DocuNet — Enterprise-Grade Document Intelligence Subnet on Bittensor

Overview

DocuNet is a decentralized AI subnet built on Bittensor that creates a competitive marketplace for enterprise document intelligence. It enables miners to extract structured data from complex business documents while validators verify accuracy through sampling-based adjudication. Rewards are distributed based on verified performance, aligning economic incentives with measurable intelligence.

DocuNet transforms enterprise document automation budgets into on-chain economic demand, creating a sustainable intelligence market within the Bittensor ecosystem.

Problem

Enterprises across finance, insurance, legal, and accounting sectors process millions of documents annually — invoices, contracts, claims, compliance forms, and KYC records.

Current solutions suffe

• High error rates (5–20

• Expensive

• Vendor loc

• Lack of transparent performance benchmarking

• Limited incentive for continuous quality improvement

Traditional AI APIs operate as black boxes. There is no open competition that continuously rewards the most accurate models in a trust-minimized environment.

This creates inefficiency, high operational costs, and stagnation in model performance.

Solution

DocuNet introduces a decentralized competitive framework for document intelligence.

Core Idea

• Miners compete to extract structured data from documents.

• Validators verify outputs through randomized sampling.

• Rewards are distributed proportionally to verified accuracy.

• Stake and reputation mechanisms discourage low-quality participation.

The system produces a measurable stream of intelligence, not just AI output.

How It Works

1. Miner Role

Miners receive document inputs and return structured outputs in standardized JSON format:

• Extracted fields (invoice number, total amount, due date, policy ID, etc.)

• Confidence scores

• Optional metadata

Miners can use any internal model architecture (OCR, LLM, layout-aware transformers), creating open competition.

2. Validator Role

Validators perform randomized sampling and verification against ground truth or adjudicated references.

They compute:

• Verified Accuracy

• Field-level precision

• Confidence calibration error

• Latency metrics

Validators are also incentivized, and dishonest behavior can be penalized.

3. Incentive Mechanism

At each epoch, subnet emissions are distributed using a performance-weighted scoring model:

Score_i =
0.7 × VerifiedAccuracy

• 0.2 × NormalizedThroughput

• 0.1 × Reputation

Rewards are proportional to relative performance within the subnet.

This ensures:

• Accuracy dominates incentives

• Speed matters but does not override quality

• Long-term consistent performance is rewarded

Anti-Exploitation Design

DocuNet integrates multiple defensive layers:

• Minimum stake requirement to reduce Sybil attacks

• Progressive sampling for suspicious miners

• Reputation decay for inconsistent accuracy

• Cross-validation to prevent validator collusion

• Confidence calibration penalties for overconfident false outputs

This creates a game-theoretically stable competitive environment.

Proof of Intelligence

Each epoch produces a verifiable time-series of miner performance:

• Verified accuracy

• Sample count

• Calibration error

• Uptime

This creates a transparent intelligence benchmark, aligning directly with Bittensor’s vision of measurable AI competition.

Market Opportunity

Enterprise document automation represents a multi-billion dollar global market, particularly in:

• Insurance claim processing

• Accounts payable automation

• Legal contract parsing

• Financial compliance (KYC / AML)

DocuNet offers:

• Competitive performance optimization

• Reduced vendor dependency

• Transparent benchmarking

• Economic alignment between users and intelligence providers

Enterprise payments can flow into subnet treasury, converting real-world automation budgets into sustainable on-chain incentive loops.

Why Bittensor

Bittensor is uniquely positioned to host DocuNet because:

• It provides a decentralized emission model

• It enables competitive AI markets

• It aligns incentives between intelligence producers and validators

• It supports measurable performance scoring

DocuNet exemplifies Bittensor’s core thesis: intelligence as a market.

Roadmap

Phase 1 — Ideathon

• Scoring simulation engine

• Miner competition modeling

• Economic design documentation

Phase 2 — Testnet Deployment

• Subnet implementation using Bittensor template

• Basic miner & validator nodes

• Live scoring dashboard

Phase 3 — Enterprise Pilot

• Small-scale invoice automation pilot

• SLA-based accuracy thresholds

• Revenue-to-subnet treasury integration

Vision

DocuNet aims to become the standard decentralized benchmark for enterprise document intelligence.

Instead of trusting opaque AI APIs, businesses will access a competitive intelligence market where accuracy is continuously measured, incentivized, and improved.

This is not just another AI service.

It is a measurable, economically aligned intelligence layer for real-world business infrastructure.

During the Ideathon phase, we focused on building a solid economic and technical foundation for DocuNet rather than only conceptual design.

1. Incentive Mechanism Design

We developed and refined a performance-based reward model where emissions are allocated according to:

• Verified accuracy

• Normalized throughput

• Reputation score

We simulated multiple miner scenarios to test:

• High-accuracy / low-speed miners

• High-speed / low-accuracy miners

• Malicious actors attempting random outputs

The simulation confirmed that the weighting system strongly favors sustained accuracy over short-term gaming behavior.

2. Attack & Game-Theory Analysis

We analyzed potential attack vectors, including:

• Sybil attacks

• Validator collusion

• Confidence inflation

• Low-effort spam miners

Mitigation mechanisms were designed, including:

• Minimum stake requirements

• Progressive sampling

• Reputation decay

• Cross-validator auditing

This ensures long-term economic stability of the subnet.

3. Subnet Architecture Planning

We mapped the full technical architecture using:

• Bittensor subnet template (Python-based)

• Miner and validator role separation

• JSON schema for structured extraction outputs

• Epoch-based scoring logic

We also defined clear API specifications for miner input/output standardization.

4. Proof-of-Intelligence Framework

We designed a measurable scoring system that produces a time-series performance stream per miner, including:

• Verified accuracy

• Calibration error

• Sample size

• Latency

This aligns with Bittensor’s core philosophy of measurable intelligence markets.

5. Roadmap Validation

We created a realistic deployment roadmap:

• Simulation environment for scoring logic

• Testnet deployment plan

• Initial enterprise pilot use case (invoice processing)

The focus during the Ideathon was ensuring that DocuNet is economically viable, technically implementable, and aligned with real-world demand.

Fundraising Status

DocuNet is currently in the ideation and technical design phase and has not raised external capital.

At this stage, the project is bootstrapped and focused on validating:

• Incentive mechanism robustness

• Subnet architecture feasibility

• Market applicability in enterprise document automation

Our immediate priority is to complete simulation testing and testnet deployment before pursuing formal fundraising.

Future Funding Strategy

Following successful testnet validation, we plan to explore:

• Strategic ecosystem grants within the Bittensor community

• Early-stage Web3 infrastructure investors

• Enterprise pilot partnerships to validate revenue flow

We believe fundraising should follow demonstrated technical viability and measurable performance, rather than precede it.

Our long-term vision is to create a sustainable intelligence market driven by real enterprise demand, minimizing reliance on speculative capital.