Privacy-first document preprocessing layer for AI coding agents
Instead of feeding raw sensitive files (scans, NDAs, invoices) into a coding agent like Claude Code and paying heavy token costs, sensitive document work — OCR, entity extraction, summarization, redaction — is offloaded to a self-hosted inference cluster exposed to the agent as an MCP server. The agent only receives a small, redacted Markdown artifact, cutting token spend, improving context quality, and keeping PII inside your own VPC.
online B2B Deep Tech / Infrastructure
From AI Engineer — Turning My Obsidian Vault Into a Local AI Engineer — Filip Makraduli, Superlinked at 00:39
Problem: Sending scanned or image-heavy documents directly to a large model is expensive in tokens, fills the context window with raw data, and leaks private or regulated information to third-party APIs. Open-source models are also often adapted poorly to niche tasks without fine-tuning.
For: Individuals and institutions with sensitive documents in an Obsidian vault (or similar) who use agents like Claude Code but cannot expose their files to external APIs.
Products from this video
Examples
- A scanned NDA with phone number and social security number is OCR'd and redacted into a masked Markdown document; the agent sees only masked entities (email, person) — 10 spans redacted in the demo.
Other takes on AI security infrastructure
- Agentic consent governance layer for autonomous AI systems
- Enterprise agentic last-mile identity and access control layer
- Identity-based security infrastructure for multi-agent AI systems
- AI-agent security and control infrastructure for enterprises
- A guardrail tool that enforces strict test-driven development and other engineering rules for AI coding agents
- Agentic fuzz-testing of sandbox platforms: point a coding agent at your own hosted sandbox environment and let it try to break it