Consulting · 01
Production AI Architecture Review
Move an AI system from working prototype to controlled production.
The problem
The prototype works, but reliability, evaluation, guardrails, observability, cost and architecture are not production-grade.
Usually triggered by
- An AI prototype needs to become production-ready
- Launch date
- Customer or security review
- Failing pilot
- Unpredictable LLM cost
- Quality complaints
Who it is for
CTO, technical founder or Head of Engineering with an AI/GenAI product moving to production.
Who it is not for
- Teams without a real AI product or a committed plan
- "AI transformation" initiatives with no engineering team
- Anyone looking for guaranteed accuracy or free advice
What is covered
- LLM architecture
- Model selection
- RAG
- Retrieval quality
- Agents
- Tool use
- Permissions
- Retries
- Fallback behaviour
- Safe termination
- Human approval points
- Hallucination handling
- Evaluation
- Guardrails
- Observability
- Latency
- Token/model cost
- Caching
- Data flow
- Security architecture
- Production readiness
What you get
- Findings
- Production-readiness gaps
- Target architecture
- Evaluation and guardrail approach
- Cost model
- Roadmap
- Review session
What success looks like. A defensible, sequenced path from prototype to production.
The review helps you reduce, measure and control failure modes. It does not promise to eliminate hallucinations or guarantee accuracy.
How it works
- Discovery
- Architecture review
- Technical deep dive
- Risk analysis
- Recommendations
- Roadmap
- Review session
What we'll need from you
- System description and diagram
- Models and providers
- Prompts
- Data sources
- Current evaluation approach and results
- Sample failures
- Tool-call and agent behaviour
- Usage and cost data
- Launch requirements
Not included unless separately agreed
- Building the full AI product
- Training a foundation model
- Large-scale fine-tuning projects
- Manually creating large evaluation datasets
- Full data engineering implementation
- Writing the complete RAG platform
- Building all agent tools
- 24x7 production support
- Legal/compliance certification
- Guaranteed accuracy claims
- Guaranteed hallucination elimination
Recommendations are prioritised by business impact, reliability, security, cost, maintainability and implementation effort. InfraTales will not recommend additional complexity unless there is a clear reason for it. Example code or configuration may be included to explain a recommendation, but an engagement must not silently turn into an implementation project.
Rahul works with the engineering team as a senior architecture partner. He does not replace the engineering team.
After this
Implementation guidance, or Fractional Principal Cloud & AI Architect.
Who leads the work
- Focus
- Principal Cloud & AI Platform Architect focused on production AWS systems, event-driven architecture, serverless, DevOps, platform engineering and production AI.
- Experience
- Rahul Ladumor has 9+ years of experience building production software, cloud, platform and AI systems.
- Certifications
- Rahul holds AWS Professional certifications in Solutions Architecture, DevOps Engineering and Generative AI Development.
- Community
- Rahul is a 5x AWS Community Builder in the Serverless category.
- Consulting
- Rahul has worked as an independent Cloud & AI consultant since March 2023.
Related work
- Enterprise RAG on Bedrock across 2M+ documents · Enterprise client (NDA)