How Doclo Works

Doclo is a document intelligence orchestration platform. It provides a unified interface for AI-powered document processing, from OCR to structured data extraction.

The Problem

Building document intelligence pipelines today is complex:

Vendor Lock-In: Teams stitch together multiple SDKs (OpenAI, Tesseract, etc.), resulting in brittle, provider-specific code. Switching providers requires significant rewrites.
No Automatic Failover: When a provider goes down or hits rate limits, your pipeline stops. There’s no automatic retry or fallback.
Lack of Visibility: Without built-in monitoring, you have no per-document cost tracking or accuracy metrics. Quality is a black box until something breaks.
Difficult Provider Switching: Testing a new provider means rewriting integration code. Comparing providers on your actual data is nearly impossible.

The Solution

Doclo addresses these challenges with a production-ready orchestration layer:

Provider-Agnostic Architecture

All providers implement unified interfaces. Swap OpenAI for Anthropic or Gemini with a config change - no code rewrite required.

// Switch providers by changing config only
const provider = createVLMProvider({
  provider: 'google',  // 'openai', 'anthropic', 'xai'
  model: 'google/gemini-2.5-flash',  // 'openai/gpt-4o', 'anthropic/claude-sonnet-4'
  apiKey: process.env.OPENROUTER_API_KEY!,
  via: 'openrouter'  // Remove for native provider access
});

Automatic Fallback and Retry

Built-in resilience with circuit breakers, exponential backoff, and smart error handling:

const provider = buildLLMProvider({
  providers: [
    { provider: 'openai', model: 'openai/gpt-4.1', ... },
    { provider: 'anthropic', model: 'anthropic/claude-haiku-4.5', ... }
  ],
  maxRetries: 3,
  useExponentialBackoff: true
});

Consensus Voting

Run extraction multiple times and take the majority vote for each field. This dramatically improves accuracy for critical data.

extract({
  provider: vlmProvider,
  schema: invoiceSchema,
  consensus: {
    runs: 3,
    level: 'field',  // 'field' or 'object'
    strategy: 'majority'
  }
})

Cost and Accuracy Visibility

Every extraction returns detailed metrics:

Per-document cost tracking in USD
Field-level accuracy metrics when using consensus
Provider performance monitoring

Architecture Overview

Documents flow through Flows (pipelines) composed of Nodes (operations) that use Providers (AI services) to produce Structured Data.

Core Components

Documents

Input formats and the intermediate representation (DocumentIR)

Providers

OCR and LLM providers that power document processing

Nodes

Processing operations: parse, extract, split, categorize

Flows

Composable pipelines that chain nodes together

Data Flow Patterns

Direct VLM Extraction

Fastest path for simple documents:

OCR + LLM Pipeline

Most accurate for text-heavy documents:

Multi-Document Processing

Split and process document bundles:

Key Terminology

Term	Definition
Flow	A composable pipeline of nodes that transforms documents into structured data
Node	A reusable, stateless operation (parse, extract, categorize, etc.)
Provider	An AI service (OpenAI, Anthropic, Google) or OCR service (Datalab, Mistral, Reducto)
Model	A specific model from a provider (gpt-4o, gemini-2.5-flash, surya, marker)
DocumentIR	Intermediate representation format that decouples parsing from extraction
Schema	JSON Schema defining the structure of extracted data
Consensus	Run extraction N times and take majority vote for improved accuracy

Getting Started

Concepts

SDK

Doclo Cloud

Guides

Resources

How Doclo Works

The Problem

The Solution

Provider-Agnostic Architecture

Automatic Fallback and Retry

Consensus Voting

Cost and Accuracy Visibility

Architecture Overview

Core Components

Documents

Providers

Nodes

Flows

Data Flow Patterns

Direct VLM Extraction

OCR + LLM Pipeline

Multi-Document Processing

Key Terminology

Next Steps

Quickstart

Documents

​The Problem

​The Solution

​Provider-Agnostic Architecture

​Automatic Fallback and Retry

​Consensus Voting

​Cost and Accuracy Visibility

​Architecture Overview

​Core Components

Documents

Providers

Nodes

Flows

​Data Flow Patterns

​Direct VLM Extraction

​OCR + LLM Pipeline

​Multi-Document Processing

​Key Terminology

​Next Steps

Quickstart

Documents

The Problem

The Solution

Provider-Agnostic Architecture

Automatic Fallback and Retry

Consensus Voting

Cost and Accuracy Visibility

Architecture Overview

Core Components

Data Flow Patterns

Direct VLM Extraction

OCR + LLM Pipeline

Multi-Document Processing

Key Terminology

Next Steps