AI Architecture Review
Find out whether your AI system is actually production-ready — reviewed against the same ten pillars I hold every system to, before you commit further budget or go live.
Delivery: Remote, worldwide
The problem
AI prototypes get built fast, often without anyone stepping back to ask whether the architecture will hold under real production load, real security pressure, or real governance scrutiny. By the time gaps in scalability, security or cost surface, they're expensive to fix. An architecture review finds them while they're still cheap to fix.
Who this is for
Founders and CTOs about to take an AI system from pilot to production
Engineering leaders inheriting an AI system built by a previous team or vendor
Companies running technical due diligence before an investment or acquisition involving an AI product
Teams that suspect architecture or security debt but have no independent way to confirm it
Approach — the ten pillars
The same methodology behind every review and case study on this site — see it in full on the Production AI section of the homepage.
Scalability
Horizontal scale, stateless services, sharding, load-aware autoscaling.
Security
Zero-trust, RBAC/ABAC, secrets management, least privilege, defense in depth.
AI Security / Guardrails
Prompt-injection & jailbreak defense, tool sandboxing, human-in-the-loop.
Reliability & Resilience
Fallbacks, retries, circuit breakers, graceful degradation, DR.
Observability
Traces, metrics, logs, evals and cost in one pane.
Governance
EU AI Act / ISO 42001 / NIST AI RMF, audit trails, model & data lineage.
Cost Optimization
Routing by cost, caching, right-sized models, budget guardrails.
Data & RAG Security
Permission-aware retrieval, poisoning detection, citation verification.
Performance
Latency budgets, streaming, batching, semantic caching, profiling.
Maintainability / DevSecOps
IaC, CI/CD, SBOM & supply-chain scanning, typed contracts, tests.
Technical scope
Deliverables
A structured findings report scored against all ten pillars
Concrete, prioritized recommendations — not a generic checklist
A clear go / no-go read on production readiness
A walkthrough call to discuss findings with your team
Evidence
Related case studies from this kind of work:
FAQ
What do you actually review?
Architecture diagrams and data flow, the model/inference layer, RAG or retrieval pipeline, agent orchestration and tool permissions, cloud infrastructure, observability setup, cost model and governance posture — scored against ten pillars.
Do I need to give you production access?
No. Most reviews work from architecture documentation, diagrams and a working session with your team. Production access is only used if agreed and scoped in advance.
What if you find nothing wrong?
Then you have independent confirmation your architecture holds up — valuable in itself, especially ahead of an investment, audit or governance review.
Is this the same as a security assessment?
Security is one of the ten pillars reviewed here. For a dedicated, adversarial security test of LLMs, agents and MCP, see the AI Security Assessment instead.
Have an AI system you're about to take to production?
A structured, ten-pillar review before you commit further budget or go live.