NinjaDoc Ai

Product Introduction

1. NinjaDoc AI is a document Q&A platform that converts unstructured document data into structured JSON outputs using natural language questions, providing exact coordinates for every extracted data point within the original document. It enables users to define custom extraction schemas by asking questions like "What’s the total?" or specifying technical field names, then integrates these schemas via a REST API for automated processing. The platform supports diverse document types, including invoices, contracts, IDs, and medical forms, with verifiable proof of data origins through geometry coordinates.
2. The core value of NinjaDoc AI lies in its ability to deliver structured data with cryptographic proof of extraction accuracy, eliminating reliance on error-prone OCR templates or unverifiable generic AI outputs. It replaces months of manual integration work with a five-minute API setup, offering developers and businesses a scalable solution for document intelligence that combines natural language flexibility with technical precision.

Main Features

1. NinjaDoc AI provides a visual Q&A schema builder that allows users to define extraction rules using natural language queries (e.g., "What’s the monthly fee?") or technical field names (e.g., "invoice_total"), creating reusable processors for document types like invoices or contracts without coding. Schemas automatically map questions to document layouts, handling variations in formatting through context-aware AI.
2. The platform offers a REST API that processes documents in seconds, returning JSON responses with extracted values, bounding box coordinates (geometry), confidence scores, and metadata. Developers send documents and a processor_id (e.g., "invoice_qa_processor") to integrate extraction into workflows, replacing manual data entry or complex OCR pipelines.
3. Every extracted field includes exact coordinates (e.g., [[18.8,20.8],[43.8,20.8],[43.8,23.3],[18.8,23.3]]) to verify data origins, enabling downstream applications to highlight source text in PDFs/UIs or validate compliance requirements. This coordinate precision is maintained across rotated, skewed, or multi-column documents.

Problems Solved

1. NinjaDoc AI addresses the inefficiency and inaccuracy of traditional document processing methods, such as manual data entry, rigid OCR templates requiring per-vendor adjustments, and generic AI APIs lacking verifiable proof. It eliminates the need for costly template maintenance ($500+/template) and months of OCR integration work.
2. The product targets developers, data engineers, and industries like finance, healthcare, and logistics that require auditable data extraction from documents such as invoices, legal contracts, medical forms, and shipping manifests.
3. Typical use cases include automating accounts payable by extracting invoice totals and due dates, verifying patient insurance IDs from medical records, analyzing contract terms for legal review, and validating ID expiration dates for KYC compliance.

Unique Advantages

1. Unlike template-based OCR tools or generic LLM APIs, NinjaDoc AI combines natural language querying with coordinate-proofed structured outputs, allowing both business users and developers to interact with documents in their preferred mode.
2. The platform’s self-learning AI core improves extraction accuracy across document variations over time, while its pay-per-use pricing ($10 for 4,500 credits) undercuts legacy solutions costing $3K-$20K+/month.
3. Competitive advantages include zero-configuration schema creation, multi-document type support (e.g., a single "invoice" processor handles diverse layouts), and developer-centric features like webhooks, usage dashboards, and upcoming SDKs.

Frequently Asked Questions (FAQ)

1. How does NinjaDoc AI differ from traditional OCR tools? NinjaDoc AI does not require predefined templates or manual field mapping, using context-aware AI to answer questions and extract data from any document layout while providing coordinate proof. Traditional OCR tools fail when document formats change or lack rigid templates.
2. Can it handle documents with complex layouts or multiple pages? Yes, the platform’s AI understands relationships between sections and extracts data from multi-page PDFs, tables, or skewed scans, returning coordinates relative to the original document’s page dimensions.
3. What is the integration time for the API? Developers can integrate the REST API in under five minutes using cURL or HTTP clients, with no upfront configuration beyond defining Q&A schemas in the visual builder.
4. Which document types are supported? The platform processes invoices, contracts, IDs, medical forms, bills of lading, and custom document types through user-defined schemas, with prebuilt processors for common categories.
5. How is data security handled? All document processing is encrypted in transit and at rest, with optional on-premise deployment for regulated industries. Anonymized data is used only for model improvement unless explicitly opted out.