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GROWTHProject Description
A research-focused client in the AI space, dedicated to improving factual integrity in NLP outputs, approached Oodles to build a lightweight tool for discrepancy detection. The goal was to test the effectiveness of their custom NLP models through local execution before scaling into a full-fledged application. The engagement focused on enabling precise, revision-based model testing.
Scope Of Work
With the help of Oodles, the client aimed to detect nuanced factual mismatches in natural language using a local desktop tool. They needed a flexible, testable environment to identify subject, verb, and time-based inconsistencies. The solution covered key areas like model evaluation, NLP pipeline integration, and lightweight UI for visual outputs to support research-based model iteration.
Our Solution
To support the client’s evaluation of factual discrepancy detection, a custom-built local application was delivered with streamlined NLP pipelines and model-swapping capabilities.
Key Features Implemented:
- Model-Agnostic NLP Integration: Supports easy testing and comparison of multiple NLP models.
- Discrepancy Detection Engine: Highlights variations in subjects, actions, and time-sensitive details.
- Interactive UI: Enables real-time inspection of model outputs, revision tracking, and error categorization.
- Offline Accessibility: Fully functional without internet to support local research and data security.
- Error Logging Module: Captures flagged discrepancies with metadata for iterative improvement.