SYSTEMS ENGINEERING FOR AI

Engineering Certainty in a Probabilistic World.

We apply rigorous Systems Engineering to AI development, ensuring Safety by Design and total Interpretability. We translate high-dimensional vector space into actionable, auditable logic.

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01. THE METHODOLOGY

The Deterministic AI Framework

Safety cannot be an afterthought.

Traditional AI development often rushes from data to training, treating safety as a patch applied at the end. We invert this process. By defining the Operational Design Domain (ODD) and safety constraints *before* a single line of Python is written, we ensure your model is verifiable by design and transparent in its decision-making.

The V-Model Adaptation:

P1

Concept of Operations (ConOps) & ODD

The Descent: Constraints

We rigorously define "Where is it allowed to operate?" We map the Operational Design Domain (ODD) to establish the exact environmental and logical boundaries of the system.

P2

System Safety Architecture

The Descent: Decomposition

Utilizing STPA (System-Theoretic Process Analysis) to identify hazardous states. We design "Safety Wrappers" to override unsafe probabilistic predictions.

P3

Constrained Training & Verification

The Vertex

Implementation using custom loss functions that penalize not just inaccuracy, but safety violations. We prioritize 'Safe Failures' over high-risk guesses.

P4

Interpretability Validation (XAI Layer)

The Ascent: Evidence

The "White Box" Audit. We verify that the model is looking at the *correct* features to make decisions, not relying on spurious correlations (SHAP, LIME).

P5

Robustness & Adversarial Testing

The Ascent: Integration

We stress-test against adversarial attacks and edge cases. We test to find exactly where the system breaks under duress.

P6

System Validation & Certification

The Ascent: Proof

Final verification against ISO standards (42001, 26262). We deliver a complete "Safety Case" document proving all requirements are met.

02. CAPABILITIES

Core Safety Services

Safety-Critical Architecture

We design and implement the surrounding deterministic infrastructure that governs your ML model, including runtime monitors and fail-safe mechanisms for graceful degradation.

STPA Hazard Analysis Deterministic Wrappers OOD Detection

Explainability & Forensics

We provide auditable trails linking predictions back to training data provenance and feature importance, satisfying internal and regulatory scrutiny.

SHAP / LIME Analysis Causal Feature Mapping Auditable Trail Generation

Regulatory Compliance Prep

We conduct gap analyses and prepare your technical documentation, risk management systems, and post-market monitoring plans to ensure certification readiness.

ISO 42001 / 26262 EU AI Act Readiness
03. AUDIT & ASSURANCE

If you can't explain it, you can't trust it.

In safety-critical engineering, "It just works" is not an acceptable answer. Our delivery includes a bespoke Explainability Interface, allowing your engineers and auditors to query specific decisions.

We translate high-dimensional vector space into actionable logic. Our pipelines ensure every prediction comes with an audit trail, mapping outputs back to training data provenance.

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Feature Importance Heatmap

Build with Certainty.

Schedule an initial consultation with our Principal Safety Engineers to discuss your specific use case and regulatory requirements.

Schedule a System Audit