You will get powerful LLM chat bot for your custom data (pdf, txt, csv, ms doc)
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Project details
Key Project Components:
1) Custom LLM Model Usage: Leverage state-of-the-art Language Models to enhance the intelligence and natural language understanding of the chatbot.
2) Contextual Chatbot : Tune the model to work on corporate datasets, ensuring optimal performance and relevance.
3) Chat Interface: Design an intuitive and user-friendly chat interface for seamless interaction between users and the chatbot.
4) Open AI Models Usage: Explore and integrate OpenAI models to augment the capabilities of the chatbot, pushing the boundaries of conversational AI.
5) Custom Embedding Usage: Implement custom embeddings to enhance the representation of textual data, enabling more accurate responses and understanding.
6) Retrieval QA Addon (Document Search): Integrate a powerful Document Search feature using Retrieval Question-Answering (QA) to provide users with precise information.
Note: As per request, we can use best of breed GPT models or if privacy options are required, we can use privacy-enabled LLMs like LLAMA2 or Anthropic Claude.
1) Custom LLM Model Usage: Leverage state-of-the-art Language Models to enhance the intelligence and natural language understanding of the chatbot.
2) Contextual Chatbot : Tune the model to work on corporate datasets, ensuring optimal performance and relevance.
3) Chat Interface: Design an intuitive and user-friendly chat interface for seamless interaction between users and the chatbot.
4) Open AI Models Usage: Explore and integrate OpenAI models to augment the capabilities of the chatbot, pushing the boundaries of conversational AI.
5) Custom Embedding Usage: Implement custom embeddings to enhance the representation of textual data, enabling more accurate responses and understanding.
6) Retrieval QA Addon (Document Search): Integrate a powerful Document Search feature using Retrieval Question-Answering (QA) to provide users with precise information.
Note: As per request, we can use best of breed GPT models or if privacy options are required, we can use privacy-enabled LLMs like LLAMA2 or Anthropic Claude.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Machine Translation, Natural Language Generation, Natural Language Understanding, Neural Machine TranslationAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$100
|
Standard
$1,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 3 days | 30 days | 70 days |
Number of Revisions | 0 | 1 | 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 | - | - |
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About Vivek
Senior Full-Stack Engineer | GenAI, LLM, RAG, FastAPI, AWS | SaaS
95%
Job Success
Bengaluru, India - 9:00 am local time
Clients contact me when they face below:
• Their LLM feature works but isn’t stable
• A RAG pipeline needs proper backend integration
• An AI prototype must become production-ready
• Performance and scalability issues are emerging
• They need a senior engineer who can join an ongoing project and take ownership
I specialize in designing and deploying Gen AI systems integrated into clean full-stack architecture. My focus is not experimentation it’s production.
Core Specialization
• Generative AI application development
• Large Language Model (LLM) systems
• Retrieval-Augmented Generation (RAG) pipelines
• Agentic workflows (LangGraph-based orchestration)
• Vector database integration (Pinecone, Weaviate, FAISS)
• Structured output validation and evaluation systems
• SaaS backend architecture
• API-first full-stack engineering
• AWS cloud deployment
Planning & Product Structuring:
• Linear
• Framer AI
Engineering & Implementation:
• Cursor
• Claude Code
• GitHub Copilot
• Replit
Backend Architecture & API Layer:
• Python
• FastAPI
• PostgreSQL
• Supabase
• Firebase
• REST APIs
• OpenAPI
• Async Architecture
Frontend & Deployment:
• Tailwind CSS
• Modern Component Frameworks
• Vercel
Cloud Infrastructure & Scalability:
• AWS (S3, EC2, Lambda, App Runner, Bedrock)
• Docker
• CI/CD Pipelines
• Multi-Tenant SaaS Architecture
Monitoring & Production Reliability:
• Sentry
• Structured Logging
• Observability Systems
What I Build:
• LLM-powered SaaS features
• RAG-based knowledge systems
• Multi-step agent workflows
• Document intelligence and automation tools
• AI copilots integrated into existing platforms
• Full-stack SaaS platforms with Generative AI layers
• Backend restructuring for AI-enabled products
I work primarily with funded startups and mid-size product companies.
My Typical engagements include:
• Joining an active engineering team as senior backend/AI lead
• Hardening LLM and RAG systems before scale
• Designing full-stack architecture for new AI features
• Refactoring unstable AI integrations
• Leading backend modernization for SaaS products
I’m comfortable stepping into ongoing projects, reviewing codebases, and stabilizing systems quickly.
If you’re experimenting with AI concepts, there are many lower-cost options available.
If you’re building AI-enabled SaaS features that need to handle real users, real data, and real scale senior architecture matters.
I focus on long-term maintainability, measurable performance, and clean engineering.
Share your current stack, your AI roadmap, and the bottlenecks you’re facing. I’ll evaluate it and propose a structured approach.
Steps for completing your project
After purchasing the project, send requirements so Vivek can start the project.
Delivery time starts when Vivek receives requirements from you.
Vivek works on your project following the steps below.
Revisions may occur after the delivery date.
Review of the project
Data requirements review and expectations setting
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