Most AI proof-of-concepts never reach production. Here's the systematic framework we use to ship AI features that deliver measurable business value — reliably and repeatably.
The POC Graveyard Problem
Every organisation we work with has at least one AI proof-of-concept that dazzled stakeholders in a demo but never shipped. The pattern is predictable: a data scientist builds something impressive in a Jupyter notebook, leadership gets excited, engineering inherits the notebook, discovers it requires hand-curated training data, runs on a single GPU, has no error handling, and can't handle real-world input variability. The notebook gets shelved, and the organisation concludes 'AI isn't ready for us.' The problem was never the AI — it was the absence of a productionisation framework.
Phase 1: Problem Validation (Weeks 1-2)
Before evaluating any model, we validate that AI is the right tool for the problem. We ask: Is the task currently performed by humans reviewing unstructured data? Does the decision have a measurable quality metric? Is there sufficient training or evaluation data? Can the system tolerate probabilistic (non-deterministic) outputs? If the answer to any of these is 'no,' we explore rule-based or traditional algorithmic approaches first. This filter eliminates approximately 40% of proposed AI features and saves months of wasted engineering effort.
Phase 2: Architecture Design (Weeks 3-4)
Production AI systems require infrastructure that POCs don't. We design for: model serving (using FastAPI or AWS SageMaker endpoints), prompt management (version-controlled prompt templates with A/B testing capability), guardrails (input validation, output filtering, PII detection), monitoring (latency, token usage, output quality scoring), and graceful degradation (fallback responses when the model is unavailable or confidence is below threshold). This architecture is documented before any model evaluation begins, ensuring that whatever model is selected can be deployed with production-grade reliability.
Phase 3: Model Selection & Fine-Tuning (Weeks 5-8)
Model selection isn't about choosing 'the best model' — it's about choosing the right model for your latency budget, cost constraints, and accuracy requirements. For most business applications, we evaluate: GPT-4o for highest accuracy tasks with generous latency budgets, Claude for long-context reasoning and nuanced content generation, GPT-4o-mini or Claude Haiku for high-throughput classification where cost matters, and fine-tuned open-source models (Llama, Mistral) for use cases with strict data residency requirements. We build evaluation harnesses with 200+ test cases covering edge cases, adversarial inputs, and demographic fairness before committing to a model.
Phase 4: Ship, Measure, Iterate (Weeks 9-12)
We deploy AI features behind feature flags with automatic quality monitoring. Every AI-generated output is scored against confidence thresholds, and outputs below threshold trigger human review queues. We implement feedback loops where user corrections automatically populate evaluation datasets, creating a flywheel of continuous improvement. The most critical metric we track isn't model accuracy — it's business outcome: did the AI feature actually move the metric we set out to improve? If not, we iterate on the problem framing, not just the model.