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
Delivered a cloud-first transformation on Azure with a Trusted Data Foundation and modern analytics, migrating core applications, optimizing databases, and replacing legacy reporting with self-service dashboards.
Brittle on-premises systems threatened the board's NCCA accreditation, required costly maintenance, created security vulnerabilities through deprecated components, and lacked any data strategy for actionable insights.
Achieved 99.9% uptime with automated disaster recovery, cut infrastructure maintenance costs by 35%, reduced administrative overhead 40% through self-service, and enabled real-time analytics across 15-plus KPIs.
Modernized a legacy Dealer & Distributor Management System with an AI-powered DDMS on Salesforce Consumer Goods Cloud, enhanced with Agentforce and offline-first mobility across 12 states.
As the brand scaled to 1,200-plus distributors and 450,000-plus retailers, manual applications, disconnected systems, and legacy infrastructure slowed dealer onboarding, disrupted sales, and limited visibility.
Cut onboarding time by roughly 60%, reduced dealer support volume 40% through AI self-service, and delivered a unified Dealer 360 with offline-first field execution on a platform built for scale.
Led a 13-year, API-first payments modernization with microservices, React micro-frontends, and AI-led engineering across cards, BNPL, POS, and digital wallets.
Years of product-specific builds left the bank with siloed systems, duplicated components, and inconsistent delivery, while legacy frameworks, manual test cycles, and evolving PCI requirements slowed releases and raised risk.
Reached roughly 500 releases annually with more than 70% automation and under 1% production defects, delivering about $4M in annual savings on a low-latency, PCI-compliant platform ready for new geographies.
