Legora reviewed 41 documents in minutes with GPT-6 Astra
Legora says GPT-6 Astra reviewed 41 documents within minutes, caught every planted error, and improved its workflow result by nearly 40%, though the underlying measure is unspecified.
Legora applied **GPT-6 Astra** to a financial-review workflow covering **41 documents**. It finished in minutes, found **all four planted errors**, and improved performance by **nearly 40%**.
Builders designing document agents should evaluate the full workflow: retrieval across many files, error detection, completion time, and whether planted issues are consistently recovered.
Legora applied **GPT-6 Astra** to a financial-review workflow covering **41 documents**. It finished in minutes, found **all four planted errors**, and improved performance by **nearly 40%**. Builders designing document agents should evaluate the full workflow: retrieval across many files, error detection, completion time, and whether planted issues are consistently recovered. The material does not define the performance metric, baseline, document complexity, or exact runtime. Planted-error recall also does not establish accuracy on unstructured real-world mistakes.
This provides a concrete multi-document test for Astra and suggests evaluating retrieval, detection, and runtime together rather than judging the model alone. Finding every planted error is encouraging, but undefined baselines and metrics—and the gap between planted and naturally occurring mistakes—leave real-world financial-review reliability unresolved.