iPro • Case Study
Continuous Active Learning
Built a machine-assisted review workflow that improved speed, quality, and defensibility by prioritizing what mattered first.
Why this case matters
Better review comes from ranking what matters first.
Traditional document review treats too much work as equally important. That creates waste, delays, and inconsistent quality.
I built a system that learned from reviewer feedback, reprioritized the corpus continuously, and surfaced the most relevant content sooner. That changed the workflow from brute-force review to machine-assisted prioritization with human judgment still in the loop.
Case Study
Context
Large-scale review was slow, expensive, and uneven.
Legal and compliance review often depends on linear processes that consume too much time and money while making it hard to focus on the highest-value material early enough.
The opportunity was to replace that static model with a continuously learning workflow that improved prioritization, increased confidence, and reduced wasted effort.
What I did
Built a learning system around reviewer judgment.
I built a Continuous Active Learning workflow that used reviewer feedback to improve ranking in real time, helping teams surface relevant documents earlier in the process.
I also helped shape the product experience around trust, defensibility, workflow adoption, and practical usefulness so the system improved real review outcomes instead of just producing interesting model outputs.
Outcome(s)
Faster review with stronger prioritization.
Improved review speed, improved the relevance of what teams saw first, reduced waste in the workflow, increased confidence in ranking quality, and created a stronger human-in-the-loop AI product for an enterprise environment where defensibility matters.
Why it matters
AI products win when they improve the workflow, not just the model.
This shows the ability to connect model behavior, human judgment, workflow design, and enterprise trust requirements into one product system that actually changes how work gets done.