You will get production-ready AI Agents and Multi-Agent Systems
Project details
Most AI agent projects fail in production — not because the idea was wrong, but because the engineering wasn't built to hold up under real load, real data, and real failure scenarios.
Mundus Dynamics builds agentic AI systems that actually ship. We specialise in multi-agent orchestration (LangGraph), agentic RAG pipelines with 94%+ retrieval precision, MCP tool integration, and the production infrastructure — FastAPI, RabbitMQ, Celery, Redis, PostgreSQL — that keeps everything running at 99.99% uptime.
We've delivered across fintech, healthcare, and enterprise SaaS: an invoice reconciliation swarm that cut costs by 80%, a clinical document RAG system hitting 94% precision under HIPAA constraints, and a support triage agent resolving 81% of tickets automatically.
We don't hand off demos. We hand off production systems with full documentation, observability, and a 30-day support window — and you own 100% of the code.
If your project needs to work in the real world, not just in a notebook, let's talk.
Mundus Dynamics builds agentic AI systems that actually ship. We specialise in multi-agent orchestration (LangGraph), agentic RAG pipelines with 94%+ retrieval precision, MCP tool integration, and the production infrastructure — FastAPI, RabbitMQ, Celery, Redis, PostgreSQL — that keeps everything running at 99.99% uptime.
We've delivered across fintech, healthcare, and enterprise SaaS: an invoice reconciliation swarm that cut costs by 80%, a clinical document RAG system hitting 94% precision under HIPAA constraints, and a support triage agent resolving 81% of tickets automatically.
We don't hand off demos. We hand off production systems with full documentation, observability, and a 30-day support window — and you own 100% of the code.
If your project needs to work in the real world, not just in a notebook, let's talk.
What's included
| Service Tiers |
Starter
$750
|
Standard
$2,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 2 | 5 |
Number of Scenarios | 3 | 10 | 25 |
Number of Graphs/Charts | 2 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
About Md Abdul
AI Agent developer | LLM, RAG, LangGraph, LangChain
Dhaka, Bangladesh - 6:39 am local time
While others are still wiring up basic chatbots, I'm architecting hierarchical multi-agent systems — orchestrators delegating to specialized sub-agents, reasoning through MCP Servers, grounding decisions in Knowledge Graphs, and recovering from failures through structured feedback loops. This is production agentic AI, not weekend projects.
I've spent the last 6+ years shipping backend and agentic systems across fintech, healthcare, and enterprise SaaS — working with cross-functional teams across the US, EU, and APAC. I've contributed to systems that process millions of events daily, serve thousands of concurrent users, and operate under SOC 2 and HIPAA-adjacent compliance constraints. I know what "production-ready" actually means because I've been on-call when things break.
What I build for clients:
🤖 Agent Swarms & Multi-Agent Systems — Supervisor-worker hierarchies in LangGraph with memory, parallel tool execution, and inter-agent delegation. Designed for complex, branching workflows that a single LLM call could never handle.
🧠 Agentic RAG Pipelines — Dual-track ingestion for structured and unstructured documents, semantic chunking, hybrid search (dense + sparse), reranking, and retrieval tuning that returns the right chunk — not just the closest one.
🔌 MCP Server Architecture — Agents connected to real tools: live databases, third-party APIs, internal knowledge bases, file systems. Proper tool-calling design with fallback handling and observability baked in.
⚡ High-Throughput Event Pipelines — FastAPI backends engineered for millions of daily events. I've resolved thundering-herd bottlenecks, designed distributed ingestion layers, and scaled databases through sharding and data mart separation.
🏗️ Production Infrastructure — RabbitMQ, Celery, Redis, Docker, CI/CD. The unglamorous layer that keeps agentic systems from falling apart at 3am.
My stack: Python · LangGraph · FastAPI · AWS Bedrock · PostgreSQL · Snowflake · Redis · RabbitMQ · Docker · Golang · LangSmith · Pinecone · Qdrant
Past work includes:
Agentic pipeline replacing a 12-person manual ops team — 80% reduction in processing time
RAG system for a legal-tech client — precision improved from 61% to 94% after retrieval tuning
Multi-agent customer intelligence platform — processing 3M+ records weekly with zero human-in-the-loop
Voice AI agent with real-time WebSocket streaming — deployed across 4 enterprise clients in under 3 months
If you need an agent that holds up in production, a RAG system built for precision, or a multi-agent pipeline that scales without babysitting — I'm your person.
Steps for completing your project
After purchasing the project, send requirements so Md Abdul can start the project.
Delivery time starts when Md Abdul receives requirements from you.
Md Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Architecture Brief
Review your requirements, audit existing stack, define agent boundaries and data contracts. Deliver a written architecture plan within 48 hours for your approval before any code is written.
Data & Tool Integration
Connect agents to your data sources — document ingestion, vector store setup, API integrations, and MCP tool layer. All connections tested and verified before moving forward.