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Turn AI Prototypes into Reliable Production Systems

I help software teams integrate AI features into real products with proper backend architecture, fallbacks, monitoring, cost control and deployment strategy.

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In Short

AI production integration turns a working AI prototype into a reliable production system — adding backend architecture, RAG and vector search, fallbacks, cost and rate-limit control, monitoring and a real deployment strategy so the feature is dependable for real users.

This Is For You If

01You have an AI prototype but it is not production-ready.
02AI responses are unstable or hard to monitor.
03Costs and rate limits are unclear.
04The product needs RAG, vector search or AI workflow integration.
05The team needs backend architecture around AI features.
06The AI feature must be reliable enough for real users.

What Is Included

  • 01AI architecture review
  • 02OpenAI / Claude / AI SDK integration
  • 03RAG and vector database design
  • 04Prompt and workflow structure
  • 05Fallback and retry strategy
  • 06Cost and rate-limit control
  • 07Monitoring and logging
  • 08Production deployment plan

Related Work

FAQ

What does AI production integration involve?

It turns an AI prototype into a reliable production system: backend architecture around the AI feature, RAG and vector search, fallback and retry strategies, cost and rate-limit control, monitoring and logging, and a real deployment plan.

My AI prototype works in a demo but not in production — can you help?

Yes, that is exactly what this engagement is for. Most prototypes lack fallbacks, monitoring, cost control and solid backend architecture; this work adds the reliability layer real users need.

Which AI providers and tools do you work with?

OpenAI, Claude (Anthropic) and the Vercel AI SDK, with provider-agnostic fallback strategies so you are not locked into a single model.

Do you build RAG and vector search?

Yes. RAG and vector database design are part of the standard scope, along with prompt and workflow structure for stable, context-aware responses.

How do you keep AI costs and rate limits under control?

Through caching, request routing, fallback models, and explicit cost and rate-limit controls with monitoring — the same approach that cut AI costs by 40–45% on past projects.

How much does it cost?

Scoped per project after a short technical review — the exact scope and price are confirmed on a fit call.

Let's talk specifics.

Book a technical fit call. We will quickly establish scope, timeline and whether this is the right engagement for your situation.