Real-world AI starts with better data
One operating system
for real-world AI.
Collect it. Understand it. Trust it. Learn from it.
Improve what comes next.
Collect, structure, annotate, validate, evaluate, and understand multimodal real-world data in one connected platform—powered by human expertise, AI agents, and continuous feedback.
One platform. A continuous data loop.
How Sentious Works
From the real world
to better models — and back again.
Every stage of the data lifecycle remains connected, so what you learn during annotation, review, evaluation, and deployment can inform what you collect next.
Every decision becomes context for the next one.
01 / Data Collection
Collect the real world.
Build what comes next.
Design, capture, synchronize, and manage multimodal data programs—from one research setup to production-scale collection.
Run collections with your own team, tap into specialized operators and domain experts, or let Sentious operate the program end to end.
Real-world signals structured experience
02 / Annotation Studios
The right workspace for every kind of data.
Purpose-built environments for robotics, audio and vision, preference data, evaluations, and more—with shared intelligence underneath.
Purpose-built interfaces.
Shared intelligence underneath.
Built into every workflow
The platform doesn’t just store the work.
It learns from it.
03 / Sentious Agentic
Every decision makes the next one smarter.
Specialized agents observe the work, surface recommendations, automate permitted actions, and learn from human decisions and real outcomes.
Grasp likely begins 0.42s later.
92% confidence
Faster work. Trusted outcomes.
04 / Quality & Review
Turn uncertain data
into trusted ground truth.
Route ambiguous work intelligently, give reviewers complete multimodal context, resolve disagreement, and preserve every decision from first suggestion to verified result.
Review only what needs judgment—not everything equally.
Ego RGBBoundary disagreement + medium uncertainty.
Under project policy
Trusted data is only the beginning.
Measure what it enables.
05 / Evaluation & Insights
Understand what works. Find what doesn’t.
Know what to do next.
Measure model behavior against trusted data, investigate failure patterns, understand the data behind those failures, and translate insights into targeted improvements.
Measure model behavior.
Run task-specific benchmarks and evaluations against trusted datasets.
Turn data into decisions.
Automatically surface patterns, gaps, and recommendations across your data program.
Grasp failure is significantly higher on reflective cylindrical objects.
Failure rate 21.4%Dataset representation 2.4%Confidence 92%Collect 1,200 additional reflective-object manipulation episodes.
Measure Understand Act
06 / Closed-Loop Improvement
Better models tell you what data should come next.
Connect model behavior back to the data that shaped it. Sentious identifies where performance is weak, what data is underrepresented, and which targeted collection or annotation intervention is most likely to help.
Recommendations identify likely high-value interventions; model outcomes are measured after implementation.
07 / Flexible by Design
Use the platform. Use our experts. Or use both.
Operate Sentious with your own teams, partner with us for end-to-end execution, or combine both approaches as your program changes.
Same platform Hybrid supported
Move work between your team and Sentious without changing tools or data models.
08 / One Foundation
Every workflow runs on the same data and intelligence layer.
Build what comes next
Turn real-world experience
into better intelligence.
From first collection to model feedback, Sentious connects the entire data lifecycle so teams can move faster, improve continuously, and build more capable real-world AI systems.