You will get a RAG knowledge base with cited answers and search
Project details
I build RAG knowledge-base assistants for teams that have useful docs but do not trust generic chatbots. The assistant ingests approved sources, retrieves evidence, and answers with citations so people can see where an answer came from.
Depending on scope, I can wire this to docs, PDFs, websites, GitHub repos, or internal notes. I usually add the boring but important pieces too: source refresh, confidence flags, eval questions, low-confidence handling, and handoff notes, so the system is useful after the first demo.
Depending on scope, I can wire this to docs, PDFs, websites, GitHub repos, or internal notes. I usually add the boring but important pieces too: source refresh, confidence flags, eval questions, low-confidence handling, and handoff notes, so the system is useful after the first demo.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| 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
About Skandesh
AI Engineer & Fullstack Developer | LLM Integration
Chennai, India - 11:07 am local time
I've helped 10+ businesses put AI into production -- not just a demo, but systems that actually run.
I handle the full stack: AI strategy, RAG systems, LLM integration, data pipelines, and fullstack development (Next.js, Python, FastAPI, LangChain, AWS, OpenAI).
If you need someone who finds the problem, builds the solution, and sticks around to make it work -- let's talk.
Steps for completing your project
After purchasing the project, send requirements so Skandesh can start the project.
Delivery time starts when Skandesh receives requirements from you.
Skandesh works on your project following the steps below.
Revisions may occur after the delivery date.
Confirm sources and answer goals
Review source types, sample questions, privacy rules, deployment target, and what the assistant should refuse or flag.
Build ingestion, retrieval, and UI
Set up ingestion, chunking, embeddings or search, cited answer flow, and the agreed UI or API for the selected tier.