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Capabilities

A few of the things you can do in just a few lines of code

Helmets and gloves boxed on a construction site

Focus (Zoom + Crop)

python

Try it out 
@perceive(model="isaac-0.2-2b-preview", max_tokens=4096, expects="box", tools=True)
def detect_ppe(frame_path: str):
return (
image(frame_path)
+ text(
"Detect and localize the following PPE: helmets, gloves, closed-toe shoes, and protective eyewear. "
"For each item, identify all instances with bounding boxes. "
"If any required PPE is missing for any person, alert me and specify exactly which items are missing."
)
)
detect_ppe("construction_site.png")
A single car picked out of an F1 pack

Conversational Pointing

python

Try it out 
text_prompt = "Identify single focal point of the scene. Be concise."

@perceive(model="isaac-0.2-2b-preview", expects="box", allow_multiple=True)
def focal_point(frame_path: str):
    scene = image(frame_path)
    return scene + text(text_prompt)

focal_point("f1.jpg")
Produce sorted against a category example

In-Context Learning

python

Try it out 
@perceive(model="isaac-0.2-2b-preview", expects="box", allow_multiple=True)

def evaluate_produce():
    apple_example = "apple.jpg"
    target_scene = "fruits.webp"
    return (
        apple_example
        + text("This is Category 1. Action to take: cut into slices.")
        + target_scene
        + text(
            "For every instance of Category 1, output a bounding box labeled with the action to take."
        )
    )

evaluate_produce()
Heavy equipment detected on a work site

Object Detection

python

Try it out 
detect(
    image_obj="construction-site.jpg",
    classes=["heavy equipment"]
)
Each puppy in a litter pointed to and counted

Counting

python

Try it out 
text_prompt = "Help me count the puppies. Point to each."

@perceive(model="isaac-0.2-2b-preview", expects="point", allow_multiple=True)
def count(frame_path: str):
    scene = image(frame_path)
    return scene + text("Help me count the puppies. Point to each.")

count(puppies.jpg)
Signage read from a city street scene

Optical Character Recognition

python

Try it out 
ocr(
    image_obj="radio-city.jpg",
    prompt="Read any signs."
)
A concise caption generated for a photo

Captioning

python

Try it out 
caption(
    image_obj="up.jpg",
    expects="text",
    style="concise"
)

Cost Calculator

$1.2900/1M in · $2.5900/1M out

Liss Loop 1.0

Total per request

$0.0205

15.744 tokens / request

2 fps dynamic sample rate · 120 frames
Prompt14 tok~$0.0000
Video in15,600 tok$0.0201
Output130 tok$0.0003

120 frames, each at 640×416.

Video

Prompt

Video is sampled at up to 2 fps into a fixed patch budget, so longer clips add frames only until that budget fills — then tokens plateau.

cost = (tokens / 1M) × rate — token counts mirror the Liss Loop 1.0 tokenizer: images scale with resolution up to a per-image patch budget, and video is sampled at up to 2 fps into a per-request budget, so video tokens plateau as frame rate and duration climb. Planning estimates only; exact counts come from the SDK.

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