Relevant by design
Define the actions, scenarios and variation that matter to your application.
REAL-WORLD AI DATA INFRASTRUCTURE
Real-world data.
Intelligent machines.
Task-specific, consented and quality-validated training data for robotics, computer vision and embodied AI. Built around what your models need to learn.
Discuss your data needs Explore our approachUK-based company / Enterprise & research
HUMAN EXPERIENCE. STRUCTURED FOR AI.
Learning begins
in the real world.Illustrative imagery
in the real world.Illustrative imagery
01 / PURPOSE-BUILT DATA
Better data begins
with the right brief.
Intelligent machines need more than generic datasets. They need examples of the tasks, objects and environments they’ll encounter. We turn your model requirements into a focused collection programme.
Human experience, captured
Consented demonstrations bring real-world context to model development.
Ready for your workflow
Agree on validation criteria, annotations and delivery formats from the start.
02 / HOW DREBAN WORKS
From your brief.
To your training pipeline.
A clear process, with quality defined before collection begins.
Define
Align on tasks, environments, capture requirements and acceptance criteria.
YOUR MODEL. YOUR REQUIREMENTS.Collect
Gather consented, task-specific demonstrations through a distributed contributor network.
REAL ACTIONS. RELEVANT CONTEXT.Validate
Review submissions against the agreed brief for completeness, relevance and quality.
CONSISTENT REVIEW CRITERIA.Deliver
Package accepted data with agreed annotations, metadata and documentation.
PREPARED FOR YOUR WORKFLOW.03 / APPLICATIONS
Data for systems that
perceive, understand and act.
Custom collection shaped around what your system needs to see, understand and do.
Robotics manipulation
Task demonstrations for grasping, sorting, placement and tool interaction. Capture sequences that help models learn how actions unfold.
Computer vision
Image and video collection focused on relevant objects, scenes and conditions. Specify the variation your perception systems need.
Multimodal & first-person video
First-person perspectives paired with task descriptions and agreed contextual metadata, for systems that connect perception with action.
04 / DATA INTEGRITY
Useful data starts
with a clear foundation.
Quality, provenance and responsible collection belong in the project brief from day one.
Discuss your requirements ↗Consent & intended use
Collection is consent-based. Define permitted uses and contributor requirements for the project.
Quality you can specify
Agree on capture standards and acceptance criteria, then validate submissions against them.
Traceable provenance
Scope the collection records, metadata and documentation your team needs for review.
Project-specific safeguards
Address privacy, access, retention and delivery requirements during scoping. Detailed sourcing information is available through appropriate due diligence.
05 / ABOUT DREBAN
UK-based.
Built for a world in motion.
Dreban connects human experience with the development of intelligent machines through real-world, task-specific training data.
We welcome conversations with enterprise teams, research organisations and investors, with an initial commercial focus on Qatar and the wider GCC.
YOUR NEXT DATASET STARTS HERE
What does your model
need to learn?
Tell us about your use case, required data and timeline.
Let’s define a collection programme around your goals.