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Not Generative Hype
Are we Generative AI?
Nope - and thatâs by design.
What is this Deterministic AI anyway?
Generative
AI
Generative AI, by contrast, employs probabilistic models (e.g., transformers, diffusion networks) to generate content or predictions based on likelihood, rather than certainty.
Examples:
ChatGPT for text generation.
Stable Diffusion for image synthesis.
Deterministic
AI
Deterministic AI, relies on predefined rules, such as decision trees, knowledge graphs, and cloud-specific configurations, to apply exact and explainable changes.
Examples:
Email spam filters using "if-then" logicâ¨
Dijkstraâs algorithm for GPS navigation
Aspect
Repeatability
Accuracy
Uncertainty Handling
Deterministic AI
Always produces identical results
High precision in controlled environments
Struggles with noisy data
Generative AI
Outputs vary due to stochasticity
Prone to hallucinations or factual errors
Adapts to ambiguity via probability
Not Generative. Not Guesswork.
Just Accuracy.
You donât want poetic code suggestions. You want fixes that work, backed by logic, not language models. Deterministic AI delivers precise, contextual, policy-aligned infrastructure changes you can trust in production. While deterministic AI can struggle with noisy inputs, Infrastructure as Code is structured and consistent, making it the perfect domain for high-precision automation.
Built for the Engineerâs Workflow DevOps and cloud engineers need:
Consistency every time
Same input = same output. No surprises.
Deployment-ready, not draft-quality
No hallucinations. Just mergeable code.
No extra QA burden
Precise, standards-aligned fixes from the start.
Explainable fixes
Every change is documented, contextual, and traceable.â¨
Security Comes Standard
Youâre not a security team, but you still get:
Alignment with CIS, NIST, SOC 2, and custom org policies
Guardrails that map directly to your IaC environment, and adapt with how you architect it
Defensible remediations that pass audits without rewriting code
Misconfiguration fixes that wonât break production
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Why Deterministic, Not Generative?
Generative AI is great for brainstorming.
Infrastructure isnât a brainstorming exercise.
Gartner cites the need for organizations to educate themselves about the different AI models and choose the right ones for the tasks at hand.
AI Techniques Heat Map
AI Technique suitability
Common AI Techniques
Use Case Families
Prediction / Forecasting
Planning
Decision Intelligence
Autonomous Systems
Segmentation /
Classification
Recommendation
Systems
Perception
Intelligent Automation
Anomaly Detection /
Monitoring
Content Generation
Conversational User
Interfaces
Intelligent Automation
Generative models
LOW
LOW
LOW
LOW
MEDIUM
MEDIUM
MEDIUM
MEDIUM
MEDIUM
HIGH
HIGH
HIGH
Non-generativeâ¨machine learning
HIGH
LOW
MEDIUM
MEDIUM
HIGH
HIGH
HIGH
HIGH
HIGH
LOW
HIGH
MEDIUM
Optimization
LOW
HIGH
HIGH
HIGH
LOW
MEDIUM
LOW
LOW
LOW
LOW
LOW
LOW
Simulation
HIGH
MEDIUM
HIGH
MEDIUM
LOW
LOW
LOW
LOW
MEDIUM
HIGH
LOW
LOW
Rules / heuristics
MEDIUM
MEDIUM
HIGH
MEDIUM
HIGH
MEDIUM
LOW
HIGH
MEDIUM
LOW
MEDIUM
MEDIUM
Graphs
LOW
HIGH
MEDIUM
LOW
HIGH
HIGH
LOW
MEDIUM
HIGH
LOW
HIGH
HIGH
Use-Case Families and Relative Generative Modelsâ Usefulness
Use Case Families
Generative Models' Current Usefulness
Use-Case Examples
Prediction/Forecasting
Planning
Decision Intelligence
Autonomous Systems
Segmentation/Classification
Recommendation Systems
Perception
Intelligent Automation
Anomaly Detection/Monitoring
Content Generation
Conversational User Interfaces
Knowledge Discovery
LOW
LOW
LOW
LOW
MEDIUM
MEDIUM
MEDIUM
MEDIUM
MEDIUM
HIGH
HIGH
HIGH
Risk prediction, customer churn prediction, sales/demand forecasting
Operation research, optimization, route planning
Decision support, augmentation, automation
Self-driving cars, advanced robotics, drones
Clustering, customer segmentation, object classification
Recommendation engine, personalized advice, next best action
Object detection, recognition, analysis
Intelligent document processing, object character recognition, robotic process automation, hyperautomation
Abnormal transaction detection, outlier detection, monitoring
Text generation, image and video generation, synthetic data
Virtual assistant, chatbot, digital worker
Knowledge store, search, mining
Aspect
Fix Consistency
Accuracy
Maintainability
Governance
Trust
Generative AI
Varies with Each Prompt
Probabilistic guess
Opaque, unexplained code
Difficult to enforce
Review and rewrite before deploy
Deterministic AI
Same output every time
Factual, doc-driven changes
Contextual, engineer-readable
Policy-aligned by design
âValidate and shipâ
Built Like Air Traffic Control,
Not Like ChatGPT
Deterministic AI isnât trained on Reddit threads or public IaC forums. Itâs trained on cloud provider documentation, infrastructure best practices, and your actual environment.
Just like air traffic control systems donât guess at flight paths, it doesnât guess at fixes. It acts based on rules, not noise
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Like aircraft collision systems,
we use decision trees and knowledge graphs to take the right action,
not a âlikelyâ one
How Deterministic AI Works
A deterministic engine doesnât guess. It applies logic deterministically every time. Hereâs how
Continuously builds and updates a cloud knowledge graph
This evolving graph encodes every cloud serviceâs configuration options, capabilities, and architectural constraints across AWS, Azure, and GCP. It understands how services can and should interact, providing the foundation for safe, standards-aligned decisions at scale.
Applies a policy engine that enforces architectural constraints
Gomboc interprets high-level policy rules, whether based on CIS, NIST, SOC 2, or your own internal standards, and maps them to specific infrastructure requirements. While currently focused on security and compliance, the system is built to scale across performance, cost, and resilience guardrails as well.
Moves between code and model to understandâ¨the full context
It doesnât just scan static code. It maps your infrastructure-as-code into an internal model and back, allowing it to reason across modules, inheritance, and deeply nested resources. This ensures that every fix is context-aware, accurate, and deployable from the start.
This is how we generate no-excuse fixes. Not predictions. Not templates.
But verifiable, policy-driven changes rooted in technical truth. Deterministic by design, so you get the same correct output every time.
The Gomboc Difference
âWhat happens when you want to change AI-generated code? You didnât write it. Good luck maintaining it.â
With GOMBOC
Built for
Engineering Velocity
Security may cheer us on. â¨But youâre the one merging â¨the pull request.
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