Knowledge work
Research, summarize, classify and prepare drafts with source context and review before action.
Applied AI / Automation
AI that works
inside the workflow.
Design AI agents and supervised automation around clear decisions, reliable context, human oversight and measurable production behavior.
Automation before theatre.
AI is useful when it changes a real operating outcome and its uncertainty can be managed. We map the workflow, identify deterministic steps, isolate the judgment that benefits from AI, and design the controls around it.
Multi-step workflows
Coordinate systems, rules and approvals while keeping consequential actions explicit and observable.
Conversational operations
Support customers or teams through grounded assistants connected to approved knowledge and tools.
AI agent development with production boundaries.
The model is one component. The reliable product includes context, permissions, evaluation, fallbacks and operating ownership.
Workflow and agent design
Tasks, tools, decision boundaries, state, escalation paths and human checkpoints.
Knowledge and integrations
Retrieval from approved sources, CRM or ERP connections, APIs and traceable information flow.
Evaluation and safeguards
Representative test sets, quality thresholds, content controls, failure modes and cost limits.
Operations and observability
Logs, feedback, model configuration, release controls and clear ownership for ongoing improvement.
A controlled path to production.
We reduce risk with evidence at each stage instead of treating a promising prototype as a finished system.
Choose the right work
Assess volume, variability, data, risk and whether rules-based automation is the better answer.
Test the uncertain part
Use representative cases to validate quality, latency, cost and operator trust.
Connect with control
Apply least privilege, approval boundaries, fallbacks and resilient system interactions.
Measure real behavior
Monitor quality and drift, review exceptions and improve against a versioned evaluation set.
Responsible AI is an operating capability.
Controls must match the consequence of being wrong.
- Approved data sources and clear handling of sensitive information
- Human review for consequential, ambiguous or irreversible actions
- Grounded outputs with citations or source context where appropriate
- Evaluation across normal, edge and adversarial cases
- Fallback behavior, cost ceilings and an accountable system owner
Have a workflow in mind?