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
AI Engineer | Multi-Agent Systems, RAG & Vertex AI | Dynamics 365
100%
Job Success
Lahore, Pakistan - 8:03 pm local time
Then there is the part almost nobody does. Most AI engineers can't touch an ERP, and most ERP consultants can't build AI. I live in the overlap. I take AI and optimization into Microsoft Dynamics 365 and the Supply Chain side most people avoid: copilots and plain-language answers over your ERP data, warehouse and routing optimization, and automated workflows that run inside the system your operations team already uses.
Around 10 years building production software, the last few deep in AI, across AWS, GCP, and Azure. If it doesn't run in production, it doesn't count.
WHAT I DO BEST
- AI Agents in Production: LangGraph, Google ADK, AWS Bedrock AgentCore, supervisor routing, typed tool contracts, human-in-the-loop approval, and full audit trails
- RAG and Document Intelligence: hybrid search, GraphRAG on Neo4j knowledge graphs, per-field confidence extraction, and citations on every answer, built for legal, fintech, and ERP data
- Fixing broken AI: RAG that hallucinates, agents that fail under load, and inference bills that are too high. Evaluation harnesses and LLM-as-judge quality gates, guardrails, and token-cost engineering (I've cut client AI spend 75%)
- AI and optimization inside your ERP, especially Microsoft Dynamics 365 with Azure AI and Power Apps: copilots, plain-language answers over ERP data, warehouse and route optimization, and automated workflows inside the system you already run
- Document intelligence: read, classify, extract, and validate data from contracts, financial documents, and forms
- AI built into a product you already use: your site, LMS, CRM, even Microsoft Word
PROOF, VERIFIABLE ON THIS PROFILE
An investment platform for a family office. Investment and legal documents (PPMs, SPAs, cap tables) become validated structured data, linked in a Neo4j knowledge graph. 7 AI agents answer in plain English, source attached, behind role-based access. It runs in production on Google Cloud with Vertex AI and Gemini.
Contract review inside Microsoft Word. A contract intelligence platform that checks a vendor contract against your playbook, with 23 AI agents scoring every clause across 12 legal categories, flagging risky language, and suggesting fixes. A full review takes 5 to 10 minutes instead of hours, right inside Word where lawyers already work. It runs in production on AWS, and smart routing cut the AI cost 75%.
A live AI co-pilot for K-12 writing teachers, embedded in their LMS. Teachers ask in plain language and get a classroom-ready lesson in seconds, grounded in their own curriculum by hybrid RAG, with an LLM-as-judge scoring every lesson on a 100-point rubric before it ships. Active client.
AI and optimization inside an enterprise ERP. For an automotive-parts manufacturer with multi-region warehouses, I optimized inventory slotting and picker routes, then optimized freight routes and wired it directly into their Microsoft Dynamics 365 ERP, so it runs inside the system their operations team already uses.
HOW I WORK
I take the requirements, make the technical calls, and hand back working software that runs, not a list of problems. I scope before I build: architecture and acceptance criteria first, milestones tied to tests passing, weekly demos, and everything documented and handed over so your team owns it.
URGENT FIXES
Broken RAG or a misbehaving agent system? I take fixed-scope diagnosis-and-repair engagements: full pipeline diagnosis, root cause with evidence traces, and fixes proven against a golden set built from your real queries. Diagnosis ships in days, not weeks.
TECH I WORK WITH
Agents: Google ADK, AWS Bedrock AgentCore, LangGraph, LangChain, LlamaIndex, CrewAI, MCP
LLMs: Claude, GPT-4o/5, Gemini, Llama, Amazon Nova, LiteLLM routing, Ollama for on-prem
RAG and data: Qdrant, Pinecone, Weaviate, pgvector, Neo4j, Elasticsearch, Vertex AI Search, hybrid search, GraphRAG
Cloud and ERP: AWS, GCP, Azure, Microsoft Dynamics 365, Power Apps, Docker, Kubernetes, Terraform
I keep a few consultation slots open each week for architecture, cost, and feasibility reviews. If you want a senior read on your system before committing budget, book one, or invite me to your job and I'll respond within hours.
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