Udgam
Sanskrit: “Source / Origin”
10 years of expert decisions. Already inside your systems.
Extracts hidden LLM training signal from SharePoint, Confluence, ERP logs, and Slack — turning institutional knowledge into fine-tuning gold.
How it works
Enterprise Data Crawler
Connects to SharePoint, Confluence, email, ERP logs, QA databases, Slack. Ingestion only — no annotation yet.
Implicit Judgment Detector
LLM scans every document for expert inference: diagnostic conclusions, risk assessments, classification decisions buried in prose.
Training Value Scorer
Each judgment is scored: domain-specificity, LLM knowledge gap, uniqueness. Top 1% surfaced for annotation.
Auto-Annotation Pipeline
Surfaced judgments converted into structured training pairs: input context → expert output. Human review layer included.
Fine-Tune Ready Dataset
Delivered as versioned, documented training dataset. Compatible with any fine-tuning pipeline — OpenAI, Anthropic, open-source.
Simple, transparent pricing
Full Extraction
Enterprise crawl, scoring, annotation pipeline, and delivered dataset. Typical: 10K–50K training pairs.
Discovery Scan
Crawl + scoring only. Tells you how much hidden training data exists before committing to full extraction.
Continuous Mining
Ongoing extraction as new documents enter your systems. Monthly dataset updates with drift detection.
Request a Pilot
No long-term commitment. Results in weeks, not months.