One of our biggest launches. The validated data foundation built on your docs, now provides full traceability of any custom vibe coded app built on @parsewise. Read more: parsewise.ai/blog/whats-new-…

@parsewise is SOTA on Databricks OfficeQA benchmark, over Fable, GPT5.5 All while using Gemini flash models, showing that many use cases will go towards cheaper, faster models. Results and tech breakdown: parsewise.ai/officeqa-sota Props to @databricks, @superannotate, @USAFacts

Can you catch LLM omissions? Mostly no. And false negatives are even worse than hallucinations. Parsewise does exhaustive search -> you get reasoning traces even for cases where the LLM chooses to ignore details. E.g. married person changed their name - should it be included?

🔎 Interpretability of LLMs has gotten worse. For better responsibility quality, now there is more reasoning and longer walls of text. This gets worse with long horizon agentic analyses. We built @parsewise to fix that and help you verify fast.