Reducto Blog

We raised a $24.5M Series A led by Benchmark to help enterprises turn unstructured data into accurate, LLM-ready inputs—at scale. Read more on what we've built and where we're headed next.

Health insurance is a unique space that can benefit greatly from LLMs and proper document ingestion at scale—use AI to help automate extracting important data with accuracy.

If your extraction outputs aren't as expected, troubleshoot first by checking if you're making any of these 5 mistakes. Testing out schemas often need some tweaking, and a good quality schema will lead to a good quality output.

If you're deciding whether to build your own document processing pipeline in house or buy an alternative option, this guide highlights some of the tradeoffs and why Reducto might be a good fit.

We're excited to release RolmOCR, a drop-in alternative to olmOCR with a newer base model and other performance improvements.

We benchmarked Mistral AI vs. Gemini Flash 2.0 to assess the accuracy of their OCR models after release. The results are intriguing as they don't appear to align with what was posted on Mistral's blog.

We released a new comprehensive open benchmark for table parsing.

Reducto now powers ingestion pipelines for some of the world's leading AI companies, and we're excited to share we've raised $8.4M in funding led by First Round to further our mission of making human data LLM-ready.

A behind the scenes look of how Reducto started powering ingestion for one of the world's largest companies.

We've spent the last few months building a powerful document ingestion for LLM workflows. We're excited to share more about what we've built.

Explore the hidden costs of manual document processing, from time inefficiencies to error rates. Learn how Reducto's AI-driven solution can streamline these processes, saving businesses significant time and resources.