Ali Beheshti's Work | ContraWork by Ali Beheshti
Ali Beheshti

Ali Beheshti

AI Engineer | Document AI, Voice & Vision Systems

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Cover image for Designed a multimodal AI system
Designed a multimodal AI system combining voice input, document understanding, and structured data extraction. The system processes full-page documents and spoken input together, allowing flexible and efficient data capture. Key capabilities: AI-based document interpretation (beyond OCR) Voice-driven interaction and data entry Structured JSON outputs for downstream systems Support for multi-record extraction and validation This approach enables fully automated workflows in data-heavy environments and field operations.
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Cover image for Developed an AI-powered platform for
Developed an AI-powered platform for automating field data collection, analysis, and reporting. The system integrates multiple input methods, including handwritten sheets, voice input, and direct data entry. Key capabilities: Automated data extraction and validation AI-assisted report generation Workflow tracking and project management Customizable outputs for engineering and technical teams Designed to reduce manual work and improve accuracy in field operations.
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Cover image for Built a voice-enabled system that
Built a voice-enabled system that allows users to input structured data through natural conversation. The system captures spoken input and uses AI to map responses into structured fields in real time. Features include: Speech-to-text integration AI-assisted field mapping Multilingual support Real-time validation of inputs Designed for field environments where manual data entry is inefficient or impractical.
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Cover image for Developed a system that replaces
Developed a system that replaces traditional OCR by using AI to interpret full-page handwritten documents. Instead of extracting text line-by-line, the system: Identifies document type Uses a structured JSON contract to define required fields Sends full-page images to an AI model Returns clean, structured data (including multi-record tables) Supports: Variable number of records per document Field validation and normalization Spreadsheet-ready outputs This approach significantly improves accuracy compared to OCR-based solutions.
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