LLM App Development Company

Yes. Miracuves builds production-ready LLM applications including custom RAG chatbots, document Q&A systems, content generation engines, AI code assistants, sentiment analysis tools, and enterprise knowledge bases. Every solution is deployed with a complete RAG pipeline, evaluation benchmarks, guardrails against hallucination, and production monitoring.

Readymade clone pricing stays fixed when your scope matches the base product. An LLM application on our RAG pipeline base starts from $3,699 and takes 2-8 weeks. A custom LLM solution typically runs $8,000-$25,000 and takes 2-8 weeks; larger scopes are quoted in writing before work starts. Where it lands depends on the number of knowledge domains, document volume, custom fine-tuning, evaluation depth, integrations, languages and whether models are hosted or self-hosted. Ongoing LLM development is available from $2,299/month for feature work and model updates. Every quote is written before payment, with no surprise invoices after kickoff.

Absolutely not. Miracuves delivers 100% source code ownership - all pipeline code, prompt templates, embedding vectors, and deployment configurations. Your data is never used to train public models. We sign an IP assignment agreement confirming complete ownership transfers to you at project start. Zero lock-in, zero data sharing.

A ready-made platform such as our ChatGPT clone - web app, admin panel and mobile apps under your brand, with white-label configuration - ships in 6 working days. An LLM application built on our RAG pipeline base takes 2-8 weeks, and custom LLM builds also land in 2-8 weeks, with larger scopes quoted in writing before work starts. All timelines are stated in writing before any payment is requested.

For most use cases - customer support, document Q&A, knowledge management - RAG is the right starting point. It provides accurate, citation-backed answers without retraining the model. Miracuves recommends fine-tuning when you need the model to adopt a specific writing style, domain terminology, or consistent response format that prompt engineering alone cannot achieve. We often combine both approaches.

A comprehensive analytics dashboard with real-time query monitoring, response accuracy metrics, latency tracking, cost-per-query analysis, user feedback collection, hallucination rate monitoring, and usage patterns. Integrated with LangFuse or MLflow for production observability - delivered as a web application accessible from any browser.

Miracuves handles the full infrastructure stack - vector store provisioning (Pinecone, ChromaDB, or Weaviate), API endpoint setup with FastAPI, auto-scaling via Kubernetes, monitoring with LangFuse/MLflow, and CI/CD pipeline configuration. We deploy on AWS Bedrock, GCP Vertex AI, or your preferred cloud. The infrastructure is configured for production workloads from day one.

Every Miracuves delivery includes 60 days of post-launch monitoring and support. We track response quality, hallucination rates, and latency continuously. If model accuracy degrades due to data drift or model API changes, we diagnose and fix within the support window. Ongoing monitoring and model updates are available through monthly maintenance retainers at published rates.

Miracuves signs a bilateral NDA before any project details are shared. Your data is never used to train or improve public models. All data is encrypted in transit and at rest. For sensitive deployments, we can self-host open-weight models (Llama, Mistral and similar families) on your infrastructure with no data leaving your VPC. An IP assignment agreement confirming 100% ownership is signed at project start. We build to the controls a SOC 2 audit checks; the audit and report remain your own.

Yes. For teams whose data cannot leave their network, we package an open-weight model such as Llama or Mistral in a Docker container behind an inference server like vLLM, which exposes an OpenAI-compatible API, so the rest of the application does not care where the model runs. It is deployed on GPU instances in your cloud account or on-premise hardware, with Kubernetes handling scaling and health checks. Before committing we compare the monthly GPU cost against hosted API pricing at your expected volume, because self-hosting only pays off above a steady load.

We choose by testing, not by brand. Two or three candidate models from the GPT, Claude, Gemini and Llama families are run against your evaluation set, and we compare answer quality, latency, cost per query and where each is allowed to process your data. Often the answer is two models: a stronger one for hard questions and a cheaper one for routine ones. The model name lives in configuration, so moving to a newer release later means re-running the evaluation, not rewriting the application.

Yes, and it is the most common engagement. We build the LLM pipeline as a separate service with its own API, then connect it to your existing login, permissions and data sources - your helpdesk, CRM, database or document store. Your current web or mobile app calls that API, so its codebase changes only where the new feature appears. Scope and price are agreed in writing before work starts.