Technology

Built for scalable AI workloads

Our infrastructure is designed to support compute-intensive AI inference and document-processing workloads as usage scales.

Processing architecture

  1. Ingestion

    PDFs, scans, Office files

  2. OCR & parsing

    Layout-aware text extraction

  3. Embeddings

    Semantic representations

  4. Vector index

    Searchable knowledge

  5. LLM reasoning

    Extraction, summaries, answers

  6. Structured output

    Fields, summaries, API

Stack

The components behind the product

Large language models

Modern LLMs read and reason over document content to extract fields, summarize, and answer questions.

OCR & document processing

Scanned pages and complex layouts are converted into clean, machine-readable text before analysis.

Embeddings

Documents are encoded as vector representations that capture meaning, not just keywords.

Vector search

Semantic retrieval finds the passages most relevant to a question across entire collections.

GPU-accelerated inference

Inference workloads run on GPU-backed infrastructure designed for responsive analysis.

Cloud infrastructure

A cloud-native architecture supports document processing and inference as usage scales.

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