Ask in plain English.
Get verified predictions.

# Upload your data
curl -X POST https://api.dsnresearch.com/api/data/upload \
  -H "X-API-Key: dsn_..." \
  -F "file=@sales_data.csv"

# Run predictive analytics
curl -X POST https://api.dsnresearch.com/api/models/predict \
  -H "X-API-Key: dsn_..." \
  -H "Content-Type: application/json" \
  -d '{
    "source_id": 42,
    "target": "revenue",
    "model": "random_forest"
  }'

import requests

API_KEY = "dsn_..."
BASE    = "https://api.dsnresearch.com"

# Upload data
with open("sales_data.csv", "rb") as f:
    resp = requests.post(
        f"{BASE}/api/data/upload",
        headers={"X-API-Key": API_KEY},
        files={"file": f}
    )

# Run predictions
result = requests.post(
    f"{BASE}/api/models/predict",
    headers={"X-API-Key": API_KEY},
    json={"source_id": 42, "target": "revenue"}
)
print(result.json())

const API_KEY = "dsn_...";
const BASE = "https://api.dsnresearch.com";

// Upload data
const form = new FormData();
form.append("file", fileInput.files[0]);

await fetch(`${BASE}/api/data/upload`, {
  method: "POST",
  headers: { "X-API-Key": API_KEY },
  body: form,
});

// Run predictions
const res = await fetch(`${BASE}/api/models/predict`, {
  method: "POST",
  headers: {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    source_id: 42,
    target: "revenue",
  }),
});
console.log(await res.json());