There is a consistent pattern in how AI integration projects fail: the demo works, the team is excited, the integration gets built, and then three things happen in the first 60 days that nobody planned for.
First, the latency is higher than expected. Second, the outputs are inconsistent in ways that matter to users. Third, the cost per request is 3 to 5 times what was estimated because nobody ran realistic load projections before choosing the model.
Production-ready AI integration means the feature behaves predictably under load, fails gracefully when the model returns unexpected output, costs what you projected at scale, and can be monitored and debugged like any other part of your system.
Most integrations that fail in production fail on the last two: cost modeling and observability. Both are solvable. Neither is addressed in the typical proof-of-concept phase.
Document processing and extraction. LLMs are reliable at pulling structured data from unstructured documents — invoices, contracts, support tickets, medical records. The failure mode is hallucination on edge cases, which is addressable with validation layers and human-in-the-loop review on low-confidence outputs.
Semantic search and retrieval. RAG (retrieval-augmented generation) is production-mature. The main engineering work is chunking strategy, embedding model selection, and retrieval evaluation. Teams that skip evaluation end up with search that works in demos and fails on real queries.
Classification and routing. Categorizing support tickets, routing leads, tagging content — these are high-volume, low-stakes tasks where LLMs outperform rule-based systems and the cost-per-call math works. They are also easy to evaluate with labeled datasets.
Internal knowledge assistants. Connecting an LLM to internal documentation, runbooks, or product knowledge bases is one of the highest-ROI integrations available. The main risk is retrieval quality — the assistant is only as good as what it can find.
Autonomous agents making consequential decisions without human review. The models are capable. The reliability and auditability standards are not there for most regulated or high-stakes environments.
Real-time voice with complex reasoning. Latency is improving but not solved for conversations requiring multi-step reasoning under 500ms.
Start with the failure modes. Before writing any code, define what a bad output looks like and how your system will detect and handle it. Teams that skip this step build integrations that work 90% of the time and create serious problems the other 10%.
Run cost projections at realistic volume. Take your current transaction volume, apply a realistic AI call rate, and calculate monthly model costs at p50 and p95 latency. Do this before choosing a model, not after.
Define your evaluation set. For any integration that affects user experience, build a labeled dataset of 50 to 100 examples before you start. Use it to compare models, track regressions, and make the go/no-go decision for production deployment.
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