You will get LLM features integrated into your live app or product
Project details
I add reliable AI features to your existing product using OpenAI, Claude, or Gemini, built into your backend so they work smoothly and cost-effectively.
You have a working product and want AI features like summarizing, drafting, smart search, or a copilot, but your team may lack AI experience and worry about reliability and cost.
You'll get the AI feature you need, integrated into your app or API, with streaming responses, prompt design for consistent output, error handling, and fallback logic. I can add multi-provider support so a single outage or price change doesn't break your feature.
I work with Python, Django or FastAPI, LangChain, OpenAI, Claude, Gemini, and gateways like LiteLLM or OpenRouter for routing and fallback. I work within your existing codebase and follow your conventions, testing against real inputs and adding safeguards so output stays on topic and safe.
API keys stay server-side, with input validation and safe handling of user data throughout.
Tell me about your product and the feature you want, and I'll recommend the best model and approach for your budget.
You have a working product and want AI features like summarizing, drafting, smart search, or a copilot, but your team may lack AI experience and worry about reliability and cost.
You'll get the AI feature you need, integrated into your app or API, with streaming responses, prompt design for consistent output, error handling, and fallback logic. I can add multi-provider support so a single outage or price change doesn't break your feature.
I work with Python, Django or FastAPI, LangChain, OpenAI, Claude, Gemini, and gateways like LiteLLM or OpenRouter for routing and fallback. I work within your existing codebase and follow your conventions, testing against real inputs and adding safeguards so output stays on topic and safe.
API keys stay server-side, with input validation and safe handling of user data throughout.
Tell me about your product and the feature you want, and I'll recommend the best model and approach for your budget.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4What's included
| Service Tiers |
Starter
$600
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 20 days |
Number of Revisions | 2 | 3 | 2 |
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.
Fast Delivery
+$150 - $500
Additional Revision
+$100
Additional AI feature
(+ 3 Days)
+$350
Multi-provider fallback setup
(+ 3 Days)
+$400
Streaming responses
(+ 2 Days)
+$250About Dhruv
RAG, Agentic AI & Multi-Agent Developer | LangChain, LangGraph, LLMs
Ahmedabad, India - 4:41 pm local time
I'm a Python and AI backend developer who works on real, live LLM systems, not just demos. On a production RAG SaaS product, I built the document ingestion pipeline, set up embeddings, and implemented real-time question answering with source citations so users could verify every response. I also added multi-provider LLM support, allowing conversations to continue even if one provider becomes unavailable or costs increase. My focus is on building AI systems that are accurate, reliable, and ready for production.
𝐖𝐇𝐀𝐓 𝐈 𝐂𝐀𝐍 𝐇𝐄𝐋𝐏 𝐘𝐎𝐔 𝐖𝐈𝐓𝐇
1. RAG Chatbots & Knowledge Assistants
Answer from your own documents with citations using hybrid retrieval (BM25 + dense embeddings) and reranking. I also build structured data extraction pipelines for invoices, contracts, forms, and other business documents.
2. Agentic AI & Multi-Agent Systems
Build LangGraph-based agents with tool calling, human approval workflows, and multi-step task automation for complex business processes.
3. LLM Integration for Your App or Product
Integrate OpenAI, Claude, and Gemini with streaming responses, multi-provider fallback, and production-ready backend architecture.
𝐓𝐎𝐎𝐋𝐒 𝐀𝐍𝐃 𝐒𝐓𝐀𝐂𝐊 𝐈 𝐔𝐒𝐄
• Backend: Python, Django, Django REST Framework, FastAPI, Node.js
• Frontend: React.js
• AI / Agent Frameworks: LangChain, LangGraph, n8n
• Async & Data: Celery, Redis, PostgreSQL
• Vector Databases: Milvus, Qdrant, pgvector
𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊
Every successful AI project starts with understanding the business problem, not choosing the latest framework or model.
I first understand your data, users, and business goals before deciding on the right retrieval or agent architecture. Then I build the solution step by step, validate it with real-world scenarios, and prepare it for production with proper logging, monitoring, and error handling.
Security is part of the implementation from day one, including API key management, document access control, and protection against prompt injection. Throughout the project, I communicate clearly, share regular progress updates, and raise potential risks early so there are no surprises later.
You get clean, documented code, an architecture that's easy to maintain, and AI systems that behave predictably when real users arrive.
If you're building a RAG assistant, an agentic AI agent, or a multi-agent system. Send me a short note about what you're building and the outcome you want. I'll tell you how I'd approach it, what challenges I see, and what's realistically achievable.
Steps for completing your project
After purchasing the project, send requirements so Dhruv can start the project.
Delivery time starts when Dhruv receives requirements from you.
Dhruv works on your project following the steps below.
Revisions may occur after the delivery date.
Review your product and target feature
I review your product, tech stack, and the AI feature you want, so the integration fits how your app already works.
Design the integration and prompts
I design how the feature fits into your backend, along with the prompts and output format needed for consistent results.