Work we can show you
The brief, what we did and what it produced, in the client's numbers.
If Your Model Must Judge Like a Chef, a Chef Must Teach It.
How ConsultBae used credentialed chefs and dietitians to label 310,000 food images and 92,000 recipes, closing the gap between data rich and label poor for a culinary AI product.
Your Data Scientists Are Your Most Expensive Annotators.
How ConsultBae took an AI company's annotation off its research team, scaling to 240,000 images and 65,000 text rows a month across six languages at 48 hour turnaround.
Why Compliant Health Data Cannot Teach a Model Names.
How ConsultBae built a named entity phonetic lexicon of 72,800 entries across four languages, closing the proper noun gap that anonymized clinical data leaves in speech recognition systems.
60,000 Video Reviews a Month Across Four Age Bands.
How ConsultBae runs age banded content review and search relevance rating for a children's video platform, with calibrated guidelines, tiered escalation and reviewer wellbeing built into the operating model.
9,400 AI Image Evaluations Across 22 Locales.
How ConsultBae localized 2,600 prompts and ran culturally grounded evaluation of a text to image model across 18 languages and 22 locales, using an English control baseline.
40,000 Expert LLM Evaluations Per Six-Day Sprint.
How ConsultBae ran rapid sprint LLM evaluation and A/B testing across eight complex domains using subject matter experts, delivering 420,000+ evaluations at a six day cadence.
A 600-Rater Content Relevance Pool, Live in 12 Days.
How ConsultBae sourced, vetted and trained 600 content relevance raters across 12 markets and 9 languages in 12 days, holding 95 percent inter-rater agreement on a subjective task.
500 Hours of Egocentric Data in Ten Days.
How ConsultBae collected 500+ hours of unscripted egocentric first person recordings across home and office environments in a ten day window at 98% approved accuracy.
How We Built the Dataset Behind an AI Smart Toilet
How ConsultBae built a stool and urine annotation dataset for an AI smart-toilet health monitor, using controlled biological studies, polygon segmentation, and multi-pass medical QA.