A01
Multi-agent orchestration
Parallel agents handle extraction, NER, sentiment, classification, translation and summarization
Explore architectureAI-NATIVE DATA INFRASTRUCTURE
DATA
BUILT
INTELLIGENCE
READY
Ateegnas coordinates specialized AI agents to transform raw text and real-world video into structured, high-quality training data
72M+tokens processed300K+records annotated100+ML engineers
NER
SENTIMENT
CLASSIFY
ENTITY
QA
SWARM CORE
CENTRAL ORCHESTRATOR
05 SPECIALIZED AGENTS•01 CONSENSUS ARBITRATOR
01 / PLATFORM
One systemMultiple specialists
Instead of forcing one model to do everything, Ateegnas routes each task to specialized agents, aggregates their outputs, and evaluates the result before delivery
A02
Structured data output
Bring your ontology and receive machine-ready annotations instead of disconnected model responses
A03
Quality assurance
Consensus and scoring layers help surface uncertain annotations before they reach your dataset
02 / WORKFLOW
From raw inputto model-ready data
01
UPLOAD
CSV, JSON, TXT, PDF or video
02
UNDERSTAND
Ontology and task configuration
03
ORCHESTRATE
Specialized agents run in parallel
04
VERIFY
Consensus, confidence and QA
05
EXPORT
Structured training-ready output
03 / DATA SYSTEM
Text todayPhysical AI next
Two data categories, one infrastructure layer: high-volume text annotation and egocentric video collection for embodied AI
01
TEXT / NLPTeach models what language means
NER, entity extraction, sentiment, classification, translation and summarization through coordinated AI agents
02
EGOCENTRIC VISIONCapture how the physical world works
Household and everyday POV video collection designed for robotics, embodied intelligence and physical AI datasets
THE ATEEGNAS THESIS
Better dataBetter modelsFaster iteration
Ateegnas is building the infrastructure between raw-world information and the AI systems that learn from it
MEITY TIDE 2 0