You will get I will build an AI agent to automate your workflow with LangChain
Project details
You've got a repetitive workflow eating your team's time: answering the same questions, moving data between tools, chasing updates. An AI agent can take it over.
I build agents that actually do the work, not chatbots that just talk. The agent reads the context, decides what to do, calls the right tools (your CRM, email, Slack, WhatsApp, calendar, database, or any API), and completes the task end to end. It loops until the job is done, not just started.
I build production AI for a living. My own platform runs an agent live for 300+ paying users, so I know the difference between a demo that works once and a system that keeps working when an API times out or an input is malformed. Every build includes error handling, retries, and logging so it holds up in the real world.
Starter covers a single-task agent with one integration. Standard adds multi-step reasoning, tool use, and 2 to 3 integrations. Advanced is a full multi-channel system with monitoring and handoff docs.
Tell me the workflow you want gone and I'll tell you honestly whether an agent is the right tool, and what it would take. Built with LangChain, LangGraph, Python, OpenAI, and Claude.
I build agents that actually do the work, not chatbots that just talk. The agent reads the context, decides what to do, calls the right tools (your CRM, email, Slack, WhatsApp, calendar, database, or any API), and completes the task end to end. It loops until the job is done, not just started.
I build production AI for a living. My own platform runs an agent live for 300+ paying users, so I know the difference between a demo that works once and a system that keeps working when an API times out or an input is malformed. Every build includes error handling, retries, and logging so it holds up in the real world.
Starter covers a single-task agent with one integration. Standard adds multi-step reasoning, tool use, and 2 to 3 integrations. Advanced is a full multi-channel system with monitoring and handoff docs.
Tell me the workflow you want gone and I'll tell you honestly whether an agent is the right tool, and what it would take. Built with LangChain, LangGraph, Python, OpenAI, and Claude.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$400
|
Standard
$850
|
Advanced
$1,600
|
|---|---|---|---|
| Delivery Time | 4 days | 8 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 |
Optional add-ons
You can add these on the next page.
Additional Integration (CRM, Slack, WhatsApp, etc.)
+$200
Rush delivery (halve timeline)
+$250
30-day support & tuning
+$300Frequently asked questions
About Canberk
AI Search Engineer | RAG, LangChain, pgvector | Shipped to Paying User
Berkeley, United States - 12:28 am 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.
Scope & design
Map your current workflow, confirm the triggers, tools, and end result. Agree on exactly what "done" means.
Access & setup
Get access to the accounts and APIs, set up credentials and the project skeleton securely.