Bring domain expertise into your AI product. Go from a generic LLM-wrapper to a specialized expert that truly understands your industry.
Tabular classification, regression, text extraction, image analysis, text classification, forecasting, and more.
Start building ML models without the need for large datasets. We generate training data for you.
Choose to build your tools with only open-source models. Full transparency and control.
Five steps to transform your problem into a production-ready API endpoint.
Tell us what you want to accomplish in plain language
Odious tools are authenticated APIs that you can call from everywhere. Integrate into your existing agent framework seamlessly.
Works with your favorite agent frameworks
import requests
from langchain.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
ODIOUS_API_KEY = "your-api-key"
BASE_URL = "https://api.odious.io/tasks"
@tool
def classify_ticket(text: str) -> str:
"""Classify a support ticket into categories like billing, technical, or general."""
response = requests.post(
f"{BASE_URL}/text-classification/ticket-classifier-id",
headers={"X-API-Key": ODIOUS_API_KEY},
json={"text": text}
)
return response.json()["prediction"]
@tool
def analyze_screenshot(image_url: str) -> str:
"""Analyze a screenshot to identify UI issues or error messages."""
response = requests.post(
f"{BASE_URL}/image-analysis/screenshot-analyzer-id",
headers={"X-API-Key": ODIOUS_API_KEY},
json={"image_url": image_url}
)
return response.json()["prediction"]
@tool
def predict_churn_risk(customer_id: str, monthly_spend: float, tickets_last_30d: int) -> str:
"""Predict if a customer is at risk of churning based on their profile."""
response = requests.post(
f"{BASE_URL}/tabular-classification/churn-predictor-id",
headers={"X-API-Key": ODIOUS_API_KEY},
json={"customer_id": customer_id, "monthly_spend": monthly_spend, "tickets_last_30d": tickets_last_30d}
)
return response.json()["prediction"]
# Create the support agent
agent = create_react_agent(
ChatOpenAI(model="gpt-5-mini"),
tools=[classify_ticket, analyze_screenshot, predict_churn_risk]
)Get metrics, logs, usage analytics and auto-scaling from day one. Production-ready infrastructure without the DevOps headache.
Monitor performance, latency, and throughput as it happens
Full request/response logging for debugging and auditing
Track API consumption, costs, and usage patterns at a glance
Infrastructure that automatically scales with your demand
Be among the first to transform your AI workflows with specialized, custom-trained tools.