Human intelligence, engineered for AI
Data that makesmodels dependable.
Nuvo partners with AI teams to design, build, and evaluate high-quality datasets across language, vision, audio, and agentic systems.
Inside a Nuvo program
A visible system for turning model gaps into reliable data.
Task design, expert production, layered review, and model feedback stay connected—so teams can see what is improving and why.
Explore the operating modelReasoning
reviewedVision
calibratedSpeech
reviewedDomain
verifiedWhat we build
The right data at every stage of model development.
From an early capability study to a sustained production program, Nuvo brings task design, expert judgment, quality operations, and evaluation into one coherent system.
Generative AI & Post-Training
Expert-authored and carefully reviewed data programs for supervised fine-tuning, preference optimization, reinforcement learning, and agent improvement.
ExploreModel Evaluation
Custom benchmarks, human evaluations, and adversarial testing that reveal capability gaps before they become production issues.
ExploreComputer Vision
Annotation, validation, and edge-case discovery for image, video, geospatial, and sensor-fusion datasets.
ExploreSpeech & Multimodal
Collection and annotation programs spanning speech, audio, text, image, and video—designed as connected signals rather than isolated files.
ExploreManaged Data Operations
Program design, contributor operations, quality management, and delivery infrastructure coordinated around your model roadmap.
ExploreYour program
A solution shaped around your model—not a generic queue.
View all solutionsHow Nuvo works
Precision is not a final inspection. It is the operating model.
Every program is built around clear acceptance criteria, calibrated people, layered review, and an improvement loop tied to the behavior of your model.
Requirements before throughput
We turn model goals into observable criteria, unambiguous instructions, and measurable acceptance thresholds.
Quality as a system
Calibration, gold tasks, review layers, adjudication, and root-cause analysis work together—not as isolated checks.
Controls matched to risk
Data access, workforce design, and delivery practices are configured around the sensitivity of each engagement.
Learn from every cycle
Delivery signals and model failures continuously improve task design, contributor guidance, and sampling strategy.
Built for real-world complexity
Domain context changes what “good data” means.
We design task systems around the environments, risks, and performance criteria that matter in each field.
Build what your next model needs