You will get A Document Q&A AI Assistant with RAG and Source Attribution
Project details
I'll build a custom Document Q&A AI Assistant trained on YOUR company's documents internal wikis, product manuals, contracts, HR policies, technical documentation. Your team and customers ask questions in natural language and get accurate answers with full source citations.
WHY THIS MATTERS
Most "AI chatbots" hallucinate confidently making up policies that don't exist, inventing timelines, fabricating product details. My system is engineered specifically to prevent this:
Grounded answers only
Hybrid retrieval
Source attribution
WHAT YOU GET
Production-ready Python application
Streamlit web interface (deployable to your domain or Streamlit Cloud)
ChromaDB persistent vector store
Full source code on GitHub
Deployment guide
README with setup instructions
Loom video walkthrough explaining the system
USE CASES THIS WORKS FOR
HR departments answering employee policy questions
Customer support teams looking up product information
Legal teams searching contract libraries
Engineering teams navigating technical docs
Compliance teams verifying regulatory requirements
WHY THIS MATTERS
Most "AI chatbots" hallucinate confidently making up policies that don't exist, inventing timelines, fabricating product details. My system is engineered specifically to prevent this:
Grounded answers only
Hybrid retrieval
Source attribution
WHAT YOU GET
Production-ready Python application
Streamlit web interface (deployable to your domain or Streamlit Cloud)
ChromaDB persistent vector store
Full source code on GitHub
Deployment guide
README with setup instructions
Loom video walkthrough explaining the system
USE CASES THIS WORKS FOR
HR departments answering employee policy questions
Customer support teams looking up product information
Legal teams searching contract libraries
Engineering teams navigating technical docs
Compliance teams verifying regulatory requirements
AI Algorithms
Large Language ModelAI Applications
AI Mobile App DevelopmentAI Development Language
PythonAI Tools
StreamlitAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$300
|
Standard
$550
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 3 | 3 | 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 |
About Sharon
AI Engineer | Reliable Customer Support Bots, RAG & Automation
Nairobi, Kenya - 10:51 am local time
What I actually help businesses with:
Reliable AI agents - customer support bots, document assistants, and internal tools with confidence-based routing and quality review loops, so wrong answers don't reach your users.
RAG pipelines that actually retrieve - hybrid search, source attribution, and graceful failure handling, so your AI says "I don't know" instead of inventing an answer.
Automation with real validation - data pipelines that catch corrupt records, pricing errors, and silent failures before they hit your dashboards or customers.
Production systems, not prototypes - provider-flexible architectures (Open-AI, Anthropic, Groq, local models) with fallback built in, so your system keeps running when an API goes down.
How I work:
I scope projects honestly. If AI isn't the right solution for your problem, I'll tell you and suggest what is. I write documentation alongside code. I build human-in-the-loop checkpoints for decisions that matter. I don't hand over a black box.
Recent work:
1. A multi-agent customer support system with 5 specialized agents and a Generator-Critic quality layer that reviews responses before they reach users
2. A document Q&A assistant with hybrid retrieval that handles the structural questions pure semantic search consistently misses
3. A real-time pricing pipeline with anomaly detection that caught a critical $0 order bug standard validation missed entirely
If you're building something that needs to work reliably, message me with your stack, the problem, and what "done" looks like. I'll tell you honestly whether I'm the right fit and if I'm not, who probably is.
Steps for completing your project
After purchasing the project, send requirements so Sharon can start the project.
Delivery time starts when Sharon receives requirements from you.
Sharon works on your project following the steps below.
Revisions may occur after the delivery date.
Project kickoff & document review
Confirm scope, review your documents, identify any quality issues (scanned PDFs, structural complexity), align on example questions.
Document ingestion & vector store setup
Process your documents into searchable chunks, generate embeddings, build the ChromaDB vector store with metadata for filtered retrieval.