How Miracuves delivers generative AI products — from 25+ production deployments
After shipping 25+ production GenAI applications across SaaS, legal tech, and customer support, Miracuves has a specific methodology for LLM delivery. We start from proven modules — document ingestion, embedding pipelines, retrieval with reranking, agent tool routing, and evaluation harnesses — not from a blank ChatGPT API call in a demo script.
Generative AI only creates business value when responses are grounded in your data, guarded against hallucination, and observable in production. Miracuves pairs LangChain or LlamaIndex orchestration with vector stores and your choice of OpenAI, Claude, or open-weight models — full source code and prompt assets yours on handoff.
Who this service is built for: SaaS founders, enterprise product teams, and support leaders who need LLM-powered assistants, internal knowledge search, content generation, or multi-step AI agents — not a thin API wrapper with no evals. Miracuves generative AI development fits when you need RAG over proprietary documents, role-based access, audit trails, and a company accountable for production reliability. If classical predictive ML is the better fit, we recommend our machine learning development or LLM development services instead.
RAG pipelines — chunking, embeddings, vector search, reranking, and citation-backed answers
LangChain and LlamaIndex for agent workflows, tool calling, and multi-step reasoning
Guardrails — PII redaction, prompt injection filters, toxicity checks, and human-in-the-loop review hooks
LLM observability — LangSmith or custom tracing for latency, cost, and answer quality per session
Deployment on AWS, Azure, or VPC — API keys in secrets manager, not client bundles
From our GenAI team — EU B2B SaaS knowledge assistant, 5 weeks
"12,000 Confluence pages, no internal search that worked, and sales engineers wasting hours finding pricing docs. Miracuves built a RAG assistant with Pinecone, Claude, and role-based document access — answers cite the source paragraph. Support ticket deflection rose 38% in month one. Delivered on week 5 for their enterprise rollout."
25+
GenAI products in production
38%
Avg support deflection (RAG assistants)
4–10
Weeks from brief to production API
$3,699
Published starting investment anchor
100%
Prompt + pipeline code ownership
RAG
Grounded answers with citations
Why GenAI at Miracuves
Time to first RAG MVP4–6 weeks
Model flexibilityOpenAI · Claude · OSS
Grounding standardCitations on every answer
Clone-ready baseChatGPT product from $3,699
Eval harnessRegression before each release
Source code ownership100% yours
