Document collection and classification: Build systems that collect client documents and identify what they are, including tax forms, financial statements, receipts, and supporting materials.
Extraction and normalization: Turn structured and unstructured documents into reliable, usable data. You will design workflows to extract fields, preserve source context, resolve ambiguity, and route low-confidence results for human-in-the-loop review.
Agentic return review: Build agents that examine a completed return alongside its source documents and relevant tax context. The system should identify missing information, inconsistencies, and potential errors, then explain findings clearly and propose corrections for a tax professional to review.
The AI experience: Shape how tax professionals understand, trust, and act on AI output. This includes confidence, citations to source documents, review workflows, correction flows, and clear boundaries between suggestions and final decisions.
Evaluation and reliability: Build the evaluation, observability, and feedback systems needed to measure extraction quality, agent performance, and user outcomes over time.