The gap between prototype and production AI
Most AI integrations start as a prototype: a few API calls, a prompt template, and a demo that impresses in a meeting. The prototype works. Then it goes to production, and reality arrives: inconsistent outputs, runaway token costs, retrieval quality that doesn't survive real user queries, no visibility into what went wrong.
Peakvance closes that gap. We bring the same engineering rigor to AI integration that we apply to any distributed system: explicit contracts, observability, cost controls, and failure modes that degrade gracefully instead of silently.
What we deliver
LLM Integration & Prompt Engineering
Clean integration of large language models into existing applications — with structured output handling, retry logic, token budgeting, and the contract enforcement that separates production integrations from demo code.
RAG Pipeline Design & Implementation
Retrieval-augmented generation built properly: document ingestion pipelines, chunking strategies tuned to your content, embedding model selection, vector store configuration, reranking, and semantic caching. We optimize for retrieval quality, not just implementation speed.
AI Cost Optimization
Uncontrolled inference spend is one of the fastest ways to make an AI feature unviable. We analyze your inference patterns, implement caching layers, right-size model selection per task, and add the guardrails that keep costs predictable as usage grows.
AI Observability & Evaluation
You can't improve what you can't measure. We instrument your AI integrations with request tracing, output quality metrics, latency histograms, and automated eval suites — so you know immediately when model behavior drifts.
Migration from Prototype to Production
Many companies built AI integrations fast and now face reliability, cost, or quality problems. We audit the existing implementation, identify the root causes, and rebuild the critical paths on a foundation that holds up.
What makes this different
We don't use off-the-shelf AI frameworks as a crutch. Where frameworks add value, we use them. Where they add magic that hides important behavior, we build the integration ourselves. The result is AI components you can actually debug, optimize, and own long-term.
Our team understands how these models actually behave — not just how the documentation says they should behave. That includes the edge cases, the failure modes, the context window limitations, and the retrieval quality issues that vendor benchmarks don't surface.
Ready to make your AI integration production-grade?
Get in touch to discuss your integration project, or explore our other AI engineering services: