STELA + OpenAI GPT + Azure OCR: No-Code Document Automation

STELA, the Latin-American no-code automation platform, now ships native connectors for Azure OCR (Azure AI Document Intelligence) and OpenAI GPT-4o. Together, the trio lets you extract → classify → publish information from invoices, contracts, and receipts in seconds—no developers required and with enterprise-grade governance.

1 Intelligent Document Processing: a must-have in 2025

The global Intelligent Document Processing (IDP) market is forecast to jump from USD 2.4 billion in 2023 to USD 10.5 billion by 2028 (CAGR 34.9 %) (my.idc.com).
Three forces drive the boom:

1. Mature generative AI – GPT-4o reads images and text with near-human accuracy.

2. Rising compliance pressure – LATAM is rolling out e-invoicing and risk reports that depend on reliable document data.

3. Developer shortage – 67 % of regional CIOs report a lack of RPA coders (CAF, 2024).

Microsoft echoes the trend: a recent post highlights 261 real-world customer stories already live on Azure OpenAI, many centred on document workflows (blogs.microsoft.com).

Bottom line: The question is no longer if you should automate documents but how fast you can do it with a no-code tool.

2 What each technology brings to the table

3 Technical flow—step by step

Set-up time for a proof-of-concept: ± 45 minutes.

Why it’s so efficient

1. Extraction first, classification later – GPT sees a clean JSON, reducing tokens and costs.

2. Pure drag-and-drop – zero SDKs or Python scripts.

3. Flat licensing – spin up extra robots without extra licence fees.

4 Case study: tri-national logistics chain

Result: nine FTEs redeployed to higher-value tasks; customs fines down 70 %.

5 30-day ROI framework

1. Process map – list every doc-driven task.

2. Sample set – upload 200-300 files; record Azure OCR accuracy.

3. Prompt design – define your target JSON:

{"doc_type":"","total":"","account":"","due_date":""}

4. Pilot run – track field accuracy, time per doc, API costs.

5. Business case

Savings=(Current HH−Automated HH)×Hourly Rate−Cloud Costs\text{Savings}=(\text{Current HH}−\text{Automated HH})×\text{Hourly Rate}−\text{Cloud Costs}

Clients hit breakeven in < 4 months once ≥ 70 % of docs need zero human touch.

6 Secure prompt-engineering best practices

7 Benefit comparison

8 FAQs

Which file types does Azure OCR handle? PDF, TIFF, JPEG, PNG, Office, and .eml—up to 500 pages per batch.

Can I swap GPT-4o for another LLM? Yes. The same connector supports any OpenAI-compatible endpoint (Anthropic, Mistral, etc.).

How is data retention managed? Define auto-purge or encrypted Azure Blob archiving policies inside STELA.

Do I need data scientists? No—pre-built models reach > 90 % accuracy; custom training is wizard-driven.

What if Azure experiences latency? STELA queues requests and retries automatically while alerting the operator.

9 Near-term roadmap: contextual generative AI

• No-code RAG – index internal PDFs and query via ChatGPT block.

• Multi-step agents – robots that plan, act, and verify in SAP.

• Private fine-tuning – train compact models on-prem without code.

These enhancements build on STELA & GPT robots to democratise generative AI under strict governance.

10 Conclusion & CTA

The synergy of STELA + OpenAI GPT + Azure OCR wipes out the usual friction between extraction, semantic classification, and RPA orchestration:

• 80 % faster processing · < 0.5 % error rate · flat licensing with full auditability.*

Book a free demo now and see how the next-gen STELA + GPT robots turn your documents into business-ready data in under an hour.

References

• STELA AI – overview of AI capabilities.

• STELA & GPT: intelligent RPA robots – deeper dive into GPT integration.

• IDC. “Worldwide Intelligent Document Processing Market Shares, 2023.” (my.idc.com)

• Microsoft. “How real-world businesses are transforming with AI — with 261 new stories.” (22 Apr 2025) (blogs.microsoft.com)

• Microsoft Learn. “What is Azure AI Document Intelligence?” (learn.microsoft.com)