2025 / AI workflow system
Candidate Screening Platform
A screening and partner-assessment workflow that combined AI assistance with grounded evaluation and reviewable outputs.
Overview
This project focused on making AI-assisted evaluation useful in a real review process. The challenge was not only speed, but trust: the system needed to produce outputs that reviewers could inspect, challenge, and rely on.
Problem
What needed to change.
Teams needed faster screening and partner-assessment pipelines without losing traceability, trust, or human review. A fast answer was not enough unless the reasoning stayed inspectable.
Constraints
The edges that shaped the solution.
- Model output had to be reviewable rather than opaque.
- The workflow needed to fit real internal adoption, not just demo well.
- AI assistance had to stay grounded in domain inputs and reviewer context.
What I owned
The parts I was directly responsible for.
- Architecture for the overall evaluation workflow
- AI-system design around inspectable outputs and human review
- Full-stack delivery across UI, backend, and cloud integrations
Key decisions
Choices that defined the project shape.
- Structured the workflow so reviewers could inspect conclusions instead of receiving black-box scores.
- Connected model behavior to domain inputs to reduce generic output drift.
- Designed the interface around decision support, not automation theater.
Outcome
What changed after the work shipped.
The final system compressed evaluation time while staying grounded enough for real internal use, making the AI layer more trustworthy and operationally believable.
- Reduced evaluation friction without removing human review
- Reviewers could inspect and challenge outputs
- AI behavior was shaped around process fit, not novelty