Research
Searching Through a Virtual World
We trained a 4B model to search a synthetic corporate tenant with 91.1% of frontier performance and greater reliability than its 27B teacher.
Read article · 15 minThe IDE for post-training.
Find where your agent fails, build the eval that matters, and train against it. All in one workspace your team controls.
Trusted by
Pensive
CloudCruise
NuGuard AIFounded at
South Park Commons Residency
Foundation Capital BuildObserveEvaluateTrainDeploy
A normal development loop for better model behavior.
01
Find failures that matter
Trace ingestion · weakness mining · clusteringTraces · weakness mining · clustering
02
Turn judgment into an eval
Rubrics · datasets · regression suites
03
post-trained 0.87base 0.52+0.35 vs base+0.35
Train and compare
RL · ON-POLICY DISTILLATION · PREFERENCES
04
Live routing
Keep learning in production
Model routing · observability · rollouts
The blueprints for our product.
“Whitney AI was exhaustive in understanding the problem and how to solve it. They surfaced a number of insights about our click-prediction step that we can apply to the product.”
Felix EckertCo-Founder & CTO, CloudCruise“Whitney AI built us a loop that mines failure patterns from human-reviewer disagreements, proposed concrete rubric changes, and gates each one before it goes live.”
Yoonseok YangCo-Founder & CEO, Pensive“Handling calls with patients carries real liability. Whitney AI built an evaluation and guardrail that pinpoints the exact moment an agent crosses from answering a question into giving medical advice, and trained a model small enough to flag it before the sentence finishes.”
Ryan TsangCo-Founder & CEO, Harmonize Health“As AI advances, so do the threats: social engineering, deepfakes, voice clones. Fighting back means staying at the frontier. Whitney AI trained a model for us that accelerates our defensive anti-fraud research.”
Neo TiangratanakulCo-Founder & COO, Scam AI“Whitney AI took a 27B open-source model and trained it into a red-teamer that outperforms attackers many times its size.”
Ranjan GoelChief Executive Officer, NuGuard AI
Get your first post-training loop running.
Whitney field engineers can work alongside your team to connect a workflow, define the eval, and establish the process.
Your team keeps the workspace, models, evals, and the ability to run the loop independently.
Talk about your first workflowNotes from building the model improvement layer.
Research
The Art of Evals
No public benchmark for medical billing errors existed, so we built one — synthetic data, start to finish.
Read article · 12 minFull ML model lifecycle in one team.
Jake Kang
Co-Founder · Evaluations
YCMicrosoft
LinkedIn Built evaluation systems and self-recovering agents for consequential production workflows.
- 99%Browser-agent accuracy
- 1,000sRCM hours automated
- $2MAnnual infrastructure savings
Nick Mecklenburg
Co-Founder · Post-Training
Microsoft SuperintelligenceStanford
LinkedIn Post-trained custom models and built the benchmarks that proved they work.
- 75xCheaper model, no quality loss
- +10%Better agentic search
- 16%Better error detection
Vatsal Bajaj
Co-Founder · Inferencing
Netflix ML PlatformMicrosoft AI
LinkedIn Served models in production at planet scale: fast, reliable, and observable.
- <100msInference
- 99.9%Serving availability
- 40%Faster ranking layer