The building blocks of dependable AI systems

AI Agents and Workflow Automation

Design and deploy agents that can reason across bounded tasks, use tools, follow business rules, request human approval, and operate within observable workflows.

Knowledge and Context Systems

Create systems that retrieve, structure, and deliver the right organizational context to users, applications, and agents.

• Retrieval systems
• Enterprise search
• Knowledge assistants
• Context pipelines
• Memory systems
• Knowledge governance

Document and Conversation Intelligence

Turn unstructured documents, forms, messages, meetings, and conversations into usable operational information.

• Classification
• Extraction
• Summarization
• Review workflows
• Compliance support
• Action and task generation

AI Product Engineering

Design and build AI-enabled applications, product features, and platforms for internal users or external customers.

• Product architecture
• AI-native workflows
• User experience
• Model integration
• Evaluation
• Production deployment

Private, Local, and Hybrid AI

Deploy AI systems in environments that meet the organization’s privacy, security, latency, cost, and infrastructure requirements.

• Local inference
• Private cloud
• Hybrid architecture
• Edge deployment
• Model routing
• Data-boundary design

AI Evaluation and Governance

Establish the controls required to test, monitor, explain, and improve production AI behavior.

• Evaluation datasets
• Quality metrics
• Regression testing
• Human review
• Auditability
• Risk controls
• Policy enforcement

AI Infrastructure and Operations

Build and operate the infrastructure required for reliable, observable, and economically sustainable AI systems.

• Model gateways
• Provider routing
• Cost controls
• Observability
• Reliability engineering
• Deployment automation
• Performance optimization