Applied AI/ML & Evaluation
We help teams make AI, ML, RAG, and automation workflows measurable before they scale. The work turns model behavior, source quality, review ownership, and known limits into evidence that product, operations, and technical teams can discuss together.
Where this helps
- AI is moving from demo to workflow and needs clear evaluation criteria.
- RAG or document workflows need source checks before adoption.
- Teams are comparing model, retrieval, prompt, or workflow options.
- Stakeholders need evidence before scaling an AI initiative.
Who it serves
- Product and operations teams evaluating AI-assisted workflows.
- Knowledge-heavy teams working with policies, contracts, support text, research notes, or documentation.
- Leaders deciding whether to continue, redesign, buy, or stop an AI initiative.
Intervention
- Define test cases, acceptance criteria, and review routines.
- Compare model, retrieval, data, prompt, and workflow options.
- Measure retrieval, citation, abstention, consistency, and known limits.
- Build small reviewable AI, RAG, or document-intelligence workflows.
Observable deliverables
- Evaluation brief with workflow risks, test cases, and review boundaries.
- Behavior and source-quality report covering model, retrieval, and citation gaps.
- Prototype AI, RAG, or document-intelligence workflow with known limits.
- Iteration backlog and handoff notes for product, operations, or technical teams.
Engagement model (high level)
- Phase 1: Evaluation design - define the workflow, audience, decision context, evidence needs, and review boundaries.
- Phase 2: Prototype / Measure - build or inspect a small AI workflow and measure behavior against real cases instead of demo prompts only.
- Phase 3: Handoff / Iteration - document findings, decision points, review responsibilities, and the next implementation path.
Useful for
- Evidence-backed AI adoption decisions.
- RAG and document workflows with source review.
- Model behavior review before buying, scaling, or productizing.
- Technical evidence that business stakeholders can read and discuss.
Best results when
- The workflow, users, and review expectations are clear.
- Source material, examples, or sample cases are available.
- Human review and safety boundaries are part of the plan.