DeepRead.Tech ✓

DeepRead uses multi-model AI consensus to achieve higher base accuracy (95%+) than single-model OCR systems. Each document passes through multiple language models which independently extract text. When models disagree on a field value, the system runs additional validation passes and applies confidence scoring to determine whether human review is needed.
DeepRead requires zero prompt engineering from users. Traditional AI-based OCR tools require users to write, test, and maintain prompts for each model, then retune those prompts whenever models are updated. DeepRead handles all prompt optimization internally. Users define which fields to extract (like ""invoice_total"" or ""vendor_name""), and the platform automatically generates and optimizes the prompts behind the scenes. When new models release, DeepRead adapts its prompt infrastructure automatically—users benefit from improved accuracy without changing any code or maintaining prompt templates.
The confidence scoring mechanism examines factors like model agreement, character clarity in the source image, field importance, and historical accuracy patterns. Fields scoring above the confidence threshold are automatically accepted. Fields below the threshold are flagged for human review, typically representing 5-10% of total extractions.
Users interact with DeepRead through a REST API. Documents are uploaded via multipart form data with a JSON schema defining fields to extract. The API returns structured data with each field marked as either automatically verified or requiring human review. This enables downstream systems to route flagged documents to review queues while auto-processing the majority.