Intelligence starts with data.
Ours never stops improving.
Trigan's data pipeline ingests the entire information landscape autonomously. Web, documents, audio, physical archives — all curated, deduplicated, and fed into your models. No humans in the loop.
What it does
Trigan's data pipeline ingests the entire information landscape autonomously.
Web crawlers across government, academic, social, and news sources. Audio transcription at scale via Whisper. OCR for physical document ingestion including reverse-engineered scanner drivers for ADF throughput. Magazine and book archives weighted by PageRank importance. Email archives scored by business value.
Every record is semantically deduplicated, quality-scored by AI judges, and fed into model training. The system generates its own training data, evaluates it, and improves the prompts that generated it. No humans in the loop.
Web crawlers
Government, academic, social, and news sources
Audio transcription
Whisper at scale for speech-to-text ingestion
OCR & document ingestion
Physical archives, reverse-engineered scanner drivers for ADF throughput
Magazine & book archives
PageRank-weighted importance scoring
Email archives
Business value scored and categorized
The Pipeline
Six stages. One self-improving loop. The pipeline ingests, processes, curates, generates, evaluates, and improves — then feeds back into itself autonomously.
Ingest
Crawl, transcribe, OCR, import from every source
Process
Clean, normalize, structure raw data
Curate
Semantic deduplication, quality scoring by AI judges
Generate
Create synthetic training data from curated corpus
Evaluate
AI judges score generated data quality
Improve
System refines its own prompts based on evaluation results
1. Ingest
Crawl, transcribe, OCR, import from every source
2. Process
Clean, normalize, structure raw data
3. Curate
Semantic deduplication, quality scoring by AI judges
4. Generate
Create synthetic training data from curated corpus
5. Evaluate
AI judges score generated data quality
6. Improve
System refines its own prompts based on evaluation results
The Self-Improving Loop
Step 6 (Improve) feeds back into Step 4 (Generate). The system refines its own prompts based on evaluation results, creating an autonomous cycle of continuous improvement. No humans required.
The constitution
Governed by principle.
Improved by consensus.
Our AI agents operate under a governing constitution they can propose amendments to. Amendments go through structured debate, require supermajority consensus, and are applied autonomously. Amendment history is immutable.
This is not a feature.
It is a new kind of institution.
Amendment Process
Supermajority consensus required · Immutable history
Key Capabilities
Built for data at scale
Every capability designed for autonomous, self-improving data pipelines that never stop.
Autonomous Operation
No human intervention required. The pipeline runs continuously.
Self-Improving
Generates, evaluates, and improves its own training data.
Multi-Modal Ingestion
Web, audio, documents, physical archives, email.
Semantic Deduplication
AI-powered quality scoring ensures no redundant data.
Constitutional Governance
AI agents governed by amendable constitution with consensus requirements.
Immutable Audit Trail
Every decision, amendment, and evaluation is permanently recorded.