You will get I will build a custom RAG chatbot over your documents with LangChain
Project details
Need a RAG chatbot or AI system built with LangChain, connected to your own data? I build production ready AI systems, not demos.
What I Offer:
Custom RAG Development: RAG pipelines using LangChain and OpenAI, built on real production experience (I shipped one live with 300+ paying users)
Vector Search Integration: Semantic search across your documents using pgvector, tuned for relevance and speed
Full-Stack Delivery: API and app integration using FastAPI, Next.js, or React Native, whatever fits your existing setup
Clear Documentation: Setup, usage, and handoff docs so your team can maintain it after delivery
I've built this exact kind of system before at scale, RAG search across 12,000+ items, plus enterprise search tooling used by 50+ law firms in a past role.
Ready to get started? Send me your project details and I'll tell you honestly what's realistic for your timeline and budget.
What I Offer:
Custom RAG Development: RAG pipelines using LangChain and OpenAI, built on real production experience (I shipped one live with 300+ paying users)
Vector Search Integration: Semantic search across your documents using pgvector, tuned for relevance and speed
Full-Stack Delivery: API and app integration using FastAPI, Next.js, or React Native, whatever fits your existing setup
Clear Documentation: Setup, usage, and handoff docs so your team can maintain it after delivery
I've built this exact kind of system before at scale, RAG search across 12,000+ items, plus enterprise search tooling used by 50+ law firms in a past role.
Ready to get started? Send me your project details and I'll tell you honestly what's realistic for your timeline and budget.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$450
|
Standard
$900
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 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 |
Optional add-ons
You can add these on the next page.
Additional document source
+$200
Rush delivery (halve the timeline)
+$250
30 days post-launch support
+$300Frequently asked questions
About Canberk
AI Search Engineer | RAG, LangChain, pgvector | Shipped to Paying User
Berkeley, United States - 2:52 pm local time
Most retrieval projects demo beautifully and degrade quietly once the corpus grows. I know because I hit it. Building semantic search across 12,000+ documents, pure vector search kept missing exact-phrase queries users actually typed. I ended up combining pgvector cosine similarity with trigram text matching, and the hybrid approach fixed relevance in a way neither did alone.
That system is live in ActorRise, an AI platform I built and shipped solo. 300+ users, paying subscribers, running on a production ETL pipeline that handles scraping, content analysis, and batch embedding generation. Next.js and FastAPI on Supabase.
Before going independent I spent three years at Prevail Legal AI, where search accuracy was a compliance issue, not a nice-to-have. I built transcript search that let attorneys navigate 10,000+ line documents 3x faster, used across 50+ enterprise law firms.
What I can help with:
RAG pipelines: ingestion, chunking strategy, embeddings, hybrid retrieval
AI agents and chatbots: LangChain, LangGraph, OpenAI, Claude
Fixing RAG that already exists but returns bad answers
Full-stack around it: FastAPI, Next.js, React, Postgres, Supabase
I've taken two products from empty repo to paying users. If your project fits, send me the brief and I'll tell you honestly whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Canberk can start the project.
Delivery time starts when Canberk receives requirements from you.
Canberk works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff
Review your requirements and sample data you provided at purchase
Build
Set up the RAG pipeline, ingest your data, generate embeddings, connect retrieval to the LLM