I tested five leading AI models on real business tasks: extracting fields from invoices and parsing messy tables. The models evaluated were AWS Textract, Azure Document Intelligence, Google Document AI, GPT-4o, and Gemini 1.5 Pro. The goal was to measure accuracy, speed, and robustness against real-world documents.
Quick conclusion: none solved everything, but some performed well enough to deploy solutions with minimal cleanup. If you're going to incorporate AI into critical processes, test first or plan for post-processing cleanup.
Gemini 1.5 Pro: the best balance of speed, accuracy, and structural understanding. It detected complex invoice fields and maintained consistency in the output schema. Recommended when you need an all-in-one solution and value speed in production.
GPT-4o: excelled on clean and semi-structured invoices, retrieving totals and key fields with high reliability. However, it had notable difficulties with messy tables and non-standardized formats, requiring additional post-processing steps.
AWS Textract: very fast and consistent, with rigid and predictable behavior. Ideal when input is relatively homogeneous and operational performance is prioritized. Not the best for extremely erratic scenarios without a normalization pipeline.
Azure Document Intelligence: met the basics of extraction and structuring. A good option for integrations in Microsoft ecosystems and when you need a stable solution without intensive fine-tuning. Not as cutting-edge in extreme cases or with heavily deteriorated documents.
Google Document AI: powerful on clean, labeled documents but showed weakness with dirty inputs, noisy images, or poorly delimited tables. Requires prior document preparation or a robust cleanup module to achieve confidence in production.
Practical lessons: 1) There is no universal model for all formats. 2) For invoices, GPT-4o and Gemini are strong candidates, but Gemini wins on structure and speed. 3) For flows receiving varied images, combining Textract or Document AI with cleanup steps improves results. 4) Planning a validation and normalization pipeline reduces operational errors.
At Q2BSTUDIO we help companies identify the best combination of models and architectures for their use cases. We are specialists in custom software development and custom applications, we implement artificial intelligence and AI solutions for businesses, we deploy personalized AI agents, and we build extraction and cleanup pipelines that integrate AWS and Azure cloud services.
Our services include custom software, cybersecurity, business intelligence services, and Power BI solutions for visualization and control. We design integrations with models like GPT-4o and Gemini and with platforms like AWS Textract and Google Document AI, adapting the solution to minimize rework and maximize accuracy.
If you need a proof of concept or want to optimize invoice and table processing, Q2BSTUDIO develops everything from custom applications to extraction pipelines with AI agents and Power BI dashboards. Contact us to evaluate performance, estimate cleanup effort, and deploy with security and compliance guarantees.
Final summary: test before deciding, combine models when necessary, and rely on experts in artificial intelligence and AWS and Azure cloud services to accelerate results and reduce risks.
