You will get Production RAG System: Chat With Your Documents (LLM + Vector DB)
Project details
You'll get a production RAG system that lets users ask questions and get accurate, sourced answers from your own documents — not generic AI guesses.
Most "AI chatbot for your docs" builds fail in two places: poor chunking and no grounding, so the bot invents answers. I focus on both. Your documents are parsed and chunked properly, embedded, and stored in a vector database, so retrieval returns genuinely relevant context — and every answer cites its source, so your team can trust and verify it.
You'll receive a working pipeline (ingestion → embedding → retrieval → generation), a clean API your app can call, source citations on every answer, and a short handover doc. I match the model and vector database to your accuracy, latency, and budget needs rather than defaulting to the most expensive option.
7+ years building Python and AI systems. Let's scope it on a quick message.
Most "AI chatbot for your docs" builds fail in two places: poor chunking and no grounding, so the bot invents answers. I focus on both. Your documents are parsed and chunked properly, embedded, and stored in a vector database, so retrieval returns genuinely relevant context — and every answer cites its source, so your team can trust and verify it.
You'll receive a working pipeline (ingestion → embedding → retrieval → generation), a clean API your app can call, source citations on every answer, and a short handover doc. I match the model and vector database to your accuracy, latency, and budget needs rather than defaulting to the most expensive option.
7+ years building Python and AI systems. Let's scope it on a quick message.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Azure Machine Learning, BERT, ChatGPT, fastText, Google AutoML, GPT-3, Keras, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, SciPy, Sonnet, Stanford CoreNLP, TensorFlow, TextBlob, Vertex AI, Word2vecWhat's included
| Service Tiers |
Starter
$450
|
Standard
$950
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 0 | 1 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | |||
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $300
Additional Revision
+$60
Source Code
(+ 2 Days)
+$150Frequently asked questions
3 reviews
(3)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
SE
Sean E.
Jun 7, 2026
Looking for Senior AI Backend Engineer – Conversational AI Platform
Jazay was great to work with again. He brings a strong mix of technical expertise, adaptability and clear communication, and was a trusted partner throughout the project. He stayed solution oriented from scoping through delivery, took ownership of outcomes, and kept things moving without overcomplicating the process. Would highly recommend reaching out and leaning on him for your project.
SE
Sean E.
Sep 13, 2024
Chatbot Pt 3
Jazay provides a rare combination of expertise, adaptability and quality of work. We built a dynamic LLM chatbot with custom logs, admin panels, prompt inputs - and Jazay was a trusted partner from scoping to go-live. Would highly recommend reaching out and leaning on him for your project!
SE
Sean E.
Jun 15, 2024
Chatbot Pt 2
Jazay provided a rare combination of expertise and high quality execution. He provided detailed, realistic and reasonable scopes of work - and helped build an LLM architecture with multiple complex requirements. Highly recommend choosing Jazay if you’re looking for a trustworthy development partner.
About Jazay
Generative AI Developer | LLM & RAG Systems | Python Backend
100%
Job Success
Peshawar, Pakistan - 5:16 am local time
CORE STRENGTHS
LLM Integration: OpenAI, Claude, Gemini, and open-source models wired into real products, not demos
RAG Systems: retrieval pipelines with vector databases (Qdrant, Pinecone, Weaviate) for accurate document Q&A and semantic search
AI Agents: autonomous systems with multi-step reasoning, tool calling, and API execution
Production Deployment: AWS, Docker, and CI/CD for reliable, maintainable systems
Business focus: every build ties to a measurable outcome — lower response times, reduced manual work, faster retrieval
ABOUT ME
I'm a backend and AI engineer with 7+ years of professional experience building systems that solve real business problems. My work spans large language models, RAG, and full-stack Python development. At TalentPop I led AI initiatives across property tech and customer-service automation, shipping production systems used daily by real teams. My approach pairs solid engineering with business sense I want to understand the outcome you're after before I write a line of code.
SERVICES I DELIVER
LLM Integration & RAG Systems: Retrieval-Augmented Generation pipelines combining vector databases with language models for accurate document Q&A, knowledge retrieval, and context-aware responses.
Custom AI Agent Development: Autonomous systems capable of multi-step reasoning, API integration, and real-time decision execution. Ideal for lead qualification, support automation, and workflow orchestration.
Production Chatbot Development: Multi-channel conversational AI (web, WhatsApp, Telegram) with NLP understanding, sentiment analysis, and intelligent responses for customer engagement.
End-to-End ML Solutions: Predictive models and computer vision systems deployed with monitoring and retraining pipelines for continuous improvement.
PROBLEMS I SOLVE
Manual, repetitive support work → AI systems that handle routine conversations automatically and free up your team
Unstructured data nobody can search → RAG systems enabling fast semantic search across large document sets
Workflows that need constant manual oversight → AI agents that execute multi-step processes reliably
Raw data sitting unused → ML models that turn it into usable predictions and insight
TECH STACK
Languages: Python, TypeScript, JavaScript, Node.js
Web: FastAPI, Flask, Django, Nest.js
AI/ML: LangChain, Hugging Face, TensorFlow, PyTorch, Scikit-Learn
LLMs: OpenAI, Anthropic Claude, Google Gemini, RASA
Vector DBs: Qdrant, Pinecone, Weaviate
Data: PostgreSQL, MongoDB, Redis, Apache Kafka
Cloud/DevOps: AWS, GCP, Docker, Kubernetes, Terraform, GitHub Actions
Let's talk about what you're trying to build.
Steps for completing your project
After purchasing the project, send requirements so Jazay can start the project.
Delivery time starts when Jazay receives requirements from you.
Jazay works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & document review
We confirm scope on a short call, and I review your documents and example questions to plan the right chunking and retrieval approach.
Connect the LLM & add citations
I wire up the language model to answer using only retrieved context, with source citations on every answer so results are verifiable.