You will get your LangGraph or CrewAI agent deployed to production at scale
Project details
Your AI agent works in notebooks but crashes under real traffic. Memory resets every session. No logging. No guardrails. Sound familiar?
I deploy LangGraph, CrewAI, and custom agents to AWS AgentCore or Google ADK, enterprise platforms purpose-built for production agents.
What you get:
→ Memory persistence across sessions (isolated, secure)
→ Full observability with agent-to-agent handoff tracing
→ Guardrails that block illegal/harmful requests automatically
→ Serverless Lambda or custom Kubernetes deployment
→ Architecture docs and team handover
Live example:
Financial Advisor on AgentCore with 3 specialized agents, Financial Advisory, Budget Planning, and Guardrails. User asks for retirement planning? Agents collaborate seamlessly. User asks about unreported offshore transfers? Guardrails block it instantly. Every interaction traced in CloudWatch with complete handoff visibility.
Framework-agnostic: LangGraph, CrewAI, Strands, or custom code.
Model-agnostic: Bedrock, OpenAI, Anthropic, open-source.
Why me:
• 100+ AI projects delivered
• 99.9% uptime across production
• Early adopter of enterprise agent platforms
I deploy LangGraph, CrewAI, and custom agents to AWS AgentCore or Google ADK, enterprise platforms purpose-built for production agents.
What you get:
→ Memory persistence across sessions (isolated, secure)
→ Full observability with agent-to-agent handoff tracing
→ Guardrails that block illegal/harmful requests automatically
→ Serverless Lambda or custom Kubernetes deployment
→ Architecture docs and team handover
Live example:
Financial Advisor on AgentCore with 3 specialized agents, Financial Advisory, Budget Planning, and Guardrails. User asks for retirement planning? Agents collaborate seamlessly. User asks about unreported offshore transfers? Guardrails block it instantly. Every interaction traced in CloudWatch with complete handoff visibility.
Framework-agnostic: LangGraph, CrewAI, Strands, or custom code.
Model-agnostic: Bedrock, OpenAI, Anthropic, open-source.
Why me:
• 100+ AI projects delivered
• 99.9% uptime across production
• Early adopter of enterprise agent platforms
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$5,000
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 3 | 9 |
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 |
Optional add-ons
You can add these on the next page.
Extended Support (+30 days)
(+ 1 Day)
+$1,500
Additional Agent Deployment
+$1,000
CI/CD Pipeline Setup
+$750Frequently asked questions
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PH
Patrick H.
Jul 18, 2025
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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.
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Patrick H.
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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.
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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.
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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.
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About Muhammad
Senior AI Engineer | Scalable AI Apps Multi-Agent | AI in Dynamics ERP
100%
Job Success
Lahore, Pakistan - 2:13 pm local time
You won't need a separate frontend developer, product manager, and QA engineer. I take the requirements, make the technical calls, own the features, and hand back working software, not a list of problems. If it doesn't run in production, it doesn't count. So I do the deeper production engineering that keeps a system reliable: harness engineering around the model, guardrails, evaluations and LLM-as-judge quality gates, model routing, and observability so you see problems before your users do. The result holds up at real scale and stays affordable to run.
What I can build for you
- Full-stack scalable AI solutions/Apps, end to end: one developer owns the AI, backend, frontend, UX, and the product calls, so you don't assemble a team
- AI agents and multi-agent systems that plan, take actions, and finish real tasks
- RAG, GraphRAG, and AI chat over your own documents, knowledge base, or data, with citations
- 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)
- AI and optimization inside your ERP, especially Microsoft Dynamics 365 with Azure AI and Power Apps: copilots, plain-language answers over your ERP data, and automated workflows inside the system you already run
- Regulated-domain AI for healthcare, finance, and legal: guardrails in code, audit trails, confidence scoring, human-in-the-loop
- Cutting AI running costs with model routing and prompt caching (I've cut client AI spend 75%)
Recent systems I've built
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%.
An investment platform for a $850M-AUM family office. Investment and legal documents (PPMs, SPAs, cap tables) become validated structured data, linked in a Neo4j knowledge graph. Seven AI agents answer in plain English: "What are our total obligations in this company?" comes back in seconds, source attached, behind role-based access. It runs in production on Google Cloud.
A live AI co-pilot for K-12 writing teachers, embedded in their LMS. Teachers ask in plain language and get a classroom-ready 4-step lesson in seconds, grounded in their own curriculum by hybrid RAG, and an LLM-as-judge scores 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. Bringing AI and optimization into Dynamics 365 is a lane I deliberately specialize in: most ERP consultants can't build the AI and most AI engineers can't touch the ERP.
One client trusted me with $56,000 of repeat work at a 5.0 rating.
How I work
I scope before I build. Every engagement starts with an architecture plan and acceptance criteria. Weekly demos, daily updates, a reply within a few hours. A working proof of concept in days, production in weeks. Two of my clients have worked with me for over a year, and I've delivered every contract without a single failure. The only work I'm not right for is a throwaway ChatGPT wrapper with no real product behind it. Anything that has to run in production and grow, that is my work.
Tech I work with
Agents: Google ADK, AWS Bedrock AgentCore, LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, 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
Full-stack: Next.js, React, TypeScript, FastAPI, Python, PostgreSQL
Cloud and ERP: AWS, GCP, Azure, Microsoft Dynamics 365, Docker, Kubernetes, Terraform
Tell me what you want to build and I'll send a one-page plan within 24 hours: how I'd build it, what it costs to run, and a timeline. If it's not a fit, I'll say so up front.
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 & Architecture Review
Review your existing code, understand requirements, and design production architecture with deployment platform recommendation
Environment Setup & Deployment
Configure cloud infrastructure, deploy your agent with memory persistence and session management across interactions