What is SIL? A research lab building the Semantic Operating System—infrastructure where AI representations are explicit, transformations are traceable, and reasoning can be inspected and composed with human judgment.
4 production systems · 12 research papers · 3K+ active users · 100% open source
The Problem We're Solving
AI systems today are powerful but structurally incomplete. They produce impressive results, yet their internal reasoning remains opaque, fragile, and fundamentally uninspectable.
If AI today is wood—useful but structurally unreliable—then SIL is building steel: the structural materials, building codes, and inspection protocols for production-grade intelligent systems.
What We Build
SIL develops semantic infrastructure—a substrate where:
- Representations are explicit — meaning is structured, not guessed
- Transformations are traceable — every step has provenance
- Reasoning is inspectable — you can see how conclusions were reached
- Composition works — systems combine correctly across domains
This is the Semantic Operating System: persistent memory, unified representations, deterministic engines, multi-agent orchestration, and interfaces where every cognitive layer remains visible.
Working Systems
These aren't demos. They're working infrastructure proving the architecture:
🌟 Featured: Reveal — Progressive Code Exploration
The entry point to semantic infrastructure. In production. Proven at scale.
pip install reveal-cli · v0.24.0 · 3K+ downloads/month · 100% organic growth
Structure-first code exploration with 25x token reduction. See file structure before reading content. Extract specific functions without loading entire files. Used by developers and AI agents worldwide.
# Try it now - takes 30 seconds
pip install reveal-cli
reveal src/ # Directory structure
reveal app.py # File structure (functions, classes)
reveal app.py main # Extract specific function
Install Guide · GitHub · Read the Paper
Morphogen — Cross-Domain Computation
v0.11 · 1,600+ tests · 85% coverage
Unified computational substrate spanning 40+ domains. MLIR-based deterministic execution with cryptographic provenance.
GenesisGraph — Verifiable Provenance
v0.3.0 · Cryptographic audit trails
Every transformation produces a provenance record. Selective disclosure lets you verify without revealing everything.
TiaCAD — Declarative Parametric CAD
v3.1.2 · 1,027 tests · 92% coverage
Parametric CAD in YAML. Semantic constraints, not just geometry. Proof that semantic infrastructure works for physical design.
Current Research
- Progressive Disclosure for AI Agents — Why structure-first exploration reduces tokens 25x
- RAG as Semantic Manifold Transport — Rethinking retrieval as geometric transport
- Agent Help Standard — Strategic guidance for AI agents using CLI tools
The Lab
SIL is the research division of the Semantic Infrastructure Foundation, a 501(c)(3) nonprofit (forming). We build in the open, publish our work, and invite collaboration.
Current team:
- Scott Senkeresty — Founder & Chief Architect
- TIA — Chief Semantic Agent (transparent AI collaboration)
Philosophy:
- Glass-box transparency — all work visible and traceable
- Structure before heuristics — explicit meaning, not statistical inference
- Stewardship over extraction — public infrastructure, not proprietary capture
Get Involved
- Use the tools:
pip install reveal-cliand try progressive disclosure - Read the research: Essays and Research Papers
- Explore the architecture: Foundations
- Support the mission: Semantic Infrastructure Foundation
- Collaborate: Contact us or visit GitHub
"Make meaning explicit. Make reasoning traceable. Build structures that last."
— Scott Senkeresty, Founder