Overview
We ran a high-throughput response evaluation pipeline for a frontier AI lab, grading Hindi-language model outputs for factual accuracy, groundedness, and safety.
Quality
In the engagement's first month, our cohort held 96% accuracy against the client's audit benchmark while bringing AHT on complex evaluation tasks down to 5 minutes.
One of our annotators was integrated directly into the client’s internal process as a dedicated QC reviewer, performing final-pass checks before handoff to the model training team. This contributor cleared roughly ten levels of quality evaluation to earn the role and has held it since, functioning as the project's quality backbone - a degree of integration beyond a standard vendor relationship.
Proof Through Scale: 5X Scale
Impressed with output quality, the client asked us to scale the annotator cohort 5x within a month to absorb a sharp increase in volume. We hit the ramp without a drop in quality or onboarding lag, proving that the pipeline holds its accuracy bar under rapid scale.
Expansion to Other Languages
On the strength of the Hindi-language workstream, the client extended the engagement to English and Spanish, with the same evaluation framework being applied across the additional languages - converting a single-language engagement into a multilingual evaluation partnership.
Looking to scale annotation in different languages? Let's talk about your next batch!
.png)