The most forward-looking enterprises are engineering intelligence systems that continuously sense, reason, and act, rather than treating data as back-office infrastructure.
Where intelligence systems can change the economics of the business
Defining the enterprise data and AI ambition, readiness, and your transformation priorities
Prioritizing initiatives by value, feasibility, and strategic leverage
Aligning cloud, data, and AI investments to priorities, operating realities, and scale requirements
Setting ownership, controls, KPI targets, and adoption plans from day one
Engineer the trusted foundation intelligence systems require
Building lakehouse, warehouse, and streaming foundations for AI-native workloads and rapid productization
Moving from batch-bound architecture to event-aware, decision-ready data flows
Strengthening trust with master data, quality controls, lineage, and policy-aware access
Integrating consumption analytics and model feedback into the foundation to refine data products over time
Grounding AI systems with business context to reason, decide, and act correctly
Creating a shared source of meaning across metrics, entities, policies, and workflows
Grounding copilots, assistants, and enterprise search in trusted business context
Ensuring AI outputs and actions remain explainable, governed, and aligned to enterprise rules
Giving teams and intelligence systems the shared business context required for safe, auditable action
Evoq industrializes intelligence systems, turning execution into reusable scale
Delivering data and AI initiatives up to 50% faster with reusable patterns, frameworks, and automation
Structuring focused pilots in as little as 4-8 weeks to prove value quickly
Working across AWS, Azure, Snowflake, Salesforce, Databricks, Google Cloud, and adjacent ecosystems
Turning proven approaches into repeatable assets, operating standards, and scalable delivery models
Business economics that leadership can measure and operating teams can sustain
Through better personalization, sharper targeting, and more intelligent customer interactions
Through modernized data estates, AI-enabled automation, and optimized IT spend
Replacing fragmented experimentation with scalable delivery, reusable assets, and measurable outcomes
With responsible AI, stronger governance, and better readiness for enterprise adoption
Evoq powers AI-native intelligence systems with reusable and autonomous enterprise execution.
Operating layer for autonomous execution
Event-driven, ML-powered fraud detection platform processing 4,000+ transactions per second for one of America's largest credit card issuers.
A top-3 US credit card brand needed to combat increasingly sophisticated fraud at scale while migrating 50TB of legacy data without disrupting live transaction processing for 40M+ active cardholders.
Comprehensive AIOps-powered monitoring framework on AWS serving millions of fans across 40+ countries.
A global live entertainment company managing tens of thousands of events annually struggled with CMDB performance bottlenecks and limited monitoring capabilities on AWS, risking disruption to mission-critical operations.
Advanced self-service portals and custom applications connecting every stage of the guest journey with complex third-party integrations.
An ultra-luxury cruise line needed to digitally enable the entire guest journey with self-service portals and complex integrations across shoreside and shipboard systems, while maintaining exacting luxury brand standards.
Cloud infrastructure and legacy modernization enabling real-time operational visibility and revenue validation for distributed recycling operations.
Outdated technology stack, zero field visibility, and inability to validate solvent recovery for revenue recognition.
