40%
Faster movement from use-case assessment to production planning
A global bank uses ADAM to identify priority opportunities, register reusable AI assets, and establish governance before deployment.
Bring industry expertise, modern engineering, and AI accelerators together to build, orchestrate, and govern AI agents at enterprise scale.
Robust structure and a common operating model. ADAM (Agentic Data and Application Management) brings everything under one governed platform to help you assess your AI estate, identify high-value opportunities, nurture trusted data and knowledge, build and certify agents, and govern them from development through runtime.
A domain-aware diagnostic and decision engine that assesses readiness, prioritizes use cases, and delivers value-backed roadmaps, governance guidance, scorecards, financial forecasts, and execution-ready artifacts.
A single environment to design, configure, and deploy agents from no-code to pro-code, all producing the same governed, promotable assets, with an agent passport generated for every build.
The enterprise system of record for AI assets, agents, MCPs, prompts, and skills, enabling governed distribution, standardized reuse, and scalable adoption across teams.
A portfolio of configurable AI building blocks and domain solutions that embed intelligence across enterprise functions and industries, delivering outcomes with rapid customization through Agent Canvas.
A unified control layer governing data, models, prompts, code, agents, security, and costs, enabling auditable orchestration, Responsible AI compliance, continuous value optimization, and enterprise-wide governance.
The governed runtime handshake for every model, agent, and MCP call, enforcing authentication, routing, PII controls, quotas, and cost at the point of execution across any cloud.
The context backbone that grounds agents in enterprise ontologies, knowledge graphs, and business meaning, so responses stay relevant, explainable, and aligned to the language of the business.
A unified environment for data and knowledge engineering. Turn scattered enterprise data into governed, AI-ready assets through RAG, GraphRAG, and ontology-driven pipelines. Trusted and traceable by design.
Step into the ADAM sandbox and see how the platform brings strategy, build, context, runtime controls, and governance together.
Governance is enforced in sequence: while agents are built, before they publish, and while they run, never bolted on afterward.
Extend the agents, tools, models, and platforms you already run, adding capabilities without discarding what already works.
Ground agents in your ontologies, knowledge graphs, and business meaning, so they understand your business, not just the data.
Most enterprises start from sprawl, not a blank slate. The discovery scanner maps existing AI and charts a governed path.
Solutions built using modular, plug-and-play components that snap together.
Integration with existing tools, platforms, and future technologies without architectural overhaul.
Workflows, rules, and agents can be tailored to specific client environments without custom code.
Expansion from single use cases to enterprise-wide adoption across business units and geographies.
Insights, decisions, and automation powered by robust data foundations and feedback loops.
Improves with frequent updates, new agents, and advancing capabilities as the platform matures.
40%
A global bank uses ADAM to identify priority opportunities, register reusable AI assets, and establish governance before deployment.
ADAM brings distributed AI initiatives into a common operating model across teams.
Teams use configurable building blocks to reduce duplication and accelerate adoption.
Strategy, trusted context, runtime controls, and oversight come together in one operating model.
Semantic Fabric and governed data pipelines help agents work with business meaning.
ADAM is designed to extend the agents, tools, models, and platforms you already run, adding capabilities without discarding what already works.
A unified reference architecture tailored to AWS, Azure, and GCP accelerates setup, optimizes cloud-native services, and delivers outcomes faster.
Governance is enforced in sequence: while agents are built, before they publish, and while they run, never bolted on afterward.
Ground agents in your ontologies, knowledge graphs, and business meaning, so they understand your business, not just the data.
Expansion from single use cases to enterprise-wide adoption across business units and geographies.
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