You will get a production-ready RAG pipeline for your AI application
Top Rated
Top Rated
Project details
Struggling with an AI chatbot that gives outdated or irrelevant answers? The issue is usually the retrieval layer and thatโs exactly what I fix.
I build production-ready ๐น๐จ๐ฎ (๐น๐๐๐๐๐๐๐๐-๐จ๐๐๐๐๐๐๐๐ ๐ฎ๐๐๐๐๐๐๐๐๐) ๐๐๐๐๐๐๐๐๐ that connect your LLM to your real data (documents, databases, knowledge bases) so it responds accurately and reliably.
๐๐๐๐ ๐๐๐ ๐๐๐:
โ Custom RAG pipeline tailored to your use case
โ Vector DB setup (Pinecone, Weaviate, Chroma, or FAISS)
โ Data ingestion, chunking, and embeddings
โ LLM integration (GPT-4, Claude, Gemini, etc.)
โ Semantic or hybrid search with tuning
โ Cost and latency optimization
โ Clean code, docs, and deployment support
๐๐๐๐๐๐๐๐๐๐:
Built RAG systems for legal search, enterprise knowledge bases, customer support bots, and financial Q&A.
๐๐๐ ๐๐:
I focus on retrieval quality, chunking, and ranking because RAG is only as good as what it retrieves.
Message me before ordering for large or complex datasets.
I build production-ready ๐น๐จ๐ฎ (๐น๐๐๐๐๐๐๐๐-๐จ๐๐๐๐๐๐๐๐ ๐ฎ๐๐๐๐๐๐๐๐๐) ๐๐๐๐๐๐๐๐๐ that connect your LLM to your real data (documents, databases, knowledge bases) so it responds accurately and reliably.
๐๐๐๐ ๐๐๐ ๐๐๐:
โ Custom RAG pipeline tailored to your use case
โ Vector DB setup (Pinecone, Weaviate, Chroma, or FAISS)
โ Data ingestion, chunking, and embeddings
โ LLM integration (GPT-4, Claude, Gemini, etc.)
โ Semantic or hybrid search with tuning
โ Cost and latency optimization
โ Clean code, docs, and deployment support
๐๐๐๐๐๐๐๐๐๐:
Built RAG systems for legal search, enterprise knowledge bases, customer support bots, and financial Q&A.
๐๐๐ ๐๐:
I focus on retrieval quality, chunking, and ranking because RAG is only as good as what it retrieves.
Message me before ordering for large or complex datasets.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, StreamlitAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 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 |
Frequently asked questions
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HZ
Husn e Z.
Jul 1, 2026
Fix Hallucination & Embedding Drift in Production RAG System
Nailed the hallucination and drift fixes, and even threw in dashboards to monitor and reindex drift without being asked. Great communication, delivered on time. Would hire again in a heartbeat.
LN
Lucas N.
Jan 31, 2026
C# Developer Needed for Law Firm Case Management System
I really appreciate Aqdas's help and hope to work with him again in the future.
WS
We Think Beautiful S.
Dec 18, 2025
Next.js & Tailwind CSS Developer Needed for Website Build from Figma Design
Thank you again for your work!
FO
Fraser O.
Jan 18, 2025
Long Term C#/.NET programmer, full stack experience required
Aqdas worked full time for me for four months and did an excellent job. He was always on top of things, suggesting great ideas, going above and beyond and easy to work with. He has great skill in the technologies we worked with and is always focused on making sure I was happy with the work he did.
Highly recommend for your project, I will be hiring him again.
Highly recommend for your project, I will be hiring him again.
SJ
Sol J.
Jul 2, 2024
Full Stack Web Developer for a Financial Web Platform
It was fantastic working with Aqdas. He completed the job successfully and on time. I will gladly work with him again in the future if given the chance
About Aqdas
AI Developer | RAG, Voice Agents & LLM | Full-Stack .NET/Node/React
100%
Job Success
Lahore, Pakistanย - 11:48 pm local time
Most "AI developers" can build a prototype but can't own the product around it. Most full-stack engineers can ship the product but don't understand embeddings, retrieval drift, or agent architecture well enough to make AI features actually reliable. I do both, which means you're not hiring two people, or hiring someone who hands you a working notebook and disappears when it needs to become a real feature.
๐๐ก๐๐ญ ๐ ๐๐ฎ๐ข๐ฅ๐:
โ RAG pipelines (LangChain, LlamaIndex, pgvector, Pinecone, ChromaDB) including diagnosing and fixing hallucination and embedding drift in systems already in production
โ Voice agents and conversational AI (Hugging Face, OpenAI, Claude, ElevenLabs-style stacks)
โ LLM-powered agents with tool use, memory, and multi-step workflows (LangGraph, CrewAI, custom)
โ Full-stack integration around all of the above, FastAPI/Node backends, React/Angular frontends, .NET/C# where that's the existing stack, deployed on AWS, Azure, or GCP
๐๐ก๐ฒ ๐๐ฅ๐ข๐๐ง๐ญ๐ฌ ๐ค๐๐๐ฉ ๐ฆ๐:
โ Top Rated Plus, 100% Job Success Score, 28 completed jobs
โ 18+ years shipping production systems across SaaS, FinTech, Legal, and enterprise platforms
โ I ship working systems with monitoring and error handling, not just architecture diagrams, on my last RAG engagement I added drift-monitoring dashboards the client hadn't even asked for
โ Comfortable owning a feature end-to-end: model/pipeline choice, backend, frontend, and cloud deployment, so you're not coordinating three different specialists
Let's talk about what you're building.
Steps for completing your project
After purchasing the project, send requirements so Aqdas can start the project.
Delivery time starts when Aqdas receives requirements from you.
Aqdas works on your project following the steps below.
Revisions may occur after the delivery date.
Understand Your Data & Use Case
Review your data sources, use case, and expected outputs. Define success criteria and key queries your system must handle.
Data Ingestion & Chunking Strategy
Clean, structure, and chunk your data properly based on its type (PDFs, docs, structured data) to ensure high-quality retrieval.