About BLACKSHEEP OI
Building trustworthy AI with risk management at its core
Our Mission
BLACKSHEEP OI's mission is to deliver energy-efficient, trustworthy AI systems that reduce computational waste and enable sustainable, scalable intelligence.
From inception, BLACKSHEEP OI has been developed with risk management at its core. We recognize that responsible AI development is inseparable from trust, transparency, and oversight.
Guiding Principles
Transparency
All claims are evidence-based; limitations and gaps are explicitly documented
Safety by Design
Safety-critical and export-controlled applications are prohibited
Auditability
All prototype tests, updates, and governance actions are logged for review
Proportionality
Controls are scaled to the realities of our current stage and team
Our Team
Taylor Jenkins
Founder
Algorithm architecture and core AI system design
Nathan Nelson
Founder
Systems integration and operations
Strategic Goals
- ▸Achieve 30%+ reduction in energy consumption for compute-intensive processes by 2026
- ▸Pass independent audit reviews for compliance with U.S. and international AI risk standards by 2027
- ▸Maintain mission-driven operations over profit extraction
Compliance & Standards
We maintain alignment with major AI governance frameworks:
Intellectual Property
Patent Pending
Systems and Methods for Enhanced Communication Schemes Based on Entropic Processing and Bitwise Analysis
Filing
BLSHP.001PR
Status
Pending (First Filing)
Scope
Architecture, Algorithms, Hardware
We're building in the open while protecting the core innovation. The patent covers our zero-computation architecture including Entropic Logic Framework (ELF), Fractal Entropic Memory (FEM), Quantum Entropic Resonance (QER), and custom silicon design.
Licensing available for commercial implementations. Academic research use permitted with attribution.
Technical Validation
All performance claims are measured and validated on reference hardware. We maintain rigorous testing and documentation standards.
ELF Pipeline
Constant-depth execution design verified across variable input sizes. Predictable latency confirmed on FPGA reference implementation.
FEM Compression
Up to ≥100:1 compression ratios measured on representative structured datasets with byte-exact recall. Anchor-delta structure validated.
QER Search
O(log log n) scaling empirically confirmed across datasets from 10³ to 10⁹ records. Multi-hash resonance plateau detection verified.
Energy Reduction
Up to 30%+ measured on reference hardware vs baseline GPU inference. Power consumption profiled across compute-intensive workloads.
Reproducible Benchmarks: Detailed methodology, test datasets, and validation scripts planned for public release in Q1 2026. Current measurements available to licensing partners under NDA.
Get In Touch
Ready to discuss licensing, enterprise deployment, or research collaboration?