You will get an enterprise RAG chatbot for your business documents
Project details
Your team spends hours searching documents for answers that should take seconds.
I'll build an AI assistant that reads your documents, understands your business, and answers questions with cited sources.
What you get:
Document ingestion: PDF, DOCX, TXT, Markdown, CSV
Hybrid retrieval: semantic + keyword search combined
Cited answers with confidence scoring
Google Drive sync connect your existing files
React or Streamlit chat UI embeds on any website with one script tag
Multi-provider: Groq (free), Claude, GPT-4, Gemini PostgreSQL persistence conversations saved across sessions
Docker Compose one command to run everything
Full documentation and 30-day support
I'll build an AI assistant that reads your documents, understands your business, and answers questions with cited sources.
What you get:
Document ingestion: PDF, DOCX, TXT, Markdown, CSV
Hybrid retrieval: semantic + keyword search combined
Cited answers with confidence scoring
Google Drive sync connect your existing files
React or Streamlit chat UI embeds on any website with one script tag
Multi-provider: Groq (free), Claude, GPT-4, Gemini PostgreSQL persistence conversations saved across sessions
Docker Compose one command to run everything
Full documentation and 30-day support
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Large Language Model, YOLOAI Applications
AI Chatbot, Conversational AI, Natural Language Understanding, Object DetectionAI Development Language
PythonAI Tools
Hugging Face, PyTorch, StreamlitAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$100
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 2 | 4 | 7 |
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.
Fast Delivery
+$30 - $500
Additional Revision
+$50Frequently asked questions
About Kidus
AI Systems Engineer | RAG Pipelines |Document Intelligence | LLM apps
Addis Ababa, Ethiopia - 7:32 pm local time
two seconds.
I specialise in two things that most AI developers treat as afterthoughts: reliability and architecture.
WHAT I BUILD
▸ Enterprise RAG Chatbots
AI systems that answer questions from your company's own documents — policies, support tickets, product manuals, contracts — with source citations, confidence scoring, and zero hallucination tolerance. Deployable as a web app, a Slack bot, or an embeddable widget on your existing site.
One script tag. No rebuild required.
▸ Document Extraction Pipelines
Structured data extraction from directories of PDFs and images at scale. Upload 500 invoices, get back a clean CSV with vendor names, totals, dates, and line items — reviewed, flagged, and correctable by a human before download.
Works with local OCR (no API cost) or cloud providers.
▸ Multi-Provider LLM Backends
Model-agnostic systems that work with Claude, GPT-4, Gemini, Groq, and local Ollama/llama.cpp. Swap providers by changing one environment variable. No vendor lock-in.
No redeployment. No code changes.
▸ Document Intelligence Integrations
Connect to Google Drive, AWS S3, Dropbox, SharePoint, OneDrive, FTP/SFTP, or local directories. Process files wherever they already live. No migration required.
TWO PROJECTS THAT SHOW HOW I THINK
▸ Enterprise RAG Chatbot
- A production-grade knowledge base chatbot with hybrid retrieval (vector + BM25), cross-encoder reranking, PostgreSQL persistence, conversation memory, Google Drive sync, confidence scoring, and a React UI with streaming responses. Domain-agnostic — the same codebase serves a
law firm, a hospital, and an e-commerce brand, configured via YAML with zero code changes per client.
▸ OmniTrace OCR v4
A source-aware document extraction pipeline using a two-stage local OCR architecture: GLM-OCR (ranked #1 on OmniDocBench, 0.9B params) extracts raw text from PDFs and images; Qwen3 structures it to schema. Human-in-the- loop review for flagged rows. Per-field confidence scoring.
Incremental writes — safe on crash. Seven data source connectors. Every file tracked, no silent failures.
HOW I'M DIFFERENT
✦ Clean Architecture by Default
Every system follows strict dependency inversion — core interfaces, adapter layer, application logic, thin API shell. That means any component is swappable, every layer is testable, and you own the codebase completely.
Your team can extend it without calling me.
✦ No Vendor Lock-in, Ever
LLM provider, vector store, database, file source —all abstracted behind interfaces. Switching from Chroma to Pinecone, or Postgres to MongoDB, touches one file.
✦ Production Guarantees Built In Confidence scoring, hallucination filtering, human review
flows, incremental writes, graceful error handling, Docker Compose one-command startup, structured logging, health checks, rate limiting, API key auth — from day one.
✦ Local-First When It Makes Sense
Local OCR (GLM-OCR + llama.cpp) runs with zero API cost and outperforms models 10× its size on document tasks. Cloud providers for when you need scale or quality.
You choose per run. No commitment.
WHAT YOU GET
→ Working software, not a proof of concept
→ Full source code with clean, readable git history
→ Architecture documentation: system design, data flow,
layer responsibilities, decision log
→ API documentation: every endpoint, request/response
examples, error codes
→ Setup guide: from clone to running in under 10 minutes
→ 30-day post-delivery support
Steps for completing your project
After purchasing the project, send requirements so Kidus can start the project.
Delivery time starts when Kidus receives requirements from you.
Kidus works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Kickoff
Review client's document types, business domain, and tech stack. Confirm LLM provider preference and delivery environment (cloud or on-premise). Share .env.example and setup checklist.
Environment Setup
Scaffold project structure, configure Docker Compose, set up PostgreSQL and ChromaDB, verify all provider connections are working.