Your Intern’s Still Parsing Contracts? Azure AI + .NET 8 Can Do It Faster, Smarter, and Without Quitting

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Photo by Tingey Injury Law Firm on Unsplash

Did you know you don’t need a junior analyst to suffer through 500-word docs? You need GPT, OCR, and Azure to stop the madness.

The Word Document Jungle

Contracts look “structured” until you try to extract:

• Party names
• Dates
• Renewal clauses
• Payment terms
• Cancellation conditions

Word documents are chaos in disguise. Some use tables, and some use bullet points. Some are just paragraph soup. You can’t just scrape text and hope for the best. You need semantics — something that understands meaning, not just layout.

Turn Word Docs Into Structured SQL Rows

We’ll use:

• Azure Form Recognizer: To convert scanned documents and messy DOCX into structured key-value pairs and tables.
• Azure OpenAI (GPT): To clean up, fill in gaps, and reframe the output.
• .NET 8: To glue it all together and push it to SQL.

This isn’t a “demo-only” approach. It works at scale and can handle real-world docs that would make your intern cry.

Step 1: Convert DOCX or Scans with Azure Form Recognizer

Form Recognizer supports:

• Prebuilt models (Invoices, Contracts, IDs)
• Custom models (train on your own docs)

Why not just use OpenXml in .NET?

Because layout != meaning. You’ll spend weeks building a fragile parser.

Code Snippet (.NET 8 + Azure SDK):

var client = new DocumentAnalysisClient(
    new Uri(endpoint),
    new AzureKeyCredential(apiKey)
);

var result = await client.AnalyzeDocumentAsync(
    "prebuilt-document", BinaryData.FromFile("contract.docx")
);

foreach (var kvp in result.Value.KeyValuePairs)
{
    Console.WriteLine($"{kvp.Key.Content} = {kvp.Value?.Content}");
}

Structured content, including tables, key-values, and paragraphs.

Step 2: Clean and Complete Using GPT (Azure OpenAI)

Let’s say the Form Recognizer gives you partial outputs or ambiguous terms.

Use GPT to fill the gaps and standardize fields like:

• “Effective Date”: standardized to ISO format
• “Termination Window”: parsed to “30 days”
• “Jurisdiction”: normalized

Prompt Template Example:

You are an AI legal assistant. Standardize the following extracted contract data:
- Document Effective Date: "January 5th, 2023"
- Termination Clause: "Either party may terminate..."
Output JSON with fields: effectiveDate (ISO), terminationNoticeDays, governingLaw

API Call in .NET:

var gptResponse = await openAIClient.GetCompletionsAsync(
    deploymentId: "gpt-4",
    new CompletionsOptions
    {
        Prompts = { yourPromptHere },
        MaxTokens = 500,
        Temperature = 0.2
    }
);

Now your messy, half-structured contract is clean JSON

Step 3: Write to SQL Like a Grown-Up

Your data now looks like:

{
  "effectiveDate": "2023-01-05",
  "terminationNoticeDays": 30,
  "governingLaw": "Delaware"
}

Now use EF Core or Dapper to insert into your Contracts table.

dbContext.Contracts.Add(new Contract {
    EffectiveDate = parsed.effectiveDate,
    TerminationNoticeDays = parsed.terminationNoticeDays,
    GoverningLaw = parsed.governingLaw
});
await dbContext.SaveChangesAsync();

This is automation. . This is how you avoid hiring another intern next summer.

What Most Devs Get Wrong

1. They treat DOCX files like structured data.
2. They try to parse paragraphs with Regex.
3. They ignore AI because “it sounds too fancy.”

Meanwhile:

1. Azure Form Recognizer understands layout and semantics.
2. GPT cleans, corrects, and fills in missing meaning.
3. .NET 8 makes it all production-ready and scalable.

This Is More Than Just Tech — It’s a Business Shift

This isn’t about code. It’s about freeing up humans to work on meaningful things. Interns don’t dream of parsing termination clauses. Analysts shouldn’t double-check which document version is correct. This is AI as an intern replacement — and it’s here now.