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 help banks, payments companies, and insurers put AI workflows into production, led by people who ran data and AI at the world’s largest financial institutions.
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 and Context management
Data products, 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 and capability give 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.
Agent Reliability Engineering
Model governance, evals, observability, and guardrails to run AI responsibly at scale.
Key Capability
Context Engine
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 Workflow Engineering 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
Always-On Agentic Workflows
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 business strategy and define the priorities that matter most.
Engineering support
ReKnew experts embedded alongside your product, engineering and delivery teams, from prototype through production.
StarterPacks and accelerators
Pre-built frameworks and solutions, expertly engineered to reduce time to value on Enterprise use cases.