You will get an AI agent that automates your workflow with real tool calling
Project details
An agent that only chats is a demo. An agent that reads the request, pulls the record, updates the system and reports back is a hire.
I build LLM agents that take real actions: calling your APIs, querying your database, writing to your tools, with the boring parts handled properly. Retries, rate limits, cost caps, full logging, and a human approval gate on anything irreversible.
I start by mapping your workflow the way you would explain it to a new employee, then decide honestly which steps should be an LLM and which should just be code. Most AI automation projects fail because everything got handed to the model: slow, expensive and unpredictable. I do not build them that way.
You get the agent, the tool integrations, a run log showing exactly what it did and why, and deployment on your infrastructure.
Built with Python or Node, LangChain, OpenAI or Claude tool calling, PostgreSQL and Redis.
Tell me the workflow and I will tell you whether it is worth automating.
I build LLM agents that take real actions: calling your APIs, querying your database, writing to your tools, with the boring parts handled properly. Retries, rate limits, cost caps, full logging, and a human approval gate on anything irreversible.
I start by mapping your workflow the way you would explain it to a new employee, then decide honestly which steps should be an LLM and which should just be code. Most AI automation projects fail because everything got handed to the model: slow, expensive and unpredictable. I do not build them that way.
You get the agent, the tool integrations, a run log showing exactly what it did and why, and deployment on your infrastructure.
Built with Python or Node, LangChain, OpenAI or Claude tool calling, PostgreSQL and Redis.
Tell me the workflow and I will tell you whether it is worth automating.
AI Algorithms
Large Language ModelAI Applications
AI-Generated Code, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$65
|
Standard
$299
|
Advanced
$799
|
|---|---|---|---|
| Delivery Time | 6 days | 14 days | 28 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 Kiran
Full-Stack Developer (React/Next.js/Node) | AI, RAG & LLM Apps
Severn, United States - 11:40 pm local time
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WHAT I BUILD
▸ Full web applications, end to end — React / Next.js front ends on Node, Django, or FastAPI
▸ Responsive, high-performance UIs — TypeScript, Tailwind, clean component architecture
▸ APIs & backends — REST and GraphQL, auth, payments, third-party integrations
▸ AI features that ship — chatbots, RAG pipelines, and AI agents grounded in your own data
▸ The production layer most freelancers skip — Docker, CI/CD, cloud deploy, monitoring
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MY STACK
Frontend — React, Next.js (App Router), TypeScript, Tailwind CSS
Backend — Node.js, Express, NestJS, Python, Django, FastAPI
Databases — PostgreSQL, MongoDB, Redis
AI — OpenAI & Claude APIs, LangChain, RAG, vector DBs (pgvector, Pinecone)
DevOps — Docker, GitHub Actions, AWS, Vercel, CI/CD
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HOW I WORK
1. A short call to pin down the outcome you need — not just the feature list
2. A written scope with milestones, so you know what lands and when
3. Working software early and often — you see progress weekly, not just at the end
4. Handover with documentation, so your team isn't dependent on me afterward
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I take on a few projects at a time and reply fast — usually within a couple of hours. Send me your 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 Kiran can start the project.
Delivery time starts when Kiran receives requirements from you.
Kiran works on your project following the steps below.
Revisions may occur after the delivery date.
Workflow mapping
We walk the task step by step and agree exactly what the agent owns and what it does not.
Build the tools
The API calls and database actions the agent is allowed to make, and nothing beyond them.