You will get an autonomous Multi-Agent AI System for Research and Content Generation
Project details
I specialize in building production-grade Multi-Agent AI Systems using a professional Coordinator-Dispatcher architecture. Unlike basic ChatGPT wrappers, my solutions include integrated programmatic guardrails—specifically Before-Tool and After-Tool callbacks—to ensure data integrity, block restricted domains, and provide 100% audit transparency.
What sets this project apart:
Three-Tier Orchestration: Separation of concerns between the Brain (Coordinator), Logic (Guardrails), and Tasks (Dispatcher Agents).
Structured Persistence: Automated research synthesis into high-quality Markdown reports or Pydantic-validated data structures.
Reliability: I implement state management and session memory, allowing the system to handle complex, multi-step workflows without losing context.
Actionable Results: Whether you need real-time financial analysis, automated web research, or content generation like podcast scripting, this system is designed for high-stakes business environments.
What sets this project apart:
Three-Tier Orchestration: Separation of concerns between the Brain (Coordinator), Logic (Guardrails), and Tasks (Dispatcher Agents).
Structured Persistence: Automated research synthesis into high-quality Markdown reports or Pydantic-validated data structures.
Reliability: I implement state management and session memory, allowing the system to handle complex, multi-step workflows without losing context.
Actionable Results: Whether you need real-time financial analysis, automated web research, or content generation like podcast scripting, this system is designed for high-stakes business environments.
AI Algorithms
AlexNet, Convolutional Neural Network, Deep Belief Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Variational AutoencoderAI Applications
AI Content Creation, AI Text-to-Speech, AI-Generated Art, AI-Generated Music, AI-Generated Video, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language Understanding, Object Localization, Speech Synthesis, Text RecognitionAI Development Language
PythonAI Models
LLaMAWhat's included $250
These options are included with the project scope.
$250
- Delivery Time 3 days
- Number of Revisions 1
- Setup File
- Source Code
About Aima
AI & LLM Engineer | RAG | AI Agents | NLP | FastAPI | AWS
Islamabad, Pakistan - 2:02 pm local time
Who I Work With:
Founders building their first AI product who need a technical partner, not just a coder.
Funded startups transitioning from MVP to production with real users and real load.
Development teams integrating AI into existing systems without disrupting current operations.
Businesses seeking complete AI solutions—not just ChatGPT wrappers with a pretty UI.
Real Results:
RAG Document Intelligence System: 91% retrieval accuracy across 10,000+ documents, reducing query time from 35 minutes to under 2 seconds.
LLaMA 3 Fine-Tuning: 89% task accuracy vs. 66% base model (34% improvement), with a 71% reduction in hallucination rate, and 100Ă— cheaper than GPT-4 per token.
Multi-Agent Research System: Report generation time cut from 8 hours to 22 minutes using CrewAI + GPT-4 pipeline.
AI Voice & Chat Agent: 68% of tier-1 support queries resolved autonomously, reducing support workload by 61%.
Computer Vision Pipeline: YOLOv11 at 45 FPS on Jetson Nano edge device, achieving mAP@0.5 of 0.89 after TensorRT optimization.
AI SaaS MVP: Full platform delivered in 6 weeks, with document processing time reduced from 4 minutes to 18 seconds and 97.3% extraction accuracy.
What I Build:
LLM Fine-Tuning & AI Agents: Custom fine-tuning of LLaMA 3, Mistral 7B, Falcon, and GPT-4 via LoRA/QLoRA/PEFT/RLHF. Autonomous multi-agent systems with LangChain, LangGraph, CrewAI, MetaGPT, and AutoGen.
RAG Pipelines & Knowledge Systems: End-to-end retrieval-augmented generation using FAISS, Pinecone, ChromaDB, and Weaviate—connected to PDFs, databases, Notion, websites, or internal knowledge bases.
AI Chatbots & Voice Agents: GPT-4, Claude, and Gemini-powered conversational AI. Voice pipelines: Deepgram STT → LLM → ElevenLabs TTS. Multi-turn memory via LangChain and LangGraph.
Computer Vision Systems: Object detection (YOLOv8/11, Faster R-CNN), segmentation (SAM2, Mask R-CNN, U-Net), OCR (PaddleOCR, Tesseract), facial recognition (FaceNet, DeepFace), pose estimation (ViTPose).
AI Automation & Workflows: n8n, LangChain, and CrewAI pipelines connecting AI to your existing business tools, CRMs, databases, and APIs.
Full MLOps & Deployment: FastAPI REST APIs, Docker/Kubernetes, AWS SageMaker, GCP Vertex AI, Azure ML, edge deployment (Jetson Nano, Raspberry Pi). Experiment tracking: Weights & Biases, MLflow, DVC.
We Are a Good Fit If:
You have a defined AI problem and a clear success metric.
Your project budget is $500+ and outcome matters more than price.
You want a complete, deployed solution—not a prototype to hand off.
You are ready to start within the next 2 weeks.
We Are Not a Fit If:
You need a basic ChatGPT wrapper or template customization.
Price is the primary factor above quality and architecture.
Your project has no defined goal, user, or success metric yet.
Tech Stack:
LLMs: GPT-4/4o, Claude Opus/Sonnet, LLaMA 3, Mistral 7B, Falcon, Gemini, DeepSeek.
Agents & Pipelines: LangChain, LangGraph, CrewAI, MetaGPT, AutoGen.
RAG & Vector DBs: FAISS, Pinecone, ChromaDB, Weaviate, PGVector.
Computer Vision: YOLOv8/11, SAM2, Mask R-CNN, ViTPose, FaceNet, OpenCV.
Voice AI: Deepgram, ElevenLabs, Whisper, Amazon Polly.
MLOps: PyTorch, TensorFlow, Hugging Face, Weights & Biases, MLflow, DVC.
Deployment: FastAPI, Flask, Docker, Kubernetes, AWS, GCP, Azure, RunPod.
Automation: n8n, Zapier, LangChain, custom API pipelines.
Frontend: React.js, Next.js, Streamlit, Gradio.
Call to Action: 📩 Send me your project goal and rough timeline. I'll respond within 24 hours with: whether I can help, a rough scope and approach, and 2–3 clarifying questions. No sales pitch. Just a straight answer.
Steps for completing your project
After purchasing the project, send requirements so Aima can start the project.
Delivery time starts when Aima receives requirements from you.
Aima works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture & Knowledge Mapping
We define the specific roles for your Dispatcher agents (e.g., Research, Finance, Content) and map out the required tools and API integrations.
Custom Tool & Callback Integration
I develop custom Python tools for the agents and implement "Before-Tool" guardrails to filter sources and "After-Tool" callbacks to enhance response transparency.