You will get a RAG document assistant with source citations
Rising Talent
Rising Talent
Project details
You have documents your team keeps re-reading to find the same answers: contracts, manuals, policy PDFs, internal wikis. A general chatbot cannot help, because it has never seen them.
I build systems that answer from your documents and show the source passage behind every answer.
The part most demos get wrong: answers that sound confident but are quietly invented are worse than no answer. Once someone catches a fabricated answer, they stop trusting the system. So this assistant cites the exact passage it used, and when the evidence is not there it says so.
I have a working deployment you can try - ask and I will send you an access token.
Delivered as a FastAPI backend you own, with a web interface or API integration. Standard includes Docker deployment; Advanced can deploy to your infrastructure. The LLM provider can be OpenAI or Claude, so you can choose the best fit.
Good for internal knowledge bases, support that answers from real documentation, and contract or policy lookup.
Not a fit if you want a general chatbot, or one that answers from live web data.
I build systems that answer from your documents and show the source passage behind every answer.
The part most demos get wrong: answers that sound confident but are quietly invented are worse than no answer. Once someone catches a fabricated answer, they stop trusting the system. So this assistant cites the exact passage it used, and when the evidence is not there it says so.
I have a working deployment you can try - ask and I will send you an access token.
Delivered as a FastAPI backend you own, with a web interface or API integration. Standard includes Docker deployment; Advanced can deploy to your infrastructure. The LLM provider can be OpenAI or Claude, so you can choose the best fit.
Good for internal knowledge bases, support that answers from real documentation, and contract or policy lookup.
Not a fit if you want a general chatbot, or one that answers from live web data.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$150
|
Standard
$900
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 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 Peerapat
AI Solutions Developer | Automation & RAG for Business Operations
Bangkok, Thailand - 10:11 am local time
I build production-ready AI systems, not one-off demos. This includes RAG knowledge assistants with source citations, AI workflow automation, internal tools, and integrations across messaging platforms, databases, APIs, and business systems.
My focus is reliability: clear permissions, structured and traceable outputs, human handoff where needed, error handling, automated tests, and maintainable code that your team can continue to use after launch.
I can take ownership from discovery and solution design through development, deployment, and ongoing improvement. My core stack includes Python, FastAPI, Next.js, n8n, PostgreSQL, Docker, OpenAI, and Claude.
Typical use cases:
• Internal knowledge assistants that answer from company documents with citations
• AI automation for documents, emails, OCR, forms, approvals, and data processing
• AI integrations for LINE, WhatsApp, Telegram, email, CRM, databases, and internal APIs
• Secure internal tools with user access control and human escalation
If you want to move an AI idea beyond a prototype into a practical system your team can rely on, send me your current workflow and desired outcome.
Steps for completing your project
After purchasing the project, send requirements so Peerapat can start the project.
Delivery time starts when Peerapat receives requirements from you.
Peerapat works on your project following the steps below.
Revisions may occur after the delivery date.
Ingest and index your documents
I chunk your PDFs and DOCX in a way that preserves section structure, so every citation later points back to a real place in the source rather than a floating fragment of text.
Build the retrieval and answer loop
Vector search finds the relevant passages, then the model answers from those passages only. If the evidence is not there, it says so instead of filling the gap with something plausible.