Intelligent AI Agents for Business Support & Customer Service

pro

Muhammad Hassan

5

(13)

Starting at

$

30

/hr

About this service

Summary

We offer a comprehensive AI agent solution that integrates Azure for hosting and DevOps, Flask APIs for microservices, OpenAI and Gemini for advanced LLM capabilities, and vector databases for semantic search. Our end-to-end approach includes custom model fine-tuning, production deployment, and thorough documentation and training, ensuring businesses can effectively leverage AI for customer support and data-driven insights.

Process

1. Requirements & Design
Requirement Gathering
Identify the key use cases (e.g., customer support, internal knowledge base) and the desired AI capabilities.
Discuss user flows, brand guidelines, and any specific compliance or security needs.
Figma Mockups
Create wireframes and high-fidelity designs in Figma to visualize the user interface (UI) and user experience (UX).
Collaborate with stakeholders to finalize the layout, color scheme, and interactions.
2. Data Preparation & Model Strategy
Collect & Clean Data
Gather relevant text, FAQs, support tickets, or domain-specific knowledge.
Clean and label data where necessary to ensure high-quality model training and fine-tuning.
Choose Model and Approach
Determine which LLM(s) to use: OpenAI GPT, Gemini, or other suitable models.
Identify whether you need out-of-the-box usage, fine-tuning, or prompt engineering to achieve desired accuracy.
3. Infrastructure & Backend Setup
Azure Configuration
Set up Azure resources: App Service (or Container Apps), Azure Functions, Azure Storage, Key Vault, etc.
Configure CI/CD pipelines in Azure DevOps or GitHub Actions to automate builds and deployments.
Vector Database Integration
Choose and configure a vector database solution (e.g., Milvus, Pinecone) for semantic search and relevant retrieval of embeddings.
Set up schema and index strategies for storing and retrieving vector representations of your data.
Flask Microservices
Build a Python Flask backend that handles requests to your AI services.
This backend orchestrates calls to the LLM API (OpenAI, Gemini), retrieves relevant embeddings from the vector DB, and returns structured responses.
4. AI Model Implementation & Fine-Tuning
Initial LLM Integration
Connect your Flask API to OpenAI, Gemini, or any other chosen LLM.
Implement prompt engineering techniques or lightweight tuning as needed.
Fine-Tuning (Optional)
If deeper customization is required, prepare training datasets and run fine-tuning jobs for your LLM (e.g., with OpenAI’s GPT-4).
Validate the model’s performance on a test set and iterate to improve results.
Embedding & Semantic Search
Generate embeddings (via OpenAI Embeddings API or other libraries) for your text data.
Store and query these embeddings in the vector database to support context-aware responses.
5. Frontend Development with React
Scaffold React Application
Create a new React project (e.g., using Vite or Create React App).
Integrate design specs from Figma: implement layouts, buttons, forms, color themes, and typography.
Implement Chat UI
Build or import a chat interface component.
Handle streaming responses from the backend (e.g., via Server-Sent Events, WebSockets, or chunked fetch).
State Management & API Calls
Use React hooks or a state management library (Redux, Recoil, Zustand, etc.) to manage user input, conversation context, and errors.
Configure HTTP or WebSocket connections to the Flask API endpoints.
6. Testing
Unit Tests (Vitest or Jest)
Write isolated tests for React components (render output, state changes, props) and backend Flask routes (basic functionality, edge cases).
Use mocking to simulate responses from external services (OpenAI, Gemini, vector DB) where possible.
Integration & E2E Tests (Cypress)
Cypress E2E: Spin up the entire system (React frontend + Flask backend) and run real-browser tests.
Verify the user flow from opening the app, seeing the welcome screen, entering a question, receiving AI responses, and ensuring all UI elements behave correctly.
Continuous Integration (CI)
Integrate tests into your CI pipeline (Azure DevOps, GitHub Actions, etc.) to run on every commit/pull request.
Enforce test coverage thresholds and ensure no regressions.
7. Deployment
Azure Deployment
Package and deploy the React frontend (e.g., to Azure Static Web Apps or a container in App Service).
Deploy Flask microservices to Azure App Service or Container Instances.
Set up environment variables for your LLM keys (OpenAI, Gemini) and vector DB credentials in Azure Key Vault.
Monitoring & Logging
Configure Azure Monitor or Application Insights to track usage, performance metrics, and errors for both the frontend and backend.
Set up alerts for downtime or high error rates.
Scaling Strategy
Use Azure’s auto-scaling features to handle increased traffic and concurrency for both the Flask backend and the vector DB.
Optimize cost vs. performance by tuning instance types, scaling rules, and resource allocation.
8. Documentation & Training
Technical Documentation
Provide instructions on how to run, maintain, and extend the system.
Include guidelines for future model fine-tuning, adding new data, or updating prompts.
User Training & Handover
Offer training sessions or create tutorials for the client’s teams to manage and operate the solution.
Walk them through the Figma designs, code repositories, and deployment process.
Post-Launch Support
Provide ongoing support for bug fixes, updates, or new feature requests.
Monitor performance, usage patterns, and continuously refine the AI model to meet changing business needs.
Final Outcome
By following this process, you’ll deliver a robust, end-to-end AI agent that:
Visually aligns with Figma designs.
Runs on Azure with a Flask microservice backend.
Leverages advanced LLMs (OpenAI, Gemini) for intelligent conversations.
Provides semantic search via vector database.
Ensures quality through unit and E2E testing with Cypress.
Remains scalable and maintainable through proper DevOps practices and documentation.

What's included

  • Azure-Based Infrastructure Setup

    Provision and configure Azure resources (App Service, Azure Functions, Azure Storage, etc.) to host and scale your AI agent system. This includes setting up Azure DevOps pipelines for continuous integration and delivery

  • OpenAI LLM Integration

    Integrate OpenAI’s large language models (e.g., GPT-4) to enable natural language understanding and generation for chatbots, customer support automations, or knowledge base Q&A. Includes prompt engineering and performance tuning.

  • Flask API for Chatbot & Microservices

    Develop a lightweight Flask backend that exposes RESTful endpoints for AI-based services. This API will handle incoming requests, coordinate with various ML/LLM modules, and return responses to frontend or third-party systems.

  • Gemini Integration for Advanced AI Features

    Leverage Google’s Gemini or similarly advanced AI platforms for enhanced reasoning, multi-modal features, and complex analytics. Integrate Gemini’s APIs into your existing pipeline for superior accuracy and performance.

  • Vector Database Implementation

    Deploy and configure a vector database (e.g., Milvus, Pinecone, or similar) to store and retrieve embeddings. This database supports semantic search and allows your AI agents to quickly identify relevant information from large corpuses.

Example projects

A Voice-Enabled Mortgage Loan Chatbot - React, Flask & OpenAI

Recommendations

(5.0)

Ben Smith

Client • Jan 7, 2025

Recommended

Muhammad is a talented developer with an eye for detail. He was able to quickly identify weak parts in the code base and provide elegant solutions that will make things work better in the long term.

Shubham Naithani

Client • Jul 22, 2024

Recommended

Hassan has been an exceptional freelance developer. His technical skills, professionalism, and attention to detail are outstanding. He consistently delivers high-quality, maintainable code and meets deadlines. Hassan communicates effectively, is proactive in solving problems, and is always open to feedback. His positive attitude and dedication make him a pleasure to work with. We highly recommend Hassan and look forward to continuing our partnership with him.

Recommended

Hassan did an outstanding job on the project, showcasing great skill and dedication. His work was high-quality, and he was a pleasure to work with. I highly recommend Hassan for any future projects—his contributions were invaluable.


Client • Jul 29, 2024

Recommended

Hassan excelled in integrating voice with our chatbot, mastering both frontend and backend. His skill and dedication ensured seamless functionality and enhanced user experience. Great work!


Client • Nov 26, 2024

Recommended

Shubham recommends working with Muhammad


Client • Sep 25, 2024

Recommended

Shubham recommends working with Muhammad


Client • Aug 15, 2024

Shannon Stevens • Chairlaxed

Client • Jul 27, 2024

Recommended

Hassan constructed my website a few months ago. He did everything I asked in a timely manner with the utmost professionalism. It’s a complicated site with e-commerce capability and he knew exactly what to do. I love the website and will definitely use him again.

Recommended

Hassan does fantastic work. He built my website and does all my changes.


Client • Aug 22, 2024

Recommended

Muhammad Hassan is the only person I have work on my site. He’s fantastic.


Client • Aug 24, 2024

Recommended

Muhammad Hassan always delivers for me. Excellent work.


Client • Aug 31, 2024

Rayyan Zuberi

Client • Jul 26, 2024

Recommended

Muhammad Hassan was a highly skilled and very professional developer who delivered more than he had committed and did so in a very reasonable price. Highly Recommended !

Muhammad Hassan

Client • Jul 23, 2024

Recommended

I have worked with Hassan on a good number of projects & have found him to be a very skillful developer & an amazing person to work with. Apart from having a wide range of technical skills including knowledge of modern tech stack, he also possesses soft skills like project management which proved to be very helpful when working with him. All I can say is he is a great developer & knows what he's doing which is evident from his huge list of past satisfied clients.

Recommended

I have worked with Hassan on a good number of projects & have found him to be a very skillful developer and an amazing person to work with. Apart from having a wide range of technical skills including knowledge of modern tech stack, he also possesses soft skills like project management which proved to be very helpful when working with him. All I can say is he is a great developer & knows what he's doing which is evident from his huge list of past satisfied clients.


Client • Jul 24, 2024

Skills and tools

AI Agent Developer
AI Chatbot Developer
AI Developer
Azure
Flask
Google Gemini
LangChain
OpenAI

Industries

Artificial Intelligence (AI)
Customer Service
Software Engineering

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