Custom AI Solutions: From Concept to Deployment by Debojit Choudhury

Custom AI Solutions: From Concept to Deployment

Debojit Choudhury

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About this service

Summary

I build custom AI applications (BERT/LLMs, FastAPI, Django) that solve real-world problems—from fraud detection to automated document processing. What sets me apart? Deployable, production-ready systems with proven results (e.g., 99% accuracy models, 40% cost reductions) and end-to-end ownership—from training to scalable APIs and interactive demos.

FAQs

What kind of AI models do you typically build?
I specialize in NLP solutions like text classification (e.g., fraud detection, sentiment analysis) and custom LLM fine-tuning (e.g., for chatbots, document processing). My projects often use BERT, DistilBERT —optimized for accuracy and speed.
How long does a typical project take?
Simple prototypes: 2–4 weeks (e.g., a Gradio demo + basic API). Full production systems: 4–8 weeks (includes data prep, model training, and scalable deployment).
Do you handle deployment, or just development?
Yes—I deliver fully deployed solutions tailored to your needs: Lightweight demos: Hosted for free on Hugging Face Spaces (Gradio/Streamlit). Production-ready APIs: Deployed via Render (scalable FastAPI/Django backends). Easy handoff: Docker setups or step-by-step guides if you prefer self-hosting.

What's included

Production-Ready AI Model
A fine-tuned BERT/LLM model (e.g., for text classification, fraud detection, or sentiment analysis) deployed via FastAPI, with documented accuracy metrics (e.g., 95%+ F1-score). Includes model weights, training scripts, and evaluation reports.
Scalable Backend API
A FastAPI or Django REST Framework (DRF) API endpoint to serve model predictions, optimized for low latency. Includes Swagger/OpenAPI documentation and load-testing results.
Interactive Web Demo
A Gradio/Streamlit web app showcasing the AI model’s capabilities, with UI for real-time predictions (e.g., upload CSV or text input). Demo link hosted on Hugging Face Spaces.
Comprehensive Documentation
Technical Report: Model architecture, dataset details, and performance benchmarks. User Guide: How to integrate the API (with Python/curl examples) and extend the model. Maintenance Plan: Steps to retrain the model with new data.
Source Code & Version Control
All code (Python scripts, Jupyter notebooks, config files) delivered via GitHub/GitLab repository with MIT/commercial license. Includes CI/CD pipeline (e.g., GitHub Actions) for automated testing.

Example projects