A Real Enterprise Problem
A complex engineering, operational, or business challenge where traditional approaches fall short and GenAI can create meaningful value.
From Enterprise Problems to Production-Ready AI
We work with anchor customers to solve complex enterprise problems that traditional approaches cannot address effectively. Through our AI Pods, we co-invest alongside customers to engineer, validate, and operationalize production-ready GenAI solutions that create measurable value and can scale into reusable capabilities.
Our Model: Co-Innovation With Anchor Customers
Every GenAI solution starts with a real enterprise problem worth solving, a committed customer, and a business case strong enough for us to invest alongside them.
A Committed Anchor Customer
We work alongside customers who bring the domain context, enterprise data, workflows, and real-world environment needed to shape and validate the solution.
A Clear Business Case
Before we build, we establish the expected value, success measures, adoption path, and potential to scale.
Our Co-Investment Model
We Co-Invest to Build What Doesn't Exist Yet
Through focused AI Pods , Relevance Lab brings engineering expertise, GenAI capabilities, and our own investment to solve the problem alongside the customer. We build and validate the solution in a real enterprise environment, with shared commitment to achieving measurable outcomes.
Enterprise GenAI Agents Built for Real-World Operations
Domain-specific AI agents engineered for complex enterprise workflows, with intelligence embedded where data, systems, and decisions come together.
Agentic Enterprise Lifecycle
Build
Ingest
Reason
Regulate
Operate
Learn
Research & Higher Education
Research Assistant
Conversational GenAI Agent for Researchers
Adds a cognitive automation layer to Research Gateway, helping researchers and IT teams navigate cloud and HPC workflows through conversational guidance, recommendations, governance insights, and intelligent actions.
Digital SRE & Cloud Operations
Jasper
AI-Powered Digital SRE
An always-on AI SRE engineer that investigates operational issues using live enterprise evidence to diagnose problems, correlate findings, verify conclusions, and accelerate resolution.
Data Quality & Intelligence
IngestIQ
Hybrid Semantic Intelligence for Enterprise Data Quality
Moves beyond static data-quality rules by using semantic intelligence to understand changing data, infer mappings and transformations, and keep execution controlled, reproducible, and auditable.
Data & Analytics
GraphBridger
Knowledge Graph Driven Analytics Agent
Brings semantic intelligence to enterprise data by connecting entities, relationships, and intent through knowledge graphs, enabling contextual reasoning beyond static dashboards.
Clinical Trial Supply Chain
Sally
Clinical Trial Supply Chain Assistant
Embeds predictive intelligence into clinical trial supply workflows, bringing together fragmented supply data to improve visibility, identify risks, and support better planning with human-in-the-loop recommendations.
Clinical Intelligence
CTIA
Clinical Trial Intelligence Agent
Synthesizes multi-source clinical trial data into protocol-compliant, audit-ready patient narratives and safety intelligence while preserving regulatory control and traceability.
Software Engineering
GenAI Remediation & Test Automation Agent
AI-Powered Software Engineering Automation
Brings GenAI into software engineering workflows to identify code issues, accelerate remediation, automate testing, and improve test coverage while keeping engineering teams in control.
AI-Powered Engineering Workflow
Analyze
Identify code issues
Remediate
Accelerate code fixes
Test
Automate test generation
Improve
Increase test coverage
Built for enterprise environments — not just AI demos.
Production-ready
Engineered for real operational workflows.
Governed by design
Controls, traceability, and policy-aware execution.
Human-in-the-loop
Human validation where enterprise decisions require it.
Domain-specific
Built around the systems, data, and workflows of each domain.
Extending Enterprise AI
kaman.ai: Autonomous Multi-Agent Systems for the Enterprise
Our GenAI agents solve specific enterprise problems. kaman.ai extends this approach with an enterprise AI platform that connects agents to the data, applications, APIs, tools, and workflows that already run the business.
Built on zero-trust, API-first principles, kaman.ai helps enterprises move beyond AI that simply answers questions to AI that can understand context, orchestrate work, and take governed action.
Connect intelligence to the enterprise.
Turn reasoning into action.
Turn reasoning into action.
The kaman.ai Approach
The Agentic Intelligence Layer
01
Connect
Bring together enterprise applications, data, documents, and APIs.
02
Understand
Build enterprise context so agents can reason over the right information.
03
Orchestrate
Coordinate agents, skills, tools, and intelligent workflows.
04
Act
Execute governed workflows and turn intelligence into business action.
Enterprise Context •
Agents •
Skills •
Governance •
Action
Not Sure Where to Begin?
Assess Your Enterprise AI Readiness
Understand your readiness across data, technology, people, governance, risk, and ROI, then identify priority use cases and a practical path to scale.
Includes:
8-dimension readiness evaluation
•
Priority use cases by ROI
•
Risk & governance assessment
•
30-60-90 day roadmap
Talk to Our GenAI Team
Bring us a high-value problem that traditional approaches have struggled to solve. We work with selected anchor customers to evaluate the opportunity, co-invest through our AI Pods, and engineer a production-ready GenAI solution around real enterprise data, systems, and workflows.
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