How Miracuves delivers LLM applications — from 9,000+ projects of real experience
After deploying 200+ LLM solutions and processing 10M+ inference requests, Miracuves
has a specific way of building LLM applications. We start from production-grade RAG pipelines, prompt engineering frameworks, and fine-tuning templates —
already integrated with vector stores, guardrails, and evaluation benchmarks — not from a blank notebook.
Our RAG-based architecture delivers retrieval-augmented generation, multi-turn conversation, and tool-calling
from one unified pipeline. For enterprise deployments, this eliminates the need for separate ML engineering teams — one pipeline,
multiple deployment targets, full source code yours on handoff.
Who this service is built for: Product teams and enterprises building AI-powered applications — custom chatbots, document Q&A systems, code assistants, content generators, and knowledge retrieval systems. Miracuves LLM development fits when you want a readymade clone
base or a custom cross-platform product with published pricing, full IP ownership, and a company accountable
for delivery — not individual contractors. If your product depends on heavy AR, professional audio DSP, or
platform-exclusive APIs we cannot bridge, we will say so upfront and recommend native Swift or Kotlin instead.
RAG pipeline with chunking, embedding, and retrieval strategies tested against your data domain
Prompt engineering framework enforced — system prompts, few-shot templates, guardrails from day one
Model evaluation benchmarks (BLEU, ROUGE, faithfulness, answer relevancy) on every delivery
CI/CD pipeline for model deployment via MLflow or LangFuse configured on every project
Production monitoring with real-time latency, cost tracking, and drift detection
From our LLM team — UAE Fintech project, 10 days
"Customer support chatbot ingesting 50K+ support tickets, 12 product docs, and 3 knowledge bases — across
email, chat, and Slack — in 8 weeks. We used our RAG pipeline base, added custom chunking for technical documentation,
implemented hybrid search (semantic + keyword), and built guardrails for hallucination prevention. Reduced tickets by 70%."
