Constraint-Aware AI Engineering™
A framework for honest, deployable artificial intelligence
The Fast·Cheap·Good selector is a reminder that engineering is not about promises, but about choices. Every system operates under real constraints: latency, cost, accuracy, reliability. Pretending otherwise creates fragile products and broken trust. This framework makes tradeoffs explicit, not hidden; deliberate, not accidental. In environments where failure is expensive, hidden tradeoffs create operational, legal, and reputational risk. That is why this framework encodes an ethical stance: honesty over hype, clarity over illusion.
The industry has normalized claims that collapse under production conditions. We reject this. Our systems are designed from first principles with explicit constraint awareness. When we optimize for speed and quality, we are transparent about costs. When we optimize for cost and quality, we are honest about latency. When we optimize for speed and cost, we quantify exactly how quality is bounded.
What We Refuse to Do
- ✕ Promise all three dimensions simultaneously
- ✕ Hide uncertainty behind single-point metrics
- ✕ Deploy systems without documented failure modes
- ✕ Optimize benchmarks at the expense of production reliability
Core Principles
- Explicit Assumptions: Every model ships with documented assumptions, limitations, and failure modes
- Uncertainty Quantification: Predictions include calibrated confidence intervals, not just point estimates
- Traceable Decisions: Every design choice is logged, versioned, and auditable
- Human Accountability: Clear ownership chains for every automated decision
- Constraint Transparency: Tradeoffs are visible in the UI, API, and documentation
This manifesto is operational, not philosophical. The Tradeoff Selector™ embedded in our deployment interfaces makes constraint awareness a daily practice. Clients, researchers, and engineers engage with the same framework, ensuring alignment from scoping through production.
Systems that acknowledge their limitations are systems that can be trusted. Trust, not accuracy scores on curated benchmarks, determines whether AI creates value in production and safety-critical contexts.
The Tradeoff Selector™
Interactive constraint visualization for AI system design
Most AI failures are not model failures. They are expectation failures. The Tradeoff Selector™ surfaces those expectations before they become incidents. This prevents misalignment between business requirements and system behavior.
Fast + Cheap
Good is constrained. Quick, affordable systems sacrifice accuracy and reliability guarantees. Stakeholders understand quality bounds before deployment.
Fast + Good
Cheap is constrained. High-quality, low-latency systems require premium infrastructure and expertise. Budget expectations are set explicitly.
Cheap + Good
Fast is constrained. Cost-effective, reliable systems trade speed for thoroughness. Timeline commitments reflect actual processing requirements.
Constraint-Aware AI Engineering™ Framework
Tradeoff Selector™
Interactive UI component for explicit constraint visualization in model deployment, RAG configuration, and inference mode selection.
Constraint-Aware Audit Protocol™
Systematic methodology for AI safety audits incorporating tradeoff documentation into compliance and governance frameworks.
Honest AI Certification™
Third-party verification program certifying that AI systems meet Constraint-Aware Engineering standards.
Research Publication Series
"Engineering the Triangle" and "Why Trustworthy AI Starts with Constraints" are foundational thought leadership publications.
We don't claim to break the triangle.
We design inside it. Deliberately.
"If someone promises all three, they don't understand the system."
Discuss Your Constraints