AIception Interactive
Browse all Aiception APIs★ Only Publicly Available OpenAPI DocumentAI/MLVisionbasic10 EndpointsREST
For Agents
Run computer vision tasks on an image URL — adult content scoring, artistic image styling, object detection, face detection, and face age estimation — using a POST/GET task pattern.
Use for: Check whether an uploaded image contains nudity, I want to generate an artistic version of a photo using a style reference, Detect the main object in this product image, Find all faces in the group photo at this URL
Not supported: Does not handle video processing, OCR, or model training — use for single-image vision tasks (moderation, style transfer, object/face detection, age estimation) only.
Jentic publishes the only available OpenAPI specification for AIception Interactive, keeping it validated and agent-ready. AIception is a computer vision API that accepts an image URL, runs an asynchronous task (adult content detection, artistic image creation, object detection, face detection, or face age estimation), and returns the result via a task ID lookup. Each capability is implemented as a POST to start the task and a GET by taskId to retrieve the outcome. Authentication uses HTTP Basic with a user account.
Install Jentic One Beta
Connect the AIception Interactive to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the AIception Interactive, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with AIception Interactive API.
Score an image for adult or sexually explicit content
Generate an artistic image by combining a content image URL with a style image URL
Detect what object is in an image and return a label
Find all faces in an image and return their bounding boxes
Estimate the age of a person in an image
Use Cases
Patterns agents use AIception Interactive API for, with concrete tasks.
★ User-Generated Content Moderation
Marketplaces and social apps screen user uploads for adult content before they go live. POST /adult_content with the image URL kicks off the analysis, and GET /adult_content/{taskId} returns the score once processing completes. Integrating a single capability requires only those two calls plus HTTP Basic credentials.
POST /adult_content with image_url, then poll GET /adult_content/{taskId} for the moderation score
Artistic Image Stylisation
Creative apps offer style transfer over user photos by submitting a content image URL and a style image URL to /artistic_image. The result is fetched by task ID once generation finishes. The async pattern matches the latency of style transfer well and lets clients show a progress state without holding the connection open.
POST /artistic_image with image_url and style_url, then GET /artistic_image/{taskId} when ready
Face Analysis for Profile Apps
Identity and consumer apps detect faces in uploaded photos via /face and estimate age via /face_age. Both follow the same task pattern, returning bounding boxes or age values once the worker finishes. Applications can chain detection then age estimation for a richer profile pipeline.
POST /face with image_url, get the face boxes, then POST /face_age for the same image
AI Agent Vision Pipeline via Jentic
An agent built on Jentic can stitch AIception capabilities into a vision workflow — moderate then stylise, or detect then age-estimate — without exposing the basic auth credentials to the agent's working memory. Jentic injects the credentials at execution time and surfaces only the operation schemas to the agent.
Use Jentic to search 'detect the object in an image', execute the call, then chain into face detection if a person is found
Key Endpoints
10 endpoints — jentic publishes the only available openapi specification for aiception interactive, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/adult_content
Submit an image for adult content scoring
GET
/adult_content/{taskId}
Get adult content task result
POST
/artistic_image
Create an artistic image task
GET
/artistic_image/{taskId}
Get artistic image task result
POST
/detect_object
Submit an image for object detection
POST
/face
Submit an image for face detection
GET
/face/{taskId}
Get face detection result
POST
/face_age
Submit an image for face age estimation
POST
/adult_content
Submit an image for adult content scoring
GET
/adult_content/{taskId}
Get adult content task result
POST
/artistic_image
Create an artistic image task
GET
/artistic_image/{taskId}
Get artistic image task result
POST
/detect_object
Submit an image for object detection
POST
/face
Submit an image for face detection
GET
/face/{taskId}
Get face detection result
POST
/face_age
Submit an image for face age estimation
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AIception's HTTP Basic credentials sit in Jentic's vault and are converted into the Authorization header at request time. Agents call the operation by ID and never see the username/password, so leaked agent transcripts cannot be replayed.
Intent-based discovery
Agents search by intent (e.g. 'detect the object in an image' or 'check whether an image contains nudity') and Jentic returns the matching POST/GET pair so the async task pattern is self-describing.
Time to first call
Direct integration: 1 day to wire HTTP Basic auth and the POST/GET task polling for each capability. Through Jentic: under an hour — search, load schema, execute, with task polling scripted around the runtime.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using AIception Interactive API through Jentic.
Why is there no official OpenAPI spec for AIception Interactive?
AIception does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call AIception Interactive via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does AIception Interactive use?
HTTP Basic authentication using your AIception user account credentials. Through Jentic, the credentials live in the vault and are applied at request time, so the agent never holds the username and password directly.
How does the task pattern work in AIception?
Each capability has a POST endpoint that creates a task and returns a taskId, plus a GET /{capability}/{taskId} endpoint that returns the result. Poll the GET endpoint until the task is complete; processing time depends on the capability and image size.
How do I run object detection on an image through Jentic?
Search Jentic for 'detect the object in an image'. Load the POST /detect_object schema, supply the image_url, and execute. Jentic returns the taskId; load GET /detect_object/{taskId} to retrieve the label once processing completes.
Can I run multiple capabilities on the same image in parallel?
Yes. Each capability has its own POST endpoint and taskId namespace, so an agent can fire /face, /face_age, and /detect_object concurrently for one image and poll each task ID independently.
What are the rate limits for AIception Interactive?
The OpenAPI spec does not declare explicit per-second limits. Because tasks are async, throughput is bounded more by worker concurrency than request rate; poll task results with backoff rather than tight loops.
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