Train models on what happens between perception and outcome.
Context360 turns hard-to-access expert work into rights-cleared, model-ready trajectories—observation, intent, decisions, actions, state changes, and verified outcomes.
Model data · Post-training · Evaluation · Embodied AI
A DIFFERENT DATA PRIMITIVE
Not another expert marketplace. Not another annotation pipeline.
The market can source people to answer prompts. Context360 is built for a harder problem: instrumenting how skilled people operate across physical and digital environments, then preserving the context that makes every action learnable.
THE CONTEXT360 TRAJECTORY
The data between seeing and succeeding.
Each program captures the full episode as synchronized, provenance-linked layers—not disconnected files or labels.
Context
The operating environment, task state, constraints, and objective.
Observation
What the expert can see, hear, inspect, or retrieve at each moment.
Intent
The goal behind the next action and the success condition it serves.
Decision
The alternatives, evidence, rationale, and tradeoffs behind the choice.
Action
The physical movement, software event, tool call, or expert intervention.
Outcome
The resulting state, verification signal, correction, or task resolution.
DESIGNED FOR MODEL TEAMS
For systems that must perceive, decide, and act.
Purpose-built programs for the teams pushing beyond static knowledge into real-world capability.
MODEL DATA / POST-TRAINING
Frontier multimodal models
Expert demonstrations, long-horizon reasoning traces, tool use, and private evaluation data grounded in real work.
Explore programROBOTICS DATA / EMBODIED AI
Physical AI systems
Egocentric physical tasks with hand-object interaction, tools, state transitions, failures, and recovery behavior.
Explore programAGENT EVALS / APPLIED AI
Agents that do real work
Cross-application workflows that preserve intent, decisions, exceptions, approvals, actions, and verified outcomes.
Explore programHigh-skill domains: industrial systems, medical procedures, maintenance, inspection, skilled trades, and enterprise operations.
Design a custom programCAPABILITY-LED COLLECTION
Start with the failure mode. Build the missing signal.
Your team defines the capability boundary. Context360 translates it into a collection protocol, accepted trajectory schema, and delivery program.
Explore custom programsCapability gap
Where does the model fail?
Instrument
What signal would teach it?
Capture
Who, where, and how?
Structure
Which schema and ontology?
Validate
What makes an episode useful?
Deliver
Training set, eval, or stream.
PROVENANCE IS PART OF THE PRODUCT
Every trajectory arrives with its history intact.
Source, participant, permissions, approved use, transformations, annotation history, and QA status travel with the underlying data.
- Asset-level lineage
- Permission + rights records
- Versioned transformations
- Annotation provenance
- Technical + semantic QA
- Private access scope
- trajectory_id
- C360-TX-0048
- source_type
- expert_in_situ_capture
- episode_layers
- context / intent / action / outcome
- rights_status
- approved_use_linked
- temporal_alignment
- multimodal / event-level
- qa_status
- accepted
- delivery_scope
- private_program