Human data infrastructure for AI
Human data.
Built for AI.
Dense AI helps AI and machine learning teams collect, annotate, evaluate and manage the human data required to build better AI systems.
Human
Data layer
Quality
Control
Scale
Workflows
The operational layer behind better AI.
Modern AI systems depend on high-quality human data. Dense AI manages the people, processes and quality systems required to collect, annotate and evaluate that data.
Client requirements
Understand the data, task and quality requirements for the project.
Workflow design
Design instructions, tooling and review stages around the task.
Contributor network
Qualify and onboard contributors suited to the project.
Quality control
Review, correct and validate output against project standards.
Validated dataset
Deliver a dataset that has passed the agreed quality checks.
Services
AI data services built around your workflow.
From raw data collection to final evaluation, Dense AI manages the human workflows behind AI development.
Data Collection
Structured real-world data across people, languages, environments and use cases.
Learn moreData Annotation
Human labeling for training datasets, classification, segmentation and structured outputs.
Learn moreImage & Video
Computer-vision annotation across images, frames, objects, keypoints and sequences.
Learn moreAudio & Speech
Speech collection, transcription, classification and audio labeling workflows.
Learn moreMultilingual Data
Language data collection and processing across supported languages and regions.
Learn moreLLM Evaluation
Human evaluation of AI responses, instruction following, relevance and quality.
Learn moreHuman Feedback
Ranking, preference and structured feedback workflows for improving AI systems.
Learn moreQuality & Validation
Human and automated checks to identify errors and maintain project standards.
Learn moreWorkforce
A scalable human workforce.
Large AI projects often require hundreds or thousands of human contributors. Dense AI manages contributor recruitment, qualification, training, task allocation, monitoring and quality control.
Verified
Contributors are screened and qualified for appropriate projects.
Trained
Project-specific instructions and examples are provided before work begins.
Managed
Project managers monitor progress, quality and delivery.
Dense AI
Project management
Quality
Quality is built into every workflow.
The value of AI data depends on its accuracy. Dense AI builds quality control into each stage of the workflow.
Clear instructions
Qualification
Task execution
Human review
Final check
Multiple Review Layers
Work can pass through more than one review stage before delivery.
Project-Specific QA
Quality checks are shaped around the requirements of each project.
Responsible Operations
Workflows are run with clear accountability at every stage.
Security designed around the project.
Project-specific access, handling, monitoring and delivery requirements can be incorporated into the workflow.
Controlled Access
Access to project data can be limited to approved contributors and staff.
Role-Based Workflows
Tasks and permissions can be split by role across the workflow.
Quality Audits
Output can be periodically audited against project standards.
Secure Delivery
Datasets can be delivered through agreed, project-specific channels.
Project-Specific Handling
Handling requirements can be incorporated into the workflow design.
Access Monitoring
Activity within a project's workflow can be monitored and logged.
From pilot to production.
Define
Understand the task, data and quality requirements.
Pilot
Run a controlled initial workflow.
Validate
Review quality, instructions and operational requirements.
Scale
Expand the workforce and workflow once requirements are validated.
Why Dense AI?
Human-Centered
Human judgment for tasks where quality and context matter.
Operationally Managed
Structured workflows, project management and quality control.
Flexible
Workflows can be designed around project-specific requirements.
Quality-Focused
Validation and review are incorporated throughout the process.
Our founders
Leadership across technology, operations and business.
Frequently asked questions
Data collection, annotation, image and video workflows, audio and speech, multilingual data, LLM evaluation, human feedback and quality validation.
Requirements can be defined around skills, languages, experience and project-specific qualification or training.
Quality can be built into instructions, qualification, task execution, validation, human review, secondary QA, correction and final checks.
Yes. A pilot-to-scale workflow can validate instructions, workforce requirements and quality controls before larger production.
Examples include text, images, video, audio, speech and multilingual data, depending on project requirements.
Contact Dense AI with your data type, approximate volume, languages, timeline and quality requirements.
Contact
Tell us what your AI team needs.
Share your data type, volume, languages, timeline and quality requirements. We can shape the workflow around the project.
densee.ai@gmail.comJoin the Dense AI contributor network.
Contributors may participate in appropriate data collection, annotation, evaluation, language and research or feedback tasks.
Become a Contributor