Traditional Security Protects Infrastructure
SAST, DAST, CSPM, IAM controls, and hardening pipelines protect software and cloud systems.
AIStack.Design
Deterministic Infrastructure
for Probabilistic AI Systems
AI introduces entropy. AIStack enforces determinism.
Shift Left. Shield Right. Build AI systems that can be trusted.
AI is being deployed into production without enforceable governance. We build the infrastructure layer that validates, signs, monitors, and enforces AI policy across your development and deployment pipelines.
- Governance-first delivery
- Cost-aware design
- RAG and agent architecture with guardrails
The Missing Layer in AI Security
AI Introduces Probabilistic Drift
Non-determinism, artifact variability, and policy bypass create entropy across production workflows.
AIStack Adds Deterministic Enforcement
Signed artifacts, CI/CD constraint engines, and governance ledgers enforce accountable AI operations.
Free Tools
Start with structure. Upgrade later.
SPEC
SPEC - Production Blueprint
Generate a practical delivery blueprint your team can execute.
- Architecture outline
- Milestone sequence
- Delivery guardrails
COST
Token Cost Estimator
Forecast request and monthly spend before implementation.
- Input/output token model
- Per-request estimate
- Monthly volume planning
RISK
Prompt Risk Checker
Scan prompts for policy, PII, and injection-pattern risk.
- Risk pattern checks
- Severity indicator
- Hardening guidance
RAG
RAG Readiness Check
Validate retrieval and release readiness before production rollout.
- Checklist scoring
- Readiness status
- Release-gate guidance
Most teams don't fail on models. They fail on operations.
Controls
Audit trail, policy enforcement, and approval checkpoints from the start.
Costs
Token budgets, routing, caching, and evaluation loops to keep spend predictable.
Reliability
Observability, rollback paths, and guardrails for stable production behavior.
Services
AI Governance & Risk
Define controls, ownership, and policy checkpoints.
Deliverable: governance and risk operating model.
RAG & Retrieval Engineering
Design retrieval for quality, traceability, and change resilience.
Deliverable: production retrieval architecture.
MLOps & Monitoring
Build evaluation, drift checks, and incident-ready monitoring.
Deliverable: observability and reliability stack.
Data Integration & Evaluation
Connect data and define measurable answer quality standards.
Deliverable: evaluation-ready integration plan.
Resources
AI Cost & Governance Brief
Practical guidance for cost control and policy alignment.
RAG Readiness Checklist
Use the free checklist before launch.
From Prototype to Production
Common failure modes and how to prevent them in delivery.