Northbeam Solutions builds deterministic safety certification for autonomous systems. One mathematical kernel — multi-channel constraint evaluation, binding-constraint decision logic, and cryptographic certificate hashing — instantiated across satellite constellations, financial risk, and agentic AI. 145 patent claims. 3 U.S. provisionals filed. Research published.
Every Northbeam solution starts from the same premise: real-world autonomous systems demand mathematical rigor, not probabilistic guesses. We transform intractable problems into solvable ones using concrete mathematical techniques — then wrap them in production-grade engines with formal guarantees.
Brute-force approaches hit scaling walls. We apply topology abstraction and equivalence class decomposition to collapse O(N²) problems into O(|S|) — making the previously impossible, routine.
Our systems produce bit-identical results across runs. Every output ships with formal robustness bounds — not confidence intervals. Compliance becomes a computed artifact, not a legal argument.
We harness AI where it excels — classification, pattern recognition, optimization heuristics — but anchor every decision to deterministic constraint solvers with cryptographic audit trails. Provable, not probable.
AI excels at finding patterns in complexity — surfacing signals humans miss, classifying data at scale, optimizing across vast solution spaces. But in high-stakes domains, probabilistic outputs alone create a dangerous gap: decisions that look right but can't prove they are. When a regulator asks for evidence, when a failed allocation costs millions, when an audit demands a chain of reasoning — confidence scores aren't enough.
Our approach fuses AI's efficiency with mathematical certainty. We use AI to navigate the search space — then lock every decision into a deterministic constraint solver that produces formal proofs, robustness bounds, and cryptographically signed outputs. The result: systems that are both intelligent and auditable. Fast and provable. Adaptive and repeatable.
Pattern recognition, anomaly detection, search-space exploration, classification of complex state, natural language interfaces
Constraint satisfaction, formal verification, robustness certification, audit-ready evidence, bit-identical reproducibility, regulatory compliance
Identify an intractable real-world problem. Decompose it into equivalence classes. Build a deterministic solver at the class level. Wrap it in production-grade code with formal verification. File the patent. Ship the engine.
Three live domain instantiations of a single deterministic safety certification kernel. Production codebases, patent filings, benchmarks. Built for acquisition or licensing.
82ms solve for 6,000+ satellites. Topology abstraction reduces O(N²) to O(|S|). 0.6ms failure recovery. Ed25519 command auth. Patent-pending.
View Details →6-channel parallel constraint evaluation for portfolio trades. Smooth barrier functions with regime detection. Patent-pending.
View Details →Pre-execution safety certification for autonomous AI agents. Multi-channel constraint evaluation with hash-chained SHA-256 audit trails.
View Details →A domain-agnostic safety certification platform. The same mathematical kernel — multi-channel constraint evaluation, binding-constraint decision logic, deterministic certificate hashing — instantiated across three domains.
Every autonomous system faces the same structural problem: proposed actions must be evaluated against multiple constraints, classified into safety zones, and certified with tamper-evident audit trails — all in real time. QAE Substrate solves this once.
N constraint channels run in parallel — each returning a normalized margin in [0,1]. The binding constraint (minimum margin) drives all zone classification and decision logic. One architecture. Any constraint domain.
Regime change override → Blocked → Escalate to Human → Certified with Warning → Certified. The same decision tree governs constellation allocations, portfolio trades, and AI tool calls. No probabilistic fallback.
Every certification produces a tamper-evident SHA-256 hash over a canonical representation of the action, constraints, margins, and decision. Bit-identical across runs, platforms, and versions.
Each domain plugs into the kernel via a polymorphic adapter — providing constraint channels, action-to-state mapping, and regime detection. The kernel never touches domain-specific types. New domains require zero kernel changes.
Three U.S. provisional patents filed covering the full platform architecture — from topology abstraction through multi-channel safety certification, deterministic hashing, agentic AI safety, and topological constraint of neural network representations. All claims implemented in production Rust with 1,300+ validated tests across 15 crates. Additional provisional filings in preparation covering representational containment certification.
Platform thesis: deterministic safety certification is a horizontal capability. The domain adapter pattern means each new vertical is an integration project, not a rebuild.
BSL-1.1 for the safety kernel and agentic adapter — converts to Apache 2.0 on January 1, 2032. Finance adapter is proprietary. Kernel and agentic packages published to crates.io and PyPI.
Our patent filings are backed by published research. The theoretical foundations powering the QAE kernel are available for independent review.
William S. Tennant
Demonstrates that hard structural boundaries on neural network representational space increase Fisher information density by 104%. Provides the theoretical foundation for representational containment certification — the next domain adapter for the QAE safety kernel.
Technical founder with deep expertise across cloud data infrastructure, IP strategy, and venture capital. Built and shipped production systems spanning Snowflake, AWS, financial risk, and autonomous safety certification.
Northbeam Solutions is a solo-founder holding company structured for maximum IP leverage: a single mathematical kernel instantiated across three defensible verticals, with 145 patent claims filed and published research providing the theoretical foundations.
Deep-dive access requires a mutual NDA. Includes claim mapping, architecture documentation, live benchmarks, and full codebase review.