You will get RAG Architecture Integration (Enterprise-Grade)
Project details
Most RAG services on Upwork focus on demos and proof-of-concepts.
We build production-grade AI systems.
We are an AI product company, not prompt-engineers or experimental freelancers. Our systems are already used by thousands of users and trusted by enterprises, which shapes how we design every RAG architecture.
What sets us apart:
Built for scale, not experiments – real traffic, low latency, and cost-controlled systems
Strict grounding & low hallucination – answers generated only from your data, with citations and fallbacks
Product-first engineering – monitoring, evaluation, versioning, and future scalability built in
Enterprise-ready by default – security, access control, observability, and clean system boundaries
Battle-tested experience – SaaS platforms, internal tools, customer support, analytics, and decision systems
If you want a quick demo, this may not be the right fit.
If you want a RAG system that can ship, scale, and be trusted in production, this is exactly what we deliver.
We build production-grade AI systems.
We are an AI product company, not prompt-engineers or experimental freelancers. Our systems are already used by thousands of users and trusted by enterprises, which shapes how we design every RAG architecture.
What sets us apart:
Built for scale, not experiments – real traffic, low latency, and cost-controlled systems
Strict grounding & low hallucination – answers generated only from your data, with citations and fallbacks
Product-first engineering – monitoring, evaluation, versioning, and future scalability built in
Enterprise-ready by default – security, access control, observability, and clean system boundaries
Battle-tested experience – SaaS platforms, internal tools, customer support, analytics, and decision systems
If you want a quick demo, this may not be the right fit.
If you want a RAG system that can ship, scale, and be trusted in production, this is exactly what we deliver.
AI Algorithms
Large Language Model, Long Short-Term Memory Network, Multimodal Large Language ModelAI Applications
AI Chatbot, AI Mobile App Development, Conversational AI, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, TensorFlowAI Models
BERT, ChatGPT, LaMDAWhat's included
| Service Tiers |
Starter
$1,499
|
Standard
$3,999
|
Advanced
$6,999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 29 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | - | |
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | |||
Source Code |
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Ali D.
May 20, 2025
Machine Learning and Data Science
Very talented, smart, and dedicated ml engineer. he became part of our ML development team. We are very happing to have him in our team.
MS
Manish S.
Aug 29, 2024
New TradingView Pinescript Strategy
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Joseph R.
Aug 27, 2024
PineScript Engineer
AS
Alina S.
Aug 2, 2024
Evaluating AI Responses (Hindi)
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Gagat B.
May 16, 2024
VA table 2
excellent result, talented, beyond what I expected, will contract him again
About Neelambar
Full Stack AI Developer | AI Automation | AI Integration | AI Engineer
100%
Job Success
Pune, India - 6:41 pm local time
I have noticed that most AI projects don't fail after the MVP stage. They fail when a user hits a long-running workflow, and the system freezes, or 100 other things that are not expected. I build the infrastructure that prevents such failures when moving from an AI demo to a system that handles real users, real data, and real consequences.
Right now, asking a coding agent to build an application is a commodity any beginner can experiment with. The engineers worth hiring are the ones who keep an agent running under real load, recover it when it crashes, and know exactly what it was doing when it did. The difference is exactly that between a sailing ship and a nuclear submarine.
Over the last few years, my team and I have built 80+ AI systems across industries. And these are real systems up and running right now with real users, real revenue, and a real business impact.
We specialize in building systems that are:
- Enterprise-grade
- Reliable while maintaining harness with non-deterministic LLM
- Scalable, handling unexpected load
- Secure, with complex vulnerabilities taken care of in-house
- Integrated into real businesses solving real problems
- Built for ROI
What I Build:
- AI SaaS MVPs and Production builds: Next.js/React.js + AI Agents + CRMs/Tools + Stripe
- Multi-Agent Systems: LangGraph + LLM pipelines, 80+ products in production
- Enterprise RAG: Context-aware engines across Notion, Slack, SQL & Drive (10K+ docs)
- AI Voice Agents: Livekit + Vapi + ElevenLabs for appointment booking, sales, support
- MCP Servers: Custom servers exposing your data to your agents
- n8n + LLM Workflows: End-to-end automations with CRM, email, voice, WhatsApp
- Multi-Channel Ops: Telegram, Shopify + Square + Wholesale unified in Google Sheets / QuickBooks
Specialized in:
- Claude Ecosystem: Claude Code, Cowork, MCP, Skills, Plugins, Agent SDK, OpenClaw (self-hosted)
- Voice AI Agents: Livekit, Retell, Vapi, Bland, Synthflow, ElevenLabs, Deepgram, Whisper, Custom pipeline
- AI Systems: RAG, GraphRAG, SQL Agents, Multi-Agent Systems, Computer Use, Browser Automation
- Frameworks: LangChain, LangGraph, CrewAI, Pydantic AI, OpenAI SDK, Claude Code, Deep Agents, Livekit
Technical Stack:
- AI & LLMs: Anthropic Claude, OpenAI, Gemini, DeepSeek, Llama / Ollama (local)
- Voice & Real-Time — LiveKit, WebRTC, SIP, RTP, Deepgram, ElevenLabs, OpenAI Realtime API.
- Orchestration: LangChain, LangGraph, CrewAI, Pydantic AI, MCP, Temporal, Langfuse
- Vector & Data: Qdrant, Pinecone, ChromaDB, Supabase, PostgreSQL, pgvector
- Backend: Python, FastAPI, Node.js, TypeScript, Docker, Kubernetes, AWS, Redis, Apache Kafka.
- Frontend: Next.js, React, Tailwind, Vercel AI SDK
- Automation: n8n, Claude Code, Webhooks, REST APIs, OpenClaw
What you can expect:
Systems-first delivery. Not scripts that work once in isolation. Services with logging, error handling, and observability built in from the start, because retrofitting these into a broken production system costs three times what it would have taken to include them.
Technical honesty. If your use case doesn't need a large language model, I'll tell you. If a specific model is overkill for your budget and use case, I'll say so. I have lost projects being upfront about this. The ones I keep tend to run long.
Clean handoff. Containerized, fully documented codebases built for your team to maintain, extend, and understand without me in the room.
Steps for completing your project
After purchasing the project, send requirements so Neelambar can start the project.
Delivery time starts when Neelambar receives requirements from you.
Neelambar works on your project following the steps below.
Revisions may occur after the delivery date.
Initial consultation
To understand and use case
Requirement gathering
We will need requirements such as data format to start embedding and create the pipeline