Use Case
Agent Training
Autonomous agents need structured records of how real tasks are actually performed - not idealized flowcharts. Payana converts recorded workflows into clean, step-by-step annotated sequences for agent training and evaluation.
An agent learning to complete a multi-step task needs to see the task decomposed: what was done, in what order, with which objects and tools, and what was said along the way. Payana produces exactly that trace from ordinary video of people working.
Every second of a recording is annotated - the action being performed, the objects and tools in view, a natural-language description of the scene, and a transcript of any narration - yielding an ordered, machine-readable sequence of steps with timestamps. Voice notes recorded during capture preserve the worker's intent, which becomes grounding for instruction-following agents.
Long recordings are automatically segmented into task cycles, and each cycle becomes one training sample: a complete start-to-finish workflow. Off-task detours are flagged by automatic curation so the agent learns the task, not the coffee break.
The same data doubles as an evaluation suite: held-out task traces give you ground truth to score an agent's step ordering, tool selection, and task completion against how experts actually perform the work.
How it works
- 1
Record
Capture the workflow on wearable or fixed cameras - or send existing footage of the task being performed.
- 2
Annotate
Every second gets an action label, object detections, a scene description, and a transcript.
- 3
Structure
Recordings segment into complete task cycles; steps are ordered with timestamps; intent from voice notes is attached.
- 4
Deliver
Step-by-step sequences ship as JSONL and Parquet with train/valid/test splits, ready for agent training and evals.
What you get
- Ordered step sequences with timestamps
- Per-step actions, objects, scene descriptions and transcripts
- Worker intent from voice notes for instruction grounding
- Off-task segments flagged and curated out
- JSONL / Parquet exports with fixed splits
Workflow data ships as JSONL and Parquet - one record per step or per task cycle, with timestamps, labels, and text fields - dropping directly into agent fine-tuning and evaluation pipelines.
Frequently asked questions
An ordered sequence of steps for each task: timestamps, the action performed, the objects and tools involved, a scene description, and any spoken narration. Each complete task cycle is one training sample. Payana delivers these as JSONL or Parquet records with fixed train/valid/test splits.
Have footage to annotate?
Tell us your task and target format - we handle capture, annotation, review and delivery end to end.
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