About Bloodhound
Eliminating the $400 billion annual cost of software vulnerabilities—one codebase at a time.
Our Mission
Save businesses millions by finding the vulnerabilities others miss.
Traditional security tools catch only 42% of production vulnerabilities. We catch 94.3%. That 52% gap represents the difference between a secure business and a catastrophic breach.
Founded in 2024, we’re a research lab disguised as a security company. Our home-grown neural network architecture—trained on 2.8 million real-world vulnerabilities—detects patterns that rule-based scanners fundamentally cannot see.
The result? Our clients have saved $1.7M+ in prevented breaches, infrastructure waste, and lost productivity. We don’t just find bugs. We prevent financial disasters.
Impact to Date:
$1.7M+
Client Savings Secured
5+
Enterprise Clients
94.3%
Detection Accuracy
2.8M
Training Samples
Home-Grown Neural Network Architecture
Vulnerability Severity Scoring
Svuln(x) = σ(∑i=1n wi·fi(x) + b)
Expanded form:
Svuln = 1/(1+e-(α·E+β·I+γ·C+δ·T))
where:
E = ∫01 p(exploit|vuln) dx
I = log2(impact$) / max(log2(I))
C = McCabe(G) / (V(G) + 1)
T = temporal decay factor
α=0.45, β=0.35, γ=0.15, δ=0.05
Sigmoid activation with cyclomatic complexity normalization and logarithmic impact scaling ensures critical vulnerabilities rise to top priority.
Cross-Entropy Loss Function
ℒ(θ) = -1/N ∑i=1N ∑c=1C yic·log(ŷic)
Gradient descent update:
θt+1 = θt - η·∇θℒ(θt)
With Adam optimizer:
mt = β1mt-1 + (1-β1)gt
vt = β2vt-1 + (1-β2)gt2
θt = θt-1 - η·m̂t/(√v̂t+ε)
η=0.001, β1=0.9, β2=0.999
Multi-class cross-entropy with adaptive moment estimation achieves optimal convergence in 27-dimensional vulnerability space.
Pattern Detection Complexity
T(n,m,k) = O(n·log(n) + m·k·d)
Space complexity:
S(n) = O(n + V·E)
AST parsing:
Tparse = Θ(n) average case
Graph traversal:
Tgraph = O(V + E·log(V))
where:
n = LOC, m = deps, k = patterns
d = max depth, V = nodes, E = edges
Logarithmic scaling enables real-time analysis of enterprise codebases without sacrificing detection accuracy.
Information-Theoretic Confidence
H(Y|X) = -∑x p(x) ∑y p(y|x)·log p(y|x)
Mutual information:
I(X;Y) = H(Y) - H(Y|X)
Bayesian confidence:
P(vuln|code) = P(code|vuln)·P(vuln)/P(code)
KL divergence:
DKL(P||Q) = ∑x P(x)·log(P(x)/Q(x))
Threshold: DKL > 0.85
Entropy-based confidence scoring and KL divergence thresholding achieve industry-leading 0.7% false positive rate.
3-Layer Feed-Forward Architecture
Activation Functions
ReLU (Hidden Layers):
f(z) = max(0, z)
Derivative:
f'(z) = {z > 0 ? 1 : 0}
Softmax (Output Layer):
σ(z)ⱼ = ezⱼ / ∑kezk
Properties:
∑jσ(z)ⱼ = 1, σ(z)ⱼ ∈ (0,1)
Backpropagation (Simplified)
Output gradient:
δ₃ = ŷ - y
Hidden layer 2 gradient:
δ₂ = (W₃Tδ₃) ⊙ ReLU'(z₂)
Hidden layer 1 gradient:
δ₁ = (W₂Tδ₂) ⊙ ReLU'(z₁)
Weight updates:
∇W₃ = δ₃h₂T, ∇W₂ = δ₂h₁T, ∇W₁ = δ₁xT
Training: 2.8M samples · 150 epochs · Batch size: 256
Performance: 94.3% accuracy · 0.7% FPR · 97.2% recall
Our Journey & Vision
Where We’ve Been
Q1 2024 - Founded
Research phase: built neural network prototype
Q2 2024 - Beta Launch
Launched CLI tool with first 3 enterprise clients
Q3 2024 - Milestone
Achieved $1M in client savings; 94.3% accuracy
Q4 2024 - Scale
Expanded to 5+ clients; launched Code for Change
Where We’re Going
2025 - Enterprise Expansion
Scale to 50+ enterprise clients across North America
2025 - Real-Time Monitoring
Launch continuous vulnerability monitoring platform
2026 - Advanced Networks
Deploy next-gen transformer architecture for code analysis
2027 - Industry Leader
Become North America’s #1 neural network security platform
Research-Grade Analysis from Your Terminal
Our command-line interface runs the same neural network architecture used in production, delivering research-level vulnerability detection with confidence scoring and mathematical precision on macOS, Linux, and Windows.
macOS
Linux
Windows
Advanced Theoretical Foundations
Statistical Learning Theory
VC-dimension generalization bound:
P[suph∈H|R(h)-R̂(h)| > ε] ≤ 4mH(2m)e-ε²m/8
Rademacher complexity:
R̂m(H) = Eσ[suph∈H 1/m ∑i=1mσih(xi)]
PAC-Bayesian bound:
KL(ρ||π) + ln((2√m)/δ) / λ ≥ R(ρ)
Enables provable guarantees on generalization error with sample complexity O(d/ε²)
Reproducing Kernel Hilbert Space
Kernel trick in feature space:
K(x,x') = ⟨φ(x),φ(x')⟩H
Representer theorem:
f*(x) = ∑i=1n αiK(xi,x)
Mercer's condition:
K(x,y) = ∑i=1∞ λiφi(x)φi(y), λi≥0
Infinite-dimensional feature spaces with finite computational cost
Information Geometry
Fisher information metric:
gij(θ) = E[∂ilog p(x|θ) · ∂jlog p(x|θ)]
Natural gradient descent:
θt+1 = θt - ηG-1(θt)∇θL(θt)
Wasserstein-2 distance:
W2(μ,ν)² = infγ∈Γ(μ,ν)∫||x-y||²dγ(x,y)
Riemannian geometry on probability manifolds for optimal transport
Stochastic Optimization
Langevin dynamics (SDE):
dθt = -∇U(θt)dt + √(2β-1)dWt
Fokker-Planck equation:
∂tp(θ,t) = ∇·(∇U(θ)p(θ,t)) + β-1Δp(θ,t)
Polyak-Łojasiewicz (PL) inequality:
||∇f(θ)||² ≥ 2μ(f(θ)-f*), ∀θ ∈ ℝd
Convergence to global minimum with linear rate under PL condition
Research Impact
These theoretical frameworks—from VC theory to stochastic differential equations—form the mathematical foundation of our neural network architecture. By leveraging Riemannian optimization on statistical manifolds and RKHS-based kernel methods, we achieve provably optimal convergence rates while maintaining computational efficiency for real-time vulnerability detection.
Our Team
Samuel Fasakin
Co-Founder & CEO
Security expert with 15+ years in enterprise software development
Michael Afamefuna
Co-Founder & CTO
Former lead security architect, specializing in cloud security and AI
Kevin Chen
Senior Cloud Architecture Engineer
Cloud infrastructure expert, certified AWS and Azure architect
Our Values
Innovation
Pushing boundaries in security technology
Integrity
Transparent and honest in all our dealings
Excellence
Delivering exceptional results every time
Collaboration
Working together for better security
Integrations and Languages
Seamlessly integrate with your development workflow and support for all major programming languages