You will get Production-Ready RAG Pipeline & LLM Architecture Built to Scale
Project details
Most AI projects never leave the demo stage — not because the idea was bad, but because the architecture was wrong from day one.
Wrong model. Wrong retrieval strategy. Wrong vector database. The result: hallucinations, exploding API costs, and a PoC that can't survive real users.
I fix that.
I design and build production-grade LLM architectures starting with your use case and working backward to the right stack:
✅ RAG vs. Fine-tuning vs. AI Agents — I tell you exactly what to build
✅ Model selection: OpenAI vs. Anthropic vs. Llama — cost vs. performance analysis
✅ Vector DB architecture: Pinecone, Weaviate, FAISS — designed for your data
✅ Hallucination guardrails + evaluation pipelines (RAGAS, DeepEval)
✅ MLOps & production deployment blueprint
✅ Cost optimization — stop paying for tokens you don't need
Built on 20+ years of enterprise architecture experience across IIoT, HealthTech, and AI platforms — not API wrappers, but systems that survive real scale.
3 Packages:
→ Signal $800: Architecture audit + model selection doc
→ Inference $2,500: Full RAG pipeline build + integration
→ Command $6,000: Complete LLM system, MLOps, evals + cost optimization
Wrong model. Wrong retrieval strategy. Wrong vector database. The result: hallucinations, exploding API costs, and a PoC that can't survive real users.
I fix that.
I design and build production-grade LLM architectures starting with your use case and working backward to the right stack:
✅ RAG vs. Fine-tuning vs. AI Agents — I tell you exactly what to build
✅ Model selection: OpenAI vs. Anthropic vs. Llama — cost vs. performance analysis
✅ Vector DB architecture: Pinecone, Weaviate, FAISS — designed for your data
✅ Hallucination guardrails + evaluation pipelines (RAGAS, DeepEval)
✅ MLOps & production deployment blueprint
✅ Cost optimization — stop paying for tokens you don't need
Built on 20+ years of enterprise architecture experience across IIoT, HealthTech, and AI platforms — not API wrappers, but systems that survive real scale.
3 Packages:
→ Signal $800: Architecture audit + model selection doc
→ Inference $2,500: Full RAG pipeline build + integration
→ Command $6,000: Complete LLM system, MLOps, evals + cost optimization
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Google AutoML, Keras, MLflow, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$800
|
Standard
$2,500
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 10 |
AI Model Integration | - | ||
Detailed Code Comments | - | ||
Knowledge Graph | - | ||
Model Documentation | |||
Ontology | - | - | |
Source Code | - | ||
Taxonomy | - | - |
Frequently asked questions
About Navneet
AI Engineer Document Trust LLM Modernisation IIoT RAG Architect
Ghaziabad, India - 12:22 am local time
I've built live AI scoring engines, IIoT smart grid platforms, document intelligence pipelines, agentic workflows, and LLM modernisation systems — all production-grade, all shipped.
Stack: LLM · RAG · NLP · Computer Vision · OCR · AI Agents · FastAPI · React · MQTT · AWS · Supabase.
I don't consult on things I haven't built. Every engagement is grounded in real production experience across multiple industries and verticals.
If you need AI that actually works in the real world — let's build it.
Steps for completing your project
After purchasing the project, send requirements so Navneet can start the project.
Delivery time starts when Navneet receives requirements from you.
Navneet works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Requirements Review
I review your submitted requirements, existing stack, and use case. If anything needs clarification, I'll send a short list of questions within 24 hours of order placement.
Architecture Audit & Strategy Document
I analyze your use case and deliver an Architecture Decision Document — RAG vs fine-tuning vs agents, model selection, vector DB recommendation, and cost-performance tradeoffs.