, or modern AI innovation.
Stage 02
Order
A modern data stack is in place. Data is centralized, pipelines are clean, and the organization can finally trust what it’s looking at.
Stage 03
Insight
Data operates as a trusted asset across the organization. A single source of truth drives reliable intelligence, faster decisions, and measurable business impact at every level.
Stage 04
Innovation
Organizations at this stage have fully adopted and are operating on a modern data stack, deploying AI for automation and intelligence, launching data products, and opening revenue streams that weren’t possible before. With an AI-first infrastructure and mindset in place, they are positioned to move faster on emerging technology than slower adopters can, leaving the competition behind.
