You will get a Production RAG Pipeline with Hallucination Prevention & Hybrid Search
Top Rated
Project details
Most RAG systems fail in production, not because the concept is wrong but because the implementation skips the hard parts, semantic chunking, retrieval precision, & output validation. A chatbot that returns confident wrong answers is worse than no chatbot at all. I build RAG pipelines that are accurate, reliable, & production-ready. BusinessPulse.ai, a live analytics platform used by clients including Mediacom (WPP), runs on the same architecture I will build for you.
What makes this different from a basic RAG build, the validation layer. Every output is checked against expected schemas & source confidence before returning. Wrong answers do not reach your users.
What makes this different from a basic RAG build, the validation layer. Every output is checked against expected schemas & source confidence before returning. Wrong answers do not reach your users.
AI Algorithms
Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, AI-Generated Code, Anomaly Detection, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, BERT, BLOOM, ChatGPT, Dolly, GPT-3, GPT-4, Jurassic-2, LLaMA, Naive Bayes Classifier, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$2,500
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
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.
Additional Revision
+$150
Admin Dashboard for Knowledge Base Updates
(+ 3 Days)
+$500
Deployment to GCP or AWS
(+ 4 Days)
+$1,000
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About Aqeel
Senior AI Engineer | LLM & RAG | LangGraph | Voice AI | OpenClaw
100%
Job Success
Lahore, Pakistan - 6:11 pm local time
𝟑𝟐 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 | 𝟏,𝟓𝟗𝟏 𝐡𝐨𝐮𝐫𝐬 | 𝟏𝟎𝟎% 𝐉𝐒𝐒 | 𝐓𝐨𝐩 𝐑𝐚𝐭𝐞𝐝 𝐏𝐥𝐮𝐬 🏆
𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 👨🏻💻
🤖 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 & 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡
Multi-agent systems built with LangGraph and LangChain, autonomous workflows where AI agents collaborate, make decisions, and execute tasks end-to-end. From financial AI platforms for hedge funds to healthcare automation pipelines, every system ships to production with real data, not just demos.
🎙️ 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭
Production voice agents that handle real inbound & outbound calls, built on Convoi.AI, Twilio, Whisper & ElevenLabs. Deployed for healthcare, hospitality, home services & GCC enterprise clients. Handles multilingual conversations, live CRM integration & seamless human handoff, zero latency, zero hallucination.
🔍 𝐑𝐀𝐆 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐋𝐋𝐌 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
Retrieval-Augmented Generation systems that answer questions from live business data, not hallucinations. Built with hybrid search (BM25 + dense retrieval), pgvector, Pinecone & custom reranking pipelines. Production deployments across legal, financial & healthcare verticals.
⚙️ 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬
End-to-end automation pipelines connecting AI models to real business systems, CRMs, ERPs, databases & third-party APIs. Built on Apache Airflow, n8n, Make & LangGraph. Replaces manual processes with intelligent, self-monitoring workflows that scale.
🧠 𝐆𝐫𝐚𝐩𝐡 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 & 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐌𝐋
Anomaly detection, threat intelligence & predictive modeling using Graph Neural Networks, deployed for a US cybersecurity firm handling advanced threat detection at scale. Research foundation from neurodegenerative disease datasets at DZNE Magdeburg, Germany.
🏗️ 𝐀𝐈 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 & 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠
End-to-end AI system design for startups & enterprises, from selecting the right stack to production deployment strategy. MSc in Data Science (Otto-von-Guericke University, Germany) & 7+ years building production AI systems across finance, healthcare, retail & cybersecurity.
🌐 𝐓𝐨𝐨𝐥𝐬 & 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 🌐
👾𝐀𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: LangGraph, LangChain, OpenAI, Claude, Gemini, Groq, Whisper, ElevenLabs, Twilio, Ollama, OpenClaw
֎ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈: Stable Diffusion, ControlNet, LoRA/DreamBooth Fine-tuning, ComfyUI, DALL-E
☁️ 𝐂𝐥𝐨𝐮𝐝: AWS SageMaker, GCP Vertex AI, Azure, BigQuery, Snowflake, Databricks, S3, Redis
🗄️ 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: PostgreSQL, pgvector, Pinecone, MongoDB, MySQL
🔂 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Apache Airflow, n8n, Make, Zapier, GoHighLevel, Bitrix24, FastAPI, Docker
📊 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Power BI, Looker, Tableau, dbt, Dataform, PySpark, Talen, Keboola
👁️ 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧: OpenCV, PyTorch, TensorFlow, YOLO
Looking to reduce manual workload, cut operational costs & scale with production-ready AI, not just prototypes? 𝐋𝐞𝐭'𝐬 𝐓𝐚𝐥𝐤 🗪
Describe your use case & I'll outline a production-ready approach within 24 hours. Message me or send an invite to get started.
Keywords
Automation, n8n, Python, Artificial Intelligence, Machine Learning, Deep Learning, open ai, Next.js
AI App Development, AI Agent Development, API Integration, JavaScript, React, SaaS, Node.js, LLM Prompt Engineering, API, Microsoft Azure, anthropic, OAuth
Steps for completing your project
After purchasing the project, send requirements so Aqeel can start the project.
Delivery time starts when Aqeel receives requirements from you.
Aqeel works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Architecture Design
Review client documents, define chunking strategy per document type, select embedding model, design retrieval architecture & output schema before writing any code.
Data Ingestion & Vector Database Setup
Build document ingestion pipeline, clean & chunk content semantically, embed & index into Pinecone or pgvector, validate retrieval accuracy on sample queries.