Build what moves your business forward.

A bounded review of the use case, tool boundaries, adversarial behavior, and recovery paths—delivered with repeatable tests and prioritized findings.

Best for: Teams with an LLM or agent feature that works in demos but lacks repeatable evidence about risk and reliability.

• Threat and tool-boundary map
• Normal, ambiguous, adversarial, and unsafe test scenarios
• Repeatable evaluation harness or test suite
• Prioritized guardrail, logging, and human-review recommendations

Connect a supported AI model to your approved APIs, tools, and business information through an MCP or API layer, with permissions, error handling, tests, and a documented handoff.

Best for: Teams connecting an agent to an API, database, internal tool, or operational workflow.

• Model fit, tool schemas, and permission design
• MCP server/client or API integration
• Failure-path and tool-selection tests
• Setup documentation and handoff walkthrough

Turn one concrete workflow into a working slice with tool use, retrieval or routing, observability, human review, and defined acceptance scenarios.

Best for: Teams that need a production-shaped implementation before funding a broader AI initiative.

• Workflow map and acceptance scenarios
• One bounded end-to-end implementation
• Logging, evaluation, review, and recovery behavior
• Client-controlled deployment or documented handoff

Map the real work, configure the bounded workflow, train with actual scenarios, and define escalation and outcome measures.

Best for: Operators who selected an AI tool but still need the workflow, controls, training, and reinforcement required for actual use.

• Current-state and future-state workflow map
• Role-specific operating guidance and training
• Human-review and escalation rules
• Go-live support and adoption measures

A project or monthly SOW with priorities, clear boundaries, a review cadence, and concise delivery reporting.

Best for: Product teams and consultancies that need recurring, client-facing applied-AI support for a defined backlog.

• Prioritized delivery backlog
• Hands-on Python and TypeScript implementation
• Weekly written status, risks, and decisions
• Documentation and maintainable client handoff