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 adds workflow-level evidence for Astra in multi-document financial review, but it does not establish a general model-selection result. Against the candidates, it reinforces testing retrieval, detection, and completion together while narrowing the claim to one planted-error setup whose metric, baseline, complexity, and real-world error coverage remain unspecified.