You will get Agentic AI Insights Bot (MCP) | NL2SQL + GraphRAG over ERP & Docs
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Top Rated
Project details
You will get an Agentic AI Insights Bot—a production-ready, multi-agent pipeline that turns scattered data into instant, source-cited answers. Using MCP (Model Context Protocol), the bot fuses ERP/SQL tables, PDFs, images, and CSVs through NL2SQL, LangChain, CrewAI, GraphRAG, Pinecone or OpenSearch vectors, and Neo4j relations. The result: sub-2-second responses to 10 k+ queries per day, no hallucinations, and full traceability. Built in Python, containerised with Docker, and deployable to AWS Fargate, ECS, or on-prem, it ships with CI/CD, dashboards, and runbooks. Past deployments cut analyst hours 50 %, deflected 70 % of tier-1 support tickets, and scaled to 1 M+ documents for automotive, SaaS, and e-commerce clients. If you need secure, cost-efficient insights—not just another flashy chatbot this project delivers.
AI Algorithms
Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Speech, AIOps, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$600
|
Standard
$3,000
|
Advanced
$5,500
|
|---|---|---|---|
| Delivery Time | 5 days | 15 days | 35 days |
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 | - |
Frequently asked questions
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PH
Patrick H.
Jul 18, 2025
Optimize MERN Agentic Platform for High-Scale File Uploads & Latency Reduction in Agent Voice Calls
I worked with Muhammad and his team on a MERN Agentic Platform project, and they exceeded our expectations. They not only fixed all our issues but also guided our team on resolving other challenges. They are excellent SaaS architects and truly understand how to build and scale SaaS products.
PH
Patrick H.
Jun 17, 2025
Spark Architect Wanted to Optimize 250TB Data Pipeline on AWS EMR + Glue + Redshift
Mudassir did an outstanding job optimizing our data pipeline. From day one, he demonstrated deep expertise in Spark, AWS EMR, Glue, and Redshift. Not only did he improve the performance and scalability, but he also provided valuable architectural insights and best practices that will benefit our team long-term.
Highly recommended for any team looking for a senior data engineer who can not only solve complex problems but also empower others in the process.
Highly recommended for any team looking for a senior data engineer who can not only solve complex problems but also empower others in the process.
PH
Patrick H.
May 29, 2025
Fractional CTO/AWS consultant for Large Web Scraping Platform | System Design & Architecture
We brought in Cognilium as a Fractional CTO and AWS consultant to help us architect a large-scale web scraping platform—and they exceeded expectations. Their team provided us with a detailed, scalable system blueprint tailored to our use case, covering everything from distributed architecture and fault tolerance to cost-efficient AWS service selection.
They didn’t just consult—they acted as strategic advisors, helping us make critical design decisions and ensuring our internal team was set up for success. Thanks to their guidance, we were able to confidently build the platform in-house using a future-proof architecture.
Highly recommend Cognilium for any team seeking expert-level consulting on scalable AWS infrastructure and scraping system design.
They didn’t just consult—they acted as strategic advisors, helping us make critical design decisions and ensuring our internal team was set up for success. Thanks to their guidance, we were able to confidently build the platform in-house using a future-proof architecture.
Highly recommend Cognilium for any team seeking expert-level consulting on scalable AWS infrastructure and scraping system design.
PH
Patrick H.
May 26, 2025
GenAI Consultant - Automotive Parts Manufacturing IT Transformation
I can’t recommend Cognilium’s engineer highly enough. From day one he felt like an extension of our in-house team—always online when we needed him, answering questions within minutes, and proactively surfacing risks before they became blockers.
His grasp of generative-AI workflows was outstanding: he designed and implemented a truly scalable RAG pipeline that now powers real-time parts-search and knowledge retrieval across millions of records. Just as impressive, he re-architected our ERP workflow automation, untangling legacy processes and delivering a clean, modular design our own engineers can maintain.
Deliverables were shipped ahead of schedule, documentation was clear, and every sprint review ended with our stakeholders saying, “That’s exactly what we needed.” If you’re looking for a professional who can both code and collaborate—especially in manufacturing or automotive contexts—hire Cognilium without hesitation. Five stars all around.
His grasp of generative-AI workflows was outstanding: he designed and implemented a truly scalable RAG pipeline that now powers real-time parts-search and knowledge retrieval across millions of records. Just as impressive, he re-architected our ERP workflow automation, untangling legacy processes and delivering a clean, modular design our own engineers can maintain.
Deliverables were shipped ahead of schedule, documentation was clear, and every sprint review ended with our stakeholders saying, “That’s exactly what we needed.” If you’re looking for a professional who can both code and collaborate—especially in manufacturing or automotive contexts—hire Cognilium without hesitation. Five stars all around.
PH
Patrick H.
May 25, 2023
Full stack(MERN) multi vendor eCommerce search engine site using AWS+Elasticsearch+Nextjs+Serverless
Enjoyed working again with Mudassir. Has been very helpful in helping us achieve our milestones.
About Muhammad
Senior AI Engineer | 37 Agents in Production | RAG, LLM, Automation
100%
Job Success
Lahore, Pakistan - 8:44 am local time
🤝 How I Work
I scope before I build. Every project starts with a written architecture plan and acceptance criteria — you know exactly what you're getting before the first commit. Change requests go through milestones, not surprises.
• Weekly demos and async daily updates
• I respond within 4 hours
• POCs ship in under 2 weeks
• Full documentation and team handover — your team operates the system independently after I leave
Every system includes self-healing when APIs fail, automatic quality checks before outputs reach your users, and monitoring dashboards so you see exactly what's happening — not just hope it works.
I don't disappear after launch. Active clients right now: a $3,200/week AI co-pilot for K-12 education (repeat engagement through multiple phases) and a distributed scraping platform with a 5.0 rating across $56K in contracts (5 phases delivered over months). I build long-term — not hit-and-run.
🛠️ What I Build
AI Agent & Multi-Agent Systems
Supervisor routing, parallel execution, smart routing that cuts LLM costs by 75%, guardrails that block unsafe requests in real time. Google ADK, AWS Bedrock AgentCore, LangGraph, LangChain, CrewAI. From single agents to 23-agent orchestrated pipelines.
RAG & Knowledge Systems
Hybrid retrieval (dense + BM25 + Reciprocal Rank Fusion), GraphRAG with Neo4j, Vertex AI Search. Citations, audit trails, anti-hallucination grounding checks. LLM-as-Judge scoring validates every output before it reaches your users.
Document Intelligence & Compliance
Automated classification, extraction, validation for contracts, financial documents, investment memos, regulatory filings. 23-agent legal pipeline scoring 12 categories. 8-stage financial pipeline classifying 25 document types with cross-document entity resolution.
AI Integration Into Existing Products
I embed AI directly into tools your team already uses — no rip-and-replace. Delivered AI inside Microsoft Word (Add-in for contract review), LearnWorlds LMS (iframe for teacher coaching), QuickBooks + Google Workspace (OAuth for financial intelligence).
Full-Stack AI Platforms
Multi-tenant SaaS with RBAC (60+ permissions), SSE streaming, knowledge graphs, real-time sync. Next.js 16, FastAPI, Terraform. Production infrastructure from day one.
🚀 Production Proof
Paralegent AI — Contract & Compliance Intelligence (AWS)
23 AI agents score 12 legal and regulatory categories. 75% LLM cost reduction via smart routing. Circuit breakers and two-tier retry. Results delivered inside Microsoft Word Add-in.
FamilyOffice.ai — Investment Intelligence (GCP)
7 Google ADK agents. 8-stage document intelligence pipeline. Neo4j knowledge graph. 70+ API endpoints. Vertex AI Search. $850M AUM family office client.
Brady AI Co-Pilot — Active Client, $3,200/week (GCP)
Hybrid RAG on Qdrant. 584 curated chunks. LLM-as-Judge quality validation. Embedded in client's LMS via iframe. Repeat engagement.
VectorHire — AI Recruiting Platform
4 parallel AI agents analyze resumes, LinkedIn, and GitHub, then conduct voice interviews. 85% faster hiring, 92% accuracy, 300+ candidates screened per hour. 100+ ATS integrations. 3-day time-to-hire vs 6 weeks manual.
Marketplace Monitor — Active Client, $56K Total, 5.0 Rating (Azure)
3 microservices on Azure. 16 marketplace scrapers. 2,000 users. Auto-scaling 2-40 replicas. 97% DB load reduction. Repeat client.
VORTA — AI Customer Support
92% first-call resolution. 10,000+ tickets/month. 22 languages. Replaced 24-person team. $400K annual savings.
⚙️ Tech Stack
• Agents: Google ADK, AWS Bedrock AgentCore, LangGraph, LangChain, CrewAI, MCP
• LLMs: GPT-4o, Claude, Gemini 2.0/2.5, Llama, LiteLLM multi-provider
• RAG: Qdrant, Pinecone, OpenSearch, ChromaDB, Neo4j GraphRAG, Vertex AI Search
• Cloud: AWS (ECS, Lambda, Bedrock, CDK), GCP (Cloud Run, Firestore, Terraform), Azure (Container Apps, Bicep)
• Backend: FastAPI, Python 3.11, Pydantic v2
• Frontend: Next.js 16, React 19, TypeScript
• Reliability: Circuit breakers, two-tier retry, LLM-as-Judge, distributed rate limiting
💬 Message me with your use case and I will send you a free architecture sketch within 24 hours — how I would build it, what it costs to run, and a timeline to production.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Intake
MCP Context Build