Exact Magic
Transform the Web into a Structured Database with AI-Native Data Extraction.
The high-performance AI engine for automated data extraction and complex reasoning at scale.
Inferno is a next-generation AI-native platform specialized in high-throughput document processing and automated reasoning. By 2026, Inferno has established itself as the industry standard for 'Context-Aware Extraction,' utilizing a proprietary inference engine that leverages speculative decoding and hardware-specific optimizations to achieve sub-second processing for multi-page documents. Unlike traditional OCR tools, Inferno functions as an agentic layer that understands schema-less data, allowing enterprises to transform unstructured PDFs, emails, and images into structured JSON with 99.2% accuracy. Its technical architecture is built on a distributed cluster of H100s/B200s, providing a 'Compute-on-Demand' model that scales horizontally based on job volume. For 2026 market positioning, Inferno bridges the gap between raw LLM APIs and enterprise middleware, offering built-in PII masking, SOC2-compliant sandboxes, and a 'Low-Code' workflow builder that integrates directly with legacy ERP systems. It is specifically optimized for high-token-count contexts, enabling the analysis of 1,000+ page documents without losing semantic coherence.
Uses a smaller draft model to predict token sequences, verified by a larger model, accelerating throughput by 3x.
Transform the Web into a Structured Database with AI-Native Data Extraction.
The Intelligent Data Extraction Engine for High-Fidelity Unstructured-to-Structured Transformation.
The high-performance intelligence layer for structuring messy, unstructured data at scale.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Autonomous agents that iteratively refine data extraction when high-confidence thresholds aren't met.
Simultaneously processes visual layout and semantic text to understand complex tables and handwriting.
Indexes document chunks in real-time to provide citations for every extracted data point.
Allows deployment of optimized weights to local clusters for zero-data-leakage environments.
Automatically validates extracted data against external APIs (e.g., VAT verification, LinkedIn profiles).
Maintains version history of extraction logic to ensure backward compatibility as AI models update.
Manual entry of thousands of varying invoice formats leads to errors and slow processing.
Registry Updated:2/7/2026
Route to the 'Approval' queue if data matches, otherwise flag for review.
Analyzing 500+ page legal documents for specific indemnity clauses is time-prohibitive.
Doctors spend hours reviewing disparate patient histories and lab results.