When to bring us in
An AI or software idea needs to be tested against real sources, real users, and real operating constraints.
A workflow needs agents
Research, content review, archive exploration, creative production, or operations need tool-using AI with human checks.
The answers need sources
Retrieval, citations, timestamps, review states, and approved source material matter more than generic chatbot fluency.
The prototype may become real software
The architecture needs APIs, data pipelines, evaluation, monitoring, deployment, and a path to maintainability.
What gets built
A reviewable workflow that uses AI only where it can be checked against sources, examples, and human judgment.
- Workflow mapUser roles, source boundaries, review states, and success criteria.
- AI interfaceLLM agent, retrieval interface, browser-local model demo, approval tool, or internal application.
- Integration layerCustom API, data pipeline, integration layer, or control surface around the AI system.
- EvaluationExamples, failure-mode review, and a recommendation on what should or should not go to production.
- Deployment handoverDeployment notes, monitoring assumptions, and handover material for the team responsible for it.
Related work
AI and software systems grounded in archives, public interfaces, and real operating constraints.
