Project Bid · AI Application

AI Text Analysis Application

A tailored proposal from a full-stack AI/ML engineer — delivering fast, accurate NLP pipelines and a polished user experience, from prototype to production.

Delivery 5–7 Business Days
Expertise NLP · ML · Full-Stack
Availability Immediate
Location Chennai, India (IST)
Cover Letter

Hi,

I'm a full-stack software engineer with hands-on experience building AI/ML-powered applications — from NLP pipelines and LLM integrations to real-time data analysis dashboards. I've shipped production systems using Python, FastAPI, and modern ML libraries (spaCy, Hugging Face Transformers, scikit-learn), and I understand how to make these systems fast, accurate, and easy for non-technical users to interact with.

For this project, I'd build a clean, end-to-end text analysis application that combines:

  • NLP core — sentiment analysis, entity recognition, keyword extraction, topic modelling, or classification depending on your use case
  • User-friendly UI — a responsive web interface (React or plain HTML + FastAPI backend) where users paste or upload text and get clear, visual results instantly
  • Fast processing — async task queuing (Celery/Redis or background workers) so even large documents return results quickly

I've previously built Rasa-based chatbot systems, AI agent frameworks, and Kafka-powered message routing — so complex, multi-step text pipelines are well within my comfort zone. I treat code quality, documentation, and deployment seriously: Docker-containerised, cloud-ready (AWS/Vercel), and fully tested.

Before I begin, I have a few quick questions below to ensure I scope and deliver exactly what you need. I'm available immediately and can share working demos as I build.

Looking forward to working with you.
Saran Kirthic SP

Relevant Skills & Stack
NLP & ML Models
Hugging Face, spaCy, NLTK, scikit-learn, LLM fine-tuning
Data Analysis
pandas, NumPy, text classification, keyword extraction, topic modelling
Backend API
Python, FastAPI, Flask, async processing, REST & WebSocket
Frontend UI
React, TypeScript, responsive dashboards, data visualization
Cloud & Deploy
AWS, Docker, Vercel, CI/CD pipelines, production-ready delivery
Fast Turnaround
Agile sprints, daily updates, working demo delivered within 5–7 days
Python FastAPI Hugging Face Transformers spaCy scikit-learn React / TypeScript PostgreSQL Docker AWS Redis / Celery LangChain Kafka
Estimated Delivery Timeline
Day 1
Scope & Architecture
Finalise requirements, choose NLP models, set up project structure, API schema, and database design
Day 2–3
Core NLP Pipeline
Build and test the text analysis engine — sentiment, NER, classification, keyword extraction; connect to FastAPI backend
Day 4–5
User Interface
Build the frontend — text input, file upload, results visualisation (charts, entity highlights, score cards)
Day 6
Testing & Optimisation
Accuracy testing, performance profiling, async job handling, and edge-case handling
Day 7
Deployment & Handover
Dockerised deployment to AWS/Vercel, documentation, and full source code handover with a live demo
Clarifying Questions

These will directly shape the architecture, model choice, and delivery scope:

1
What specific text analysis tasks do you need — sentiment analysis, named entity recognition, document classification, summarisation, keyword extraction, or a combination?
This determines whether I use a lightweight library (spaCy) or a heavier transformer model (BERT/GPT), and directly affects speed vs. accuracy trade-offs.
2
What is the typical volume and size of text being processed — single paragraphs, full documents, or bulk batch uploads (e.g., thousands of records)?
This decides whether I need async queuing (Celery/Redis), streaming results, or a simple synchronous API — a key architectural choice.
3
Do you have a preferred tech stack or platform for the UI — a standalone web app, integration into an existing system, or an API-only backend?
Determines frontend framework, deployment target, and whether I need authentication, multi-user support, or just a single-user interface.
4
Is the text domain general (news, social media, emails) or specialised (medical, legal, financial)? Do you have labelled training data if a custom model is needed?
Specialised domains often require fine-tuned models. Existing labelled data can dramatically improve accuracy and reduce development time.
5
What are your data privacy requirements — can text be sent to external APIs (like OpenAI), or must everything run locally / on your own servers?
This is a binary architectural split: cloud API (fast, low cost) vs. self-hosted open-source models (full privacy, higher compute). The answer changes the entire model selection strategy.

Ready to Build Together

Share your answers to the five questions and I'll send a detailed scope, fixed quote, and start immediately. Daily progress updates guaranteed.

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