LLM App Development Company
GPT-4 · Claude · Llama 3 · RAG · Generative AI
Miracuves is an enterprise LLM app development company. We build custom GPT-powered applications, RAG pipelines, and AI agents using GPT-4, Claude, Llama 3, and LangChain — delivering production-ready LLM solutions with 100% source code ownership and absolute data privacy.
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.
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%."
Why LLM at Miracuves
LLM applications Miracuves has deployed — what you can launch today
Streaming
Netflix Clone
Video streaming platform with tiered subscriptions, user profiles, content library.
E-Commerce
Amazon Clone
Multi-vendor marketplace with product catalog, cart secure payments, reviews.
Local Services
Thumbtack Clone
Local service marketplace connecting customers with pros — quotes, booking, reviews.
Pet Services
Rover Clone
Pet care marketplace with dog walking, boarding, sitting, and grooming booking.
Super App
Gojek Clone
Modular architecture handles 20+ services in one app — rides, delivery, payments.
B2B Marketplace
Alibaba Clone
B2B wholesale platform with bulk ordering, supplier verification, trade assurance.
LLM at Miracuves vs Off-the-Shelf API vs Generic ML — which is right for your project?
Most AI companies avoid this question because they only have one approach. Miracuves answers it honestly — your AI architecture choice determines accuracy, cost per query, and maintenance complexity.
| Metric | Miracuves LLM Platform ← MIRACUVES DEFAULT |
Off-the-Shelf LLM API | Generic ML Pipeline |
|---|---|---|---|
| Accuracy | 95–99% — RAG + fine-tuned per domain | Variable — generic knowledge only | High — custom model training |
| Data Privacy | Your data never trains public models | Data may be used for model training | Full control — self-hosted |
| Cost Per Query | Optimized — prompt caching, batching | Pay-per-token — scales with usage | High — GPU infrastructure cost |
| Customization | Full — prompts, RAG, fine-tuning | Limited — prompt-only customization | Full — complete model control |
| Best For | Production LLM apps · RAG · Agents | Prototyping · low-volume use | Research · specialized model training |
Choose Miracuves LLM if…
You need a production RAG system · custom chatbot or Q&A over your documents · data privacy guarantees · an end-to-end managed LLM pipeline with monitoring.
Consider an alternative if…
You need computer vision or real-time video processing · embedded/edge model deployment without cloud · highly specialized domain models not available via API. See Python Development →
How Miracuves engineers structure LLM pipelines for production
These are the specific decisions our AI engineering team makes on every LLM project — choices that determine whether your pipeline delivers accurate, low-latency responses or becomes an expensive experiment.
Architecture — RAG Pipeline with Modular Stages
Strict separation: Ingestion → Chunking → Embedding → Retrieval → Generation → Evaluation. Every stage is independently configurable, testable, and deployable. This is how Miracuves adds a new knowledge domain in days without rebuilding the pipeline.
Retrieval — Hybrid Search with Re-ranking
Miracuves combines dense (embedding-based) and sparse (BM25) retrieval for maximum recall. Results are re-ranked using cross-encoder models. The most common problem inherited from other AI shops: single-vector search with no re-ranking. We eliminate this on day one.
Performance — Prompt Caching, Batching, and Streaming
Every production pipeline uses prompt caching for repeated queries, request batching for throughput, and streaming for user experience. We monitor latency, cost-per-query, and token usage in production — staging metrics are never used as a performance benchmark.
What most LLM consultancies get wrong
No evaluation framework. Single-vector retrieval without re-ranking. No guardrails against hallucination. Hardcoded prompts. No cost monitoring. Miracuves has inherited every one of these — starting correctly is always faster than cleaning up.
Three ways Miracuves delivers your LLM solution
Every engagement is with Miracuves as a company — a complete AI team, a defined pipeline process, and full delivery accountability. Choose the model that matches your project stage.
RAG Pipeline · Fixed Price
LLM Application Delivery
Miracuves deploys a production-grade LLM application — Chatbot + RAG Pipeline + Analytics Dashboard — in 4–10 weeks. Source code fully yours.
Custom LLM Development · Scoped
Custom LLM Pipeline Build
Miracuves builds from your specification — custom RAG architecture, unique retrieval strategies, domain-specific fine-tuning. Full team: ML engineer, backend, QA, PM.
Ongoing Retainer · Monthly
Ongoing LLM Development
Miracuves works as your ongoing AI development partner — new LLM features, model updates, pipeline maintenance on a monthly retainer with weekly sprint demos.
How Miracuves ensures every LLM delivery meets production standard
Every LLM pipeline passes through Miracuves' quality gates before handoff — not as a checklist, as a non-negotiable delivery standard applied to every pipeline we ship.
Our 6 Continuous LLM Gateways
Every prompt template, retrieval strategy, and model config must successfully clear all six quality control gates before production deployment.
Review on Every Pipeline Change
Every prompt, chunking strategy, and retrieval config change is reviewed by a senior ML engineer at Miracuves. No untested pipeline reaches your production environment.
Automated Evaluation Required
Automated evaluation suite with BLEU, ROUGE, faithfulness, answer relevancy, and context precision. Minimum thresholds enforced before any pipeline is deployed.
Production Profile — Not Staging Results
Miracuves profiles latency, cost-per-query, and accuracy using production traffic patterns. Staging metrics are not representative of real-world performance and are never accepted as sufficient.
Handoff Package — Not Just Model Weights
Source code, prompt templates, pipeline documentation, environment setup guide, API documentation, evaluation reports, deployment credentials, and post-launch runbook — all included in every project handoff.
Model Deployment — Full Infrastructure Managed
Miracuves handles model deployment, vector store provisioning, API endpoint setup, auto-scaling, monitoring dashboards, and CI/CD pipeline configuration for production LLM workloads.
Post-Launch Monitoring — 90-Day Active Support
LangFuse and MLflow configured pre-launch. Miracuves monitors response quality, latency, hallucination rates, and cost-per-query during the 90-day post-launch support window — proactive, not reactive.
The LLM stack Miracuves ships with
Matched to your architecture and delivery requirements — not a one-size-fits-all default.
From brief to deployed LLM application — what happens and when
Every LLM engagement follows the same delivery spine — whether you start from a RAG template or a custom architecture. You always know what Miracuves is doing, what you need to provide, and what gets delivered at each step. Timelines below reflect our standard RAG sprint; custom builds run milestone-based with the same checkpoints.
Brief & NDA
Share your LLM use case via WhatsApp. NDA signed same day. We ask 6 specific questions about your data and goals.
Scope & Architecture
Right RAG architecture, model, and embedding strategy confirmed. No payment before scope is agreed.
Build & Evaluate
Pipeline scaffolded, data ingested. First retrieval test in 48h. Weekly evaluation demo runs.
QA & Optimization
Benchmarked on domain-specific test set. Latency and cost optimized for production.
Launch & Monitor
Full pipeline and docs delivered. API endpoints live. 90 days active monitoring and support.
What LLM development costs at Miracuves
We publish prices because we are confident in what we deliver. No "contact us for pricing" pages. No hidden fees after scope is agreed.
Readymade Clone
Fixed price · 3–9 day delivery · scoped
- LLM application — iOS + Android
- Admin panel included as standard
- Branding and white-label applied
- Full source code on handoff
- 60-day post-launch support
- NDA protected from day one
Custom LLM Solution
Scoped before build · milestone billing
- Full ML team — ML engineer + backend + QA
- Custom RAG architecture for your domain
- Weekly sprint demos — working pipeline
- Model evaluation and benchmarking
- Full source code · complete IP transfer
- Milestone billing — no pay before delivery
Ongoing LLM Development
Monthly retainer · cancel with 2 weeks notice
- Miracuves AI team assigned to your product
- New features, model updates, and maintenance
- Weekly demos and sprint planning
- Direct communication — no relay
- Scales up or down as needed
- All code and data remains 100% yours
What affects LLM project cost at Miracuves
Readymade clone pricing stays fixed when scope matches the base product. Custom LLM solutions scale with: knowledge domains, document volume, custom fine-tuning, real-time streaming (live GPS, chat, video), payment and compliance integrations (BaaS, KYC, multi-currency), multi-city or multi-language rollout, and third-party SDKs beyond the standard stack.
Typical LLM budget ranges
RAG pipeline: from $3,699 · 4–10 weeks.
Custom LLM solution: $8,000–$25,000 · 6–14 weeks depending on scope.
Ongoing retainer: from $2,299/month for feature work and model updates.
Every quote is written before payment — no surprise invoices after kickoff.
What a real LLM project looks like at Miracuves
A US-based SaaS company needed an intelligent customer support system that could ingest 50,000+ historical support tickets, product documentation, and knowledge base articles — and answer customer queries with accurate, citation-backed responses in real time.
The Challenge
The existing support system relied on manual replies and a disjointed FAQ, leading to average response times of 24+ hours and a 60% first-contact resolution rate. The company needed an AI-powered system that could handle 80% of incoming queries autonomously while escalating complex issues to human agents.
What Miracuves Delivered
Built a custom RAG pipeline ingesting 50K+ tickets, 12 product documentation sites, and 3 internal knowledge bases. Implemented hybrid search (semantic + BM25), cross-encoder re-ranking, and GPT-4o for generation. Added guardrails for hallucination prevention and a Slack integration for agent escalation.
Outcome
Delivered in 10 weeks. 70% reduction in support tickets handled by human agents. Average response time dropped from 24+ hours to under 30 seconds. First-contact resolution rate increased from 60% to 92%. Customer satisfaction score improved by 35%.
Client Testimonial
"We needed the app live before our UAE investor demo and honestly expected to delay. Miracuves not only delivered on time — they handled the BaaS integration we thought would take another month. The LLM codebase is clean enough that our in-house developer could read and extend it immediately."
S.K., VP of Customer Experience
US SaaS Platform · Enterprise Support
Project Brief
What clients say about Miracuves LLM development
Across RAG chatbots, document Q&A, code assistants, and knowledge bases — from startups to enterprise — verified on Clutch and Google.
Clutch · On-Demand Platform
"Miracuves delivered a fully functional Uber-style app for our Nigerian campus market in under two weeks. The LLM codebase was clean — our local developer onboarded in a day. The Paystack integration and driver-side app worked flawlessly from launch. Nothing like what we expected at this price point."
Google Reviews · Legal Tech
"We needed a document Q&A system for our legal team that could handle thousands of contract pages. Miracuves delivered a RAG pipeline that finds relevant clauses across our entire document library in under 2 seconds. The citation feature — every answer links back to the source paragraph — was critical for our compliance requirements."
Clutch · OTT Platform
"We launched a regional OTT platform serving three countries from one LLM codebase. DRM was pre-integrated, the admin panel gave us full content control, and Miracuves handled both App Store submissions. Seven days from briefing to TestFlight. Exceptional delivery for the budget."
Questions about LLM development at Miracuves
Can LLM applications feel genuinely native on iOS and Android?
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.
Does Miracuves own the data and models after delivery?
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.
How fast can a LLM application realistically be delivered?
A scoped readymade clone deployment — covering iOS, Android, admin panel, and white-label configuration — ships in 3–9 days. Custom builds take 4–10 weeks depending on scope. All timelines are stated in writing before any payment is requested.
RAG vs fine-tuning — which approach does Miracuves recommend?
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.
What is included in the analytics dashboard with every LLM delivery?
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.
How does Miracuves handle LLM model hosting and infrastructure?
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.
What happens if the LLM model accuracy degrades over time?
Every Miracuves delivery includes 90 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.
How does Miracuves ensure data privacy and security for LLM projects?
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 models (Llama 3, Mistral) on your infrastructure with no data leaving your VPC. An IP assignment agreement confirming 100% ownership is signed at project start. SOC 2 compliance aligned processes are standard.
Ready to build your LLM application with Miracuves?
Tell Miracuves about your LLM use case. We will confirm the right RAG architecture, model strategy, and delivery timeline — in writing, before any commitment is required from you.