Semantic search | casestudies.dev

import requests
url = "https://whatsapp-backend-knd2.onrender.com/v1/casestudies/semantic"
payload = { "query": "companies that improved sales pipeline with better data enrichment" }
headers = {
    "X-API-Key": "<apiKey>",
    "Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())

{
  "data": [
    {
      "id": "8d25bb21-6fad-471d-9899-8facbe032549",
      "title": "Remote relies on Kernel custom data to build pipeline",
      "summary": "Remote used Kernel to add 15,000 net new accounts in one week and book 130+ extra meetings monthly.",
      "featured_client": "Remote",
      "industry": "hr tech",
      "similarity": 0.4898
    }
  ],
  "query": "companies that improved sales pipeline with better data enrichment"
}

Search case studies using natural language. Uses vector similarity to find the most relevant results, even when exact keywords don't match. Combine with filters to narrow results by company or industry first, then rank by semantic relevance.

Authentication

X-API-Keystring

Your casestudy.dev API key. Get one at POST /v1/keys.

Request

This endpoint expects an object.

querystringRequired

Natural language search query

companystring or nullOptional

Filter by company slug

industrystring or nullOptional

Filter by industry

tagstring or nullOptional

limitintegerOptionalDefaults to 10

similarity_thresholddoubleOptionalDefaults to 0.3

Minimum similarity score (0-1)

Response

Ranked case study results

datalist of objects

querystring