What is generative AI development at Miracuves?
Miracuves designs, builds, and deploys production LLM applications - RAG knowledge assistants, ChatGPT-style products, AI agents, and content generators. We handle orchestration (LangChain/LlamaIndex), vector retrieval, guardrails, eval harnesses, and deployment. You receive full source code and prompt assets with 100% IP ownership.
How much does generative AI development cost at Miracuves?
The ready-made ChatGPT clone starts from $2,799 and ships in 6 working days; its price stays fixed when your scope matches the base product. A custom RAG assistant typically runs $14,000-$35,000 and a multi-agent platform $25,000-$60,000, delivered in 2-8 weeks, with larger scopes quoted in writing before work starts. Document volume, data sources, multi-tenant access control, agent tool integrations, on-prem or cloud model hosting and fine-tuning decide where a build lands. The ongoing retainer starts from $2,299/month for index and prompt maintenance. LLM API costs are passed through at provider rates and quoted separately in writing. Every quote is written before payment, with no surprise invoices after kickoff.
How long does a RAG project take?
A scoped RAG assistant - document ingestion, vector index, chat UI, and eval suite - is custom work that typically takes 2-8 weeks; larger multi-agent scopes are quoted in writing before work starts. The ready-made ChatGPT clone ships in 6 working days from $2,799. A working retrieval prototype is shown in the early weekly demos so you can validate the approach on your own documents.
Which LLM providers does Miracuves support?
OpenAI's GPT models, Anthropic's Claude models, and open-weight models via Hugging Face inference endpoints. Provider choice depends on context length, cost, compliance, and latency requirements. Miracuves architects for provider swap - you are not locked to a single vendor API.
How do you reduce hallucinations in production?
RAG with citation requirements, cross-encoder reranking, metadata filters, and golden-question eval suites before every release. We do not rely on prompt instructions alone. Answers that cannot be grounded in retrieved chunks return a controlled fallback - not invented facts.
Can Miracuves deploy GenAI in our private cloud or VPC?
Yes. We deploy on AWS, Azure, or GCP with data residency controls. For strict compliance, we support on-prem vector stores and self-hosted open models. API keys and document stores never ship in client-side bundles.
What documents can be indexed for RAG?
PDFs, Word docs, HTML, Confluence, SharePoint, Notion exports, ticketing systems, and database records via connectors. Miracuves assesses chunking strategy and access control per source during discovery. OCR is available for scanned documents.
Do I own the source code and prompts?
Yes - 100% IP ownership on every engagement. Application code, prompt templates, ingestion pipelines, and infrastructure-as-code transfer on handoff. LLM API usage is billed at provider rates; Miracuves does not resell tokens with hidden markup.
What is included in post-launch support?
60 days of active support covering bug fixes, index tuning guidance, and eval interpretation. Ongoing index updates, prompt improvements, and model upgrades are available through our monthly GenAI ops retainer from $2,299/month.
Can Miracuves add generative AI features to an app we already run?
Yes. Most GenAI application development services we deliver sit next to an existing product rather than replacing it. We add the GenAI part as its own service - an API with ingestion, retrieval and generation - and connect it to your current app through a chat panel, a drafting button or a summary view. Your existing code, users and data stay where they are, and the new service respects the access rules your app already has. The work is scoped and quoted as custom GenAI development before it starts.
Do we need to fine-tune a model for our generative AI app?
Usually not at the start. Retrieval over your documents fixes most accuracy problems more cheaply, and it updates the moment a document changes, which fine-tuning does not. Fine-tuning earns its cost when you need a fixed output format, a house writing style or a smaller, cheaper model for one narrow task. We decide it with you after the first eval results, and when fine-tuning or self-hosting becomes the main work, our LLM development service covers it.
What do you need from us to start a generative AI project?
Three things make the first week productive: a sample of the documents or data the app must use, 20-50 real questions your users ask with the answers you expect, and a named person who can say whether an answer is right. We also need to know who may see which documents, and which model providers your security team allows. With those, the first working retrieval demo runs on your own content rather than on sample data.
