LLM Orchestration
- Multi-agent workflow design
- Tool-calling guardrails
- Evaluation and regression suites
Capability / 06
Applied AI and Automation
Private AI workflows, retrieval systems, agentic tools, and automation controls for high-stakes operations.
Capability Overview
We build AI systems that can be inspected and governed. Instead of opaque prompts scattered through operations, we design retrieval, evaluation, tool use, review checkpoints, and observability into the workflow from the beginning.
Focus Areas
The work behind the capability.
Retrieval Systems
- Private knowledge retrieval
- Vector database architecture
- Grounded response pipelines
Deterministic Controls
- Constrained generation
- Human review checkpoints
- Output quality monitoring
Data Sovereignty
- Private infrastructure
- Access-control boundaries
- Audit-ready observability
- AI workflow architecture
- Retrieval and automation pipelines
- Evaluation harness and quality metrics
- Governance, review, and observability documentation
- Workflow audit and automation candidate selection
- Data boundary and retrieval design
- Prototype with measurable evaluation criteria
- Production deployment with monitoring
OpenAIAzure OpenAIVector databasesRAGAgentsWorkflow queuesEvaluation suitesObservability
Start Here
Shape this capability around your system.
Share the current product, architecture, or operational bottleneck. We will map the right delivery shape and identify what should be designed before the first implementation sprint.