Applied AI/ML & Evaluation
We help teams turn AI, ML, RAG, and automation work into measured, reviewable workflows. The engagement makes model behavior, evidence, boundaries, and review ownership visible so adoption decisions are grounded in practical evidence. We also research model, retrieval, data, and workflow options so the evaluation compares realistic alternatives.
Where this helps
- AI is moving from demo to workflow and needs clear evaluation criteria.
- RAG, document intelligence, or automation needs source and review checks.
- Teams want to compare model, data, retrieval, prompt, and workflow options.
- Stakeholders need a concise evidence base for adoption decisions.
Who it serves
- Product and operations teams evaluating AI-assisted workflows.
- Knowledge-heavy teams working with policies, contracts, support text, research notes, or internal documentation.
- Leaders deciding whether to continue, redesign, buy, or stop an AI initiative.
Intervention
- Translate the workflow into test cases, acceptance criteria, and measurable review routines.
- Research model, retrieval, prompt, data, and evaluation options for the target workflow.
- Measure retrieval quality, citation behavior, abstention, classification quality, consistency, and known limits.
- Build small AI/ML or document-intelligence workflows with source labels and reviewable outputs.
Observable deliverables
- Evaluation brief with workflow risks, test cases, success criteria, and review boundaries.
- Evaluation report covering data gaps, model behavior, retrieval quality, citations, and review needs.
- Prototype AI/ML, 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
- AI outputs that need evidence, sources, metrics, and review checkpoints.
- Behavior evaluation before buying, scaling, or productizing a workflow.
- Documents, complaints, contracts, support text, research notes, or operational records.
- Technical evidence that business stakeholders can review with confidence.
Best results when
- The workflow, user group, and review expectations can be named clearly.
- Source material, examples, or sample cases are available for evaluation.
- Human review, safety boundaries, and iteration are part of the plan.