You will get a FastAPI backend with LangChain AI integration and RAG
Project details
I build production-ready AI integration backends using FastAPI
and LangChain — the same stack I used to build a clinical
decision support system in collaboration with Universitas Gadjah
Mada, now used by nursing professionals for generating
evidence-based care plans.
What sets this apart: I don't just connect an API and call it
done. I architect the full pipeline — prompt engineering,
multi-agent orchestration (Flash/Medium/Pro tiers), RAG over
your knowledge base, PII sanitization, session management, and
clean REST endpoints your frontend can consume immediately.
I support all major LLM providers (OpenAI, Claude, Gemini, Groq)
with hot-swap via environment config — no vendor lock-in. Every
delivery includes source code, README, and a working local setup.
I'm a computer science student at Universitas Amikom Yogyakarta
(GPA 3.98) with real production experience, not just tutorials.
I communicate clearly, ask the right questions upfront, and don't
disappear mid-project.
and LangChain — the same stack I used to build a clinical
decision support system in collaboration with Universitas Gadjah
Mada, now used by nursing professionals for generating
evidence-based care plans.
What sets this apart: I don't just connect an API and call it
done. I architect the full pipeline — prompt engineering,
multi-agent orchestration (Flash/Medium/Pro tiers), RAG over
your knowledge base, PII sanitization, session management, and
clean REST endpoints your frontend can consume immediately.
I support all major LLM providers (OpenAI, Claude, Gemini, Groq)
with hot-swap via environment config — no vendor lock-in. Every
delivery includes source code, README, and a working local setup.
I'm a computer science student at Universitas Amikom Yogyakarta
(GPA 3.98) with real production experience, not just tutorials.
I communicate clearly, ask the right questions upfront, and don't
disappear mid-project.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Image Analysis, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$75
|
Standard
$175
|
Advanced
$350
|
|---|---|---|---|
| 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 Revision
+$15Frequently asked questions
About Abyan
AI-Powered Backend Engineer
Gunungkidul, Indonesia - 4:11 pm local time
building production-grade systems across healthcare AI, Web3,
and fintech.
Recent work includes a clinical decision support system (CDSS)
built in collaboration with nursing students at Universitas Gadjah Mada — featuring
a multi-agent swarm orchestration (LangChain), custom RAG pipeline
over clinical databases, and HIPAA-compliant PII sanitization. Also
built a fiat-to-blockchain investment platform (Node.js + BullMQ +
Polygon) and an on-chain skill verification system in Go deployed
to Monad Testnet.
I work primarily in Python (FastAPI), Go (Fiber), and
Node.js/TypeScript, with experience in AI provider integration
(OpenAI, Claude, Gemini, Groq), distributed job queues, and
smart contract interaction via go-ethereum and Ethers.js.
I'm honest about what I know and what I'm still learning — and
I communicate clearly throughout every project.
Steps for completing your project
After purchasing the project, send requirements so Abyan can start the project.
Delivery time starts when Abyan receives requirements from you.
Abyan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement analysis
I review your use case, clarify the scope, confirm the tech stack, and ask any questions needed before writing a single line of code.
Development
Build the FastAPI backend, LangChain pipeline, RAG setup, LLM integration, and all agreed endpoints with proper error handling and middleware.