Cloud data platform
Modern data lakes and warehouses on Snowflake, Databricks, and AWS, built for AI consumption.
Capabilities
Three programs, built to work together.
We work across data, AI, and business transformation at the same time, because progress in one depends on the others. So far that has meant platform modernization, self-service analytics, and $60M+ in annual benefit from AI and ML optimization.
Fig. 1: Modern Data Management
The data foundation enterprise AI depends on.
Cloud data platforms, pipelines, and governance that make enterprise data trustworthy and AI-ready, including in some of the most complex regulated environments in financial services.
Data foundations built for AIEstablished data models and pipelines that deliver reliable data at scale.
Data governance and quality
Lineage, quality controls, and compliance frameworks that meet regulatory standards.
Semantic layer and data products
Self-service data products and BI infrastructure that give teams consistent, trusted access.
DataOps and pipelines
Automated, monitored pipelines that scale reliably as data environments grow.
Key Capability
Semantic Layer
Self-service data products and BI infrastructure that give teams consistent, trusted access.
Fig. 2: AI and Agentic Transformation
Context Engineering: from data platform to AI that performs.
Our Context Engineering methodology gives AI agents the knowledge structures and operational controls they need to understand enterprise context and execute workflows reliably.
In production fasterAI solutions built on modern enterprise data principles, delivered at scale.
Agentic AI and automation
Agents that automate complex, multi-step workflows on production-grade infrastructure, not stranded proofs of concept.
GenAI applications
Production-ready applications for document processing, decision support, and customer-facing automation.
Context and knowledge architecture
The knowledge structures, infrastructure, and governance agents need to understand how your enterprise works.
AI platform and AIOps
Model governance, observability, and guardrails to run AI responsibly at scale.
Key Capability
Context Engineering
The knowledge structures and controls agents need to understand how your enterprise works.
Fig. 3: Business AI Transformation
Preparing the organization to work alongside AI.
Our Adaptive Enablement practice handles the organizational side of adoption: workflow redesign, upskilling, and change management. This is where the big wins are.
Scaled AI workflows in productionFaster decisions, lower infrastructure costs, and AI fluency built in.
AI strategy and roadmap
Business-aligned strategy, use case identification, and a phased roadmap tied to operational outcomes.
Human-AI workflow design
Redesigning workflows around human-AI collaboration to improve efficiency and reduce risk.
AI fluency and upskilling
Programs that build data literacy and AI capability across business, operations, and technology teams.
Change management
Program governance, stakeholder alignment, and execution discipline for complex transformation.
Key Capability
Workflow Engine
Redesigning workflows around human-AI collaboration to improve efficiency and reduce risk.
Three ways to start.
Pick the model that fits where your organization is today.
Strategy refinement
Sharpen your AI, data, or talent strategy and define the priorities that matter most.
Engineering support
ReKnew practitioners embedded alongside your team, from prototype through production.
Starter packs and accelerators
Pre-built frameworks and solutions that reduce time to value on common use cases.