Productionize AI MVP

The Problem with Prototypes

Your demo blew investors away, but users are complaining about timeouts, weird formatting, and system crashes. You built an AI feature, not an AI product.

Symptoms You'll Recognise

• Your app relies on a single massive Python script or a tangled mess of API calls.
• When OpenAI goes down or gets slow, your whole application crashes.
• You have no visibility into what users are actually asking the model or how it's responding.

Why It Happens

Jupyter notebooks and quick LangChain prototypes are incredible for validating ideas. However, they lack the error-handling boundaries, queueing systems, and monitoring required to serve concurrent users reliably.

How We Fix It

1. Architecture Overhaul: We extract the AI logic into isolated, independently scalable services (REST or gRPC).
2. Resilience Layers: We implement exponential backoffs, circuit breakers, and fallback models so your users never see a stack trace.
3. Observability Setup: We integrate tracing tools (like Langfuse or Datadog) to capture telemetry on every LLM call.
4. Prompt Versioning: Moving prompts out of code and into proper versioned registries to enable safe continuous deployment.

Proof

Took an AI-driven marketing platform from 30 simultaneous users (crashing) to supporting 5,000 concurrent sessions seamlessly with 99.9% uptime.