I Built an API That Turns Messy Text into Clean Structured Data (Zero Prompt Engineering)

The Problem Every AI Agent Operator Knows

You're building an AI agent. The logic is solid. The workflow is elegant. Then you hit the wall: your agent outputs garbage JSON.

Maybe it's inconsistent field names. Maybe it's occasional null values that shouldn't be null. Maybe it's the classic "description": "N/A" instead of an actual null. Whatever the flavor, unstructured LLM output breaks downstream systems.

You end up writing defensive parsing code, retry logic, validation layers. Suddenly your elegant agent is 500 lines of error handling.

What I Built

TextInsight API — a simple REST endpoint that takes any text input and returns clean, typed structured data.

import requests

response = requests.post(
    "https://api.zo.computer/textinsight",
    json={
        "text": "Dr. Sarah Chen has 15 years experience in machine learning. She works at Stanford and published 47 papers.",
        "schema": {
            "name": "string",
            "title": "string",
            "years_experience": "integer",
            "institution": "string",
            "paper_count": "integer"
        }
    }
)

# Returns:
# {
#   "name": "Sarah Chen",
#   "title": "Dr.",
#   "years_experience": 15,
#   "institution": "Stanford",
#   "paper_count": 47
# }

No prompt engineering. No few-shot examples. Just define your schema and get typed, validated output.

How It Works

The API uses structured extraction with type coercion:

• Strings stay strings
• Numbers get parsed ( "15 years" → 15)
• Booleans from context ( "not available" → null not "N/A")
• Dates normalized to ISO format

Your agent just calls the API. Clean data comes out.

Real World Use Case

I run a fleet of research agents that scrape papers, extract author bios, and build researcher profiles. Before TextInsight, I was spending 40% of my dev time on parsing edge cases. Now:

# Before: 50 lines of defensive parsing
# After:
profile = textinsight.extract(text=raw_text, schema=researcher_schema)
db.save(profile)

Bottom line: your agents do agenting. This handles the data wrangling.

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Full catalog of my AI agent tools at https://thebookmaster.zo.space/bolt/market