Let's Explore More Use Cases with Text-to-API

🔹 Note: Want to dive right in? Learn how to get your free API key here and start sending test requests here! 🚀

Text-to-API isn't just for expense tracking—it can transform all kinds of human-written text like legal contracts, medical records, e-commerce support tickets, and recruitment data into structured JSON effortlessly. Let’s dive into some powerful real-world applications!

Domain: Legal Tech & Contract Analysis

Use Case: 🚀 Automated Contract Parsing & Clause Extraction for Compliance

The Problem:
Law firms, corporations, and compliance teams handle thousands of contracts every year. These contracts contain critical clauses (e.g., payment terms, termination conditions, liability clauses) that need to be extracted, reviewed, and categorized—a process that is manual, slow, and error-prone.

The Solution:
TextToAPI can automatically extract key clauses from legal contracts and convert them into structured JSON. A firm could submit a contract as plain text or a PDF (via OCR), like this:

This Non-Disclosure Agreement (“Agreement”) is made and entered into as of February 10, 2024, by and between Company A, a Delaware corporation with its principal place of business at 123 Main Street, Wilmington, DE, and Company B, a California limited liability company with offices at 456 Elm Avenue, San Francisco, CA. This Agreement shall remain in effect for a period of two (2) years from the Effective Date. Either party may terminate this Agreement with a written 30-day notice to the other party. Any disputes arising from this Agreement shall be governed by the laws of the State of Delaware. Payment for services shall be made within 30 days of receipt of invoice (Net 30 terms). Confidential information includes, but is not limited to, trade secrets, business strategies, customer lists, and proprietary technology.

We can discretize this to a JSON object with fields of interest like:

contract_type

effective_date

parties, where each party should have

name

entity_type

jurisdiction

address

duration

termination_clause

governing_law

payments_terms

confidential_information

etc.

The request body would look something like this:

{
  "input_text": "This Non-Disclosure Agreement (“Agreement”) is made and entered into as of February 10, 2024, by and between Company A, a Delaware corporation with its principal place of business at 123 Main Street, Wilmington, DE, and Company B, a California limited liability company with offices at 456 Elm Avenue, San Francisco, CA. This Agreement shall remain in effect for a period of two (2) years from the Effective Date. Either party may terminate this Agreement with a written 30-day notice to the other party. Any disputes arising from this Agreement shall be governed by the laws of the State of Delaware. Payment for services shall be made within 30 days of receipt of invoice (Net 30 terms). Confidential information includes, but is not limited to, trade secrets, business strategies, customer lists, and proprietary technology.",
  "lang": "en",
  "translation_type": "object",
  "target_object": {
    "fields": [
      {
        "name": "contract_type",
        "type": "string",
        "description": "Type of the contract",
        "allowed_values": ["Non-Disclosure Agreement", "Service Agreement", "Employment Contract"]
      },
      {
        "name": "effective_date",
        "type": "date",
        "description": "Date when the contract becomes effective",
        "format": "YYYY-MM-DD"
      },
      {
        "name": "parties",
        "type": "array",
        "description": "List of parties involved in the contract",
        "fields": [
          {
            "name": "name",
            "type": "string",
            "description": "Name of the party"
          },
          {
            "name": "entity_type",
            "type": "string",
            "description": "Type of business entity",
            "allowed_values": ["Corporation", "LLC", "Partnership", "Sole Proprietorship"]
          },
          {
            "name": "jurisdiction",
            "type": "string",
            "description": "State or country governing the entity"
          },
          {
            "name": "address",
            "type": "string",
            "description": "Physical address of the party"
          }
        ]
      },
      {
        "name": "duration",
        "type": "string",
        "description": "Duration of the contract",
        "format": "text"
      },
      {
        "name": "termination_clause",
        "type": "string",
        "description": "Conditions under which the contract can be terminated"
      },
      {
        "name": "governing_law",
        "type": "string",
        "description": "The jurisdiction that governs this contract"
      },
      {
        "name": "payment_terms",
        "type": "string",
        "description": "The agreed payment terms"
      },
      {
        "name": "confidential_information",
        "type": "array",
        "description": "Types of confidential information covered under the agreement",
        "allowed_values": [
          "trade secrets",
          "business strategies",
          "customer lists",
          "proprietary technology"
        ]
      }
    ]
  }
}

And this will give us a result like the following:

{
    "mapped_object": {
        "confidential_information": [
            "trade secrets",
            "business strategies",
            "customer lists",
            "proprietary technology"
        ],
        "contract_type": "Non-Disclosure Agreement",
        "duration": "2 years",
        "effective_date": "2024-02-10",
        "governing_law": "Delaware",
        "parties": [
            {
                "address": "123 Main Street, Wilmington, DE",
                "entity_type": "Corporation",
                "jurisdiction": "Delaware",
                "name": "Company A"
            },
            {
                "address": "456 Elm Avenue, San Francisco, CA",
                "entity_type": "LLC",
                "jurisdiction": "California",
                "name": "Company B"
            }
        ],
        "payment_terms": "Payment for services shall be made within 30 days of receipt of invoice (Net 30 terms).",
        "termination_clause": "Either party may terminate this Agreement with a written 30-day notice to the other party."
}

✔️ Imagine the time and cost savings for legal teams!

Domain: Healthcare & Medical Transcription

Use Case: 🚀 Automated Medical Notes Structuring for Electronic Health Records (EHR)

The Problem:
Doctors and medical professionals dictate patient consultations, diagnoses, and treatment plans in unstructured notes. These notes need to be converted into structured data for Electronic Health Records (EHRs), insurance claims, and clinical decision support systems.

🩺 Challenges:

Time-consuming manual entry: Doctors spend hours manually inputting data.

Errors & inconsistencies: Manually structured data is prone to mistakes.

Compliance & interoperability: Hospitals and clinics need standardized formats (FHIR, ICD-10, SNOMED CT).

The Solution:
Using TextToAPI, you can process free-text medical notes and extract structured medical data automatically.
A hospital system could send doctor's notes in plain text, and TextToAPI would return structured data in JSON format.

You could have something like this:

Patient: John Doe, 45-year-old male, presents with chest pain and shortness of breath. Symptoms started 2 days ago, worsening since. Denies fever, cough, or recent travel. History of hypertension, on Lisinopril 10mg daily. No known drug allergies. BP: 140/90 mmHg, HR: 88 bpm, RR: 18. ECG shows mild ST depression in leads II and III. Will admit for further evaluation and start on aspirin 81mg and atorvastatin 40mg. Plan for stress test in 24 hours.

Send a request body like this:

{
  "input_text": "Patient: John Doe, 45-year-old male, presents with chest pain and shortness of breath. Symptoms started 2 days ago, worsening since. Denies fever, cough, or recent travel. History of hypertension, on Lisinopril 10mg daily. No known drug allergies. BP: 140/90 mmHg, HR: 88 bpm, RR: 18. ECG shows mild ST depression in leads II and III. Will admit for further evaluation and start on aspirin 81mg and atorvastatin 40mg. Plan for stress test in 24 hours.",
  "lang": "en",
  "translation_type": "object",
  "target_object": {
    "fields": [
      {
        "name": "patient_name",
        "type": "string",
        "description": "Full name of the patient"
      },
      {
        "name": "age",
        "type": "number",
        "description": "Age of the patient"
      },
      {
        "name": "gender",
        "type": "string",
        "allowed_values": ["Male", "Female", "Other"],
        "description": "Gender of the patient"
      },
      {
        "name": "chief_complaint",
        "type": "string",
        "description": "Primary reason for the patient's visit"
      },
      {
        "name": "symptom_onset",
        "type": "date",
        "description": "When the symptoms started"
      },
      {
        "name": "past_medical_history",
        "type": "array",
        "description": "Relevant past medical history"
      },
      {
        "name": "medications",
        "type": "array",
        "description": "Current medications the patient is taking"
      },
      {
        "name": "allergies",
        "type": "array",
        "description": "Any known drug or substance allergies"
      },
      {
        "name": "vital_signs",
        "type": "object",
        "fields": [
          {
            "name": "blood_pressure",
            "type": "string",
            "description": "Recorded blood pressure"
          },
          {
            "name": "heart_rate",
            "type": "number",
            "description": "Heart rate in beats per minute"
          },
          {
            "name": "respiratory_rate",
            "type": "number",
            "description": "Respiratory rate in breaths per minute"
          }
        ]
      },
      {
        "name": "diagnostic_findings",
        "type": "string",
        "description": "Key findings from diagnostic tests"
      },
      {
        "name": "treatment_plan",
        "type": "array",
        "description": "Medications and procedures prescribed"
      },
      {
        "name": "follow_up_plan",
        "type": "string",
        "description": "Next steps for the patient"
      }
    ]
  }
}

And get this back:

{
    "mapped_object": {
        "age": 45,
        "allergies": [],
        "chief_complaint": "chest pain and shortness of breath",
        "diagnostic_findings": "ECG shows mild ST depression in leads II and III",
        "follow_up_plan": "Plan for stress test in 24 hours.",
        "gender": "Male",
        "medications": [
            "Lisinopril 10mg daily",
            "aspirin 81mg",
            "atorvastatin 40mg"
        ],
        "past_medical_history": [
            "hypertension"
        ],
        "patient_name": "John Doe",
        "symptom_onset": "2025-01-21",
        "treatment_plan": [
            "admit for further evaluation",
            "start on aspirin 81mg",
            "start on atorvastatin 40mg"
        ],
        "vital_signs": {
            "blood_pressure": "140/90 mmHg",
            "heart_rate": 88,
            "respiratory_rate": 18
        }
    }
}

✔️ Huge efficiency boost for hospitals and clinics!

Domain: E-Commerce & Customer Support Automation

Use Case: 🚀 Automated Customer Support Ticket Structuring for E-Commerce Businesses

The Problem:
E-commerce businesses receive thousands of customer inquiries daily via email, chat, and support tickets. These messages often contain order issues, refund requests, product complaints, and return inquiries, but they are unstructured and require manual sorting by support teams.

📦 Challenges:

Slow response times – Agents must manually categorize and assign tickets.

Human error – Incorrect categorization leads to inefficient resolutions.

Customer frustration – Delays in addressing issues can lead to bad reviews and lost sales.

The Solution:
TextToAPI can process unstructured customer support messages and extract structured data that automatically categorizes the issue, assigns priority, and determines the next action.

✅ Instant categorization of customer support issues.
✅ Automated routing to the correct department (refunds, shipping, technical support, etc.).
✅ Reduces manual work and speeds up resolution times.

Imagine taking the content of a customer email like this one:

Hi, I ordered a pair of running shoes from your store last week, but I still haven’t received them. My order number is #12345, and the tracking page hasn’t updated in days. Can you check the status for me? Also, if it’s lost, can I get a refund? Thanks!

Sending this request body to TextToAPI:

{
  "input_text": "Hi, I ordered a pair of running shoes from your store last week, but I still haven’t received them. My order number is #12345, and the tracking page hasn’t updated in days. Can you check the status for me? Also, if it’s lost, can I get a refund? Thanks!",
  "lang": "en",
  "translation_type": "object",
  "target_object": {
    "fields": [
      {
        "name": "order_id",
        "type": "string",
        "description": "The order number mentioned in the message"
      },
      {
        "name": "issue_category",
        "type": "string",
        "allowed_values": ["Shipping Delay", "Damaged Item", "Refund Request", "Technical Issue", "Order Cancellation"],
        "description": "The category of the support request"
      },
      {
        "name": "priority",
        "type": "string",
        "allowed_values": ["Low", "Medium", "High"],
        "description": "The urgency level of the request"
      },
      {
        "name": "requested_action",
        "type": "string",
        "allowed_values": ["Check Order Status", "Issue Refund", "Resend Item", "Cancel Order"],
        "description": "The action the customer wants to take"
      },
      {
        "name": "sentiment",
        "type": "string",
        "allowed_values": ["Neutral", "Frustrated", "Angry", "Urgent"],
        "description": "The tone of the customer's request"
      }
    ]
  }
}

And getting this JSON back:

{
    "mapped_object": {
        "issue_category": "Shipping Delay",
        "order_id": "#12345",
        "priority": "High",
        "requested_action": "Check Order Status",
        "sentiment": "Frustrated"
    }
}

✔️ Imagine how much more efficient and streamlined your workflow would become with this automation!

Domain: HR & Recruitment Tech

Use Case: 🚀 Automated Resume Parsing & Candidate Profiling for HR Platforms

The Problem:
HR teams and recruitment platforms receive hundreds or thousands of job applications for each open position. These applications come in varied formats (PDFs, Word docs, plain text emails) and require manual processing to extract key candidate details.

📄 Challenges:

Manual data entry – Recruiters spend hours inputting candidate details into ATS (Applicant Tracking Systems).

Inconsistent formats – Resumes have different structures, making it hard to standardize data.

Slow hiring process – The inefficiency of screening slows down candidate shortlisting.

The Solution:
TextToAPI can process unstructured resume text and extract structured data, automatically formatting it into a JSON object that HR software can consume.

✅ Instant resume parsing into structured candidate profiles.
✅ Automated categorization based on job roles, skills, and experience.
✅ Seamless integration with Applicant Tracking Systems (ATS) like Greenhouse, Lever, and Workday.

Imagine having the following resumé professional summary:

John Doe is a software engineer with 5 years of experience in backend development. He is proficient in Golang, Python, and cloud technologies like AWS and Docker. John previously worked at XYZ Tech, where he led the development of a high-scale API used by thousands of customers. He has a Bachelor's degree in Computer Science from Stanford University. Looking for opportunities in fintech or SaaS companies.

Sending this request body to TextToAPI:

{
  "input_text": "John Doe is a software engineer with 5 years of experience in backend development. He is proficient in Golang, Python, and cloud technologies like AWS and Docker. John previously worked at XYZ Tech, where he led the development of a high-scale API used by thousands of customers. He has a Bachelor's degree in Computer Science from Stanford University. Looking for opportunities in fintech or SaaS companies.",
  "lang": "en",
  "translation_type": "object",
  "target_object": {
    "fields": [
      {
        "name": "full_name",
        "type": "string",
        "description": "Candidate's full name"
      },
      {
        "name": "job_title",
        "type": "string",
        "description": "Candidate's most recent job title"
      },
      {
        "name": "years_of_experience",
        "type": "number",
        "description": "Total years of professional experience"
      },
      {
        "name": "skills",
        "type": "array",
        "description": "List of candidate's key skills"
      },
      {
        "name": "previous_employer",
        "type": "string",
        "description": "Last company the candidate worked at"
      },
      {
        "name": "notable_projects",
        "type": "string",
        "description": "Major projects the candidate contributed to"
      },
      {
        "name": "degree",
        "type": "string",
        "description": "Candidate's highest academic qualification"
      },
      {
        "name": "preferred_industries",
        "type": "array",
        "description": "Industries where the candidate is looking for opportunities"
      }
    ]
  }
}

And getting this JSON back:

{
    "mapped_object": {
        "degree": "Bachelor's degree in Computer Science from Stanford University",
        "full_name": "John Doe",
        "job_title": "Software Engineer",
        "notable_projects": "Development of a high-scale API used by thousands of customers.",
        "preferred_industries": [
            "fintech",
            "SaaS"
        ],
        "previous_employer": "XYZ Tech",
        "skills": [
            "Golang",
            "Python",
            "AWS",
            "Docker"
        ],
        "years_of_experience": 5
    }
}

✔️ Imagine how much faster and more efficient your hiring process would become with this automation!

What’s Next?

Have more ideas for how Text-to-API can be used? The possibilities are endless! Now, it’s time to put it to the test. 🚀

📌 Check out the next sections for:

A full API reference 📖

How to get your test API key 🔑

Start building smarter, faster, and more efficient applications today! 🔥

Using TextToAPI Across Domains

Let's Explore More Use Cases with Text-to-API#

Domain: Legal Tech & Contract Analysis#

Use Case: 🚀 Automated Contract Parsing & Clause Extraction for Compliance#

Domain: Healthcare & Medical Transcription#

Use Case: 🚀 Automated Medical Notes Structuring for Electronic Health Records (EHR)#

Domain: E-Commerce & Customer Support Automation#

Use Case: 🚀 Automated Customer Support Ticket Structuring for E-Commerce Businesses#

Domain: HR & Recruitment Tech#

Use Case: 🚀 Automated Resume Parsing & Candidate Profiling for HR Platforms#

What’s Next?#