Case Studies

A few examples of what production AI work looks like. Details are anonymized to protect client confidentiality, but the outcomes are real.

LLM Safety

LLM Safety & Evaluation Framework

Context

Organization deploying LLMs in sensitive workflows where errors could have real consequences. Needed a way to trust the outputs.

Challenge

  • Hallucinations and unsafe outputs slipping through
  • No systematic way to measure quality or risk

Solution

  • Designed structured evaluation framework with sensitivity/specificity metrics
  • Built automated testing for edge cases using red-team datasets
  • Deployed as FastAPI microservice with LangSmith tracing

Outcome

96%
Sensitivity in detecting unsafe outputs
~90%
Specificity (low false positives)
$0.0015
Cost per 1K characters
Training

AI Training & Adoption for Cross-Functional Teams

Context

Mixed technical and non-technical teams starting to adopt AI tools. Everyone had different expectations and comfort levels.

Challenge

  • Inconsistent usage across the organization
  • Over-trust in AI outputs without verification
  • No shared standards for when or how to use AI

Solution

  • Delivered hands-on workshops tailored to each role
  • Designed role-specific prompt packs and templates
  • Introduced clear guidelines on verification and safe usage

Outcome

200+
Learners trained
Faster
Adoption with fewer errors
Aligned
Teams know when AI helps — and when it doesn't

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