DeepRead.Tech

DeepRead employs a multi-model AI consensus approach to achieve a higher level of accuracy (95%+) compared to single-model OCR systems. Each document undergoes processing through multiple language models that independently extract text. In cases where models disagree on a field value, the system conducts additional validation checks and applies confidence scoring to assess whether human review is necessary.
DeepRead eliminates the need for prompt engineering from users. Traditional AI-based OCR tools often require users to develop, test, and maintain prompts for each model, as well as readjust those prompts when models are updated. DeepRead manages all prompt optimization internally. Users specify the fields to extract (such as invoice_total or vendor_name), and the platform automatically generates and optimizes the prompts in the background. As new models are released, DeepRead automatically adapts its prompt infrastructure, allowing users to benefit from improved accuracy without modifying code or maintaining prompt templates.
The confidence scoring mechanism analyzes factors like model agreement, character clarity in the original image, field significance, and historical accuracy trends. Fields scoring above the confidence threshold are automatically accepted, while those below the threshold are flagged for human review, typically accounting for 5-10% of total extractions.
Users interact with DeepRead through a REST API. Documents are uploaded using multipart form data with a JSON schema that defines the fields to extract. The API provides structured data, with each field marked as either automatically verified or requiring human review. This enables downstream systems to direct flagged documents to review queues while automatically processing the majority.