Introduction

AI chatbots leverage Generative AI to provide intelligent, context-aware responses. A hybrid approach is often used where predefined intents and FAQ-based responses are prioritized, and the AI model is engaged when no intent match is found. This ensures efficiency while allowing the chatbot to handle complex queries dynamically.

Implementation Process

1. Data Collection

Relevant data sources are gathered to form the chatbot’s knowledge base, including:

• PDFs, webpages, and structured documents such as CSV, JSON.
• Client-specific information relevant to the chatbot’s domain.
• Original documents such as company policies, medical guidelines, or financial information.

2. Data Preprocessing

• De-identification & Image Removal: For privacy compliance, sensitive data is removed from certain datasets.
• Automated Preprocessing: Python scripts process the files, ensuring they only contain de-identified text.
• Standardized Formatting: Text is extracted, cleaned, and structured for indexing, ensuring uniformity.

3. Data Storage

All processed documents are stored in cloud-based storage such as Azure Blob Storage, AWS S3, or Google Cloud Storage.

• Supported formats for indexing include CSV, HTML, JSON, PDF, TXT, and Microsoft Office formats (Word, PPT, Excel).
• Each chatbot implementation has a dedicated storage container to keep knowledge base documents organized.

4. Index Creation

To optimize retrieval efficiency, an index is created using Azure OpenAI Studio, ElasticSearch, VectorDB or other AI search tools.

• The index is built by extracting text, chunking it into manageable sections, and saving these chunks for quick lookups.
• This allows the AI model to search and retrieve relevant information efficiently instead of processing entire documents at runtime.

5. Generative AI Model Deployment

• AI models such as GPT-3.5, GPT-4, or other LLMs are deployed via cloud-based services.
• Onboarding is required to access models, and quota limits can be adjusted based on usage needs.
• Embedding models can be utilized when implementing a vector search index for semantic search capabilities, especially when handling large datasets.

6. API Configuration

Once the search index is set up and the AI model is deployed, the Chat Completions API is configured:

• The chatbot is integrated with the search index and AI model to fetch relevant data.
• API calls are structured to retrieve data, maintain chat history, and generate summarized responses

Workflow of the AI Chatbot

1. User Query Processing: A user submits a question to the chatbot.
2. Search Index Retrieval: The query is sent to the search index, retrieving the top K text chunks based on similarity.
3. AI Model Response Generation: The retrieved text chunks, along with the user query and chat history, are fed into the AI model.
4. Summarized Answer & Citations: The AI model generates a contextual response, often including references to the original sources.
5. Response Delivery: The chatbot provides the generated response, along with links to cited documents when applicable.

Additional Clarifications on how to Efficiently build a chatbot leveraging different services

Document Storage & Access

• Documents are stored in cloud-based storage solutions and indexed in AI search services.
• The AI model retrieves indexed references and provides document URLs for users to access.
• If de-identification is applied, users will still be redirected to the original raw files in storage.

Handling Different File Types

• For HTML files: Instead of providing a document link, the chatbot can redirect users to a live webpage version through a middleware adjustment.
• For PDFs and other static files: Direct access to the indexed document is provided via cloud storage URLs.

Updating the Search Index

• New files are uploaded to cloud storage and must be manually indexed.
• The index must be recreated whenever updates, additions, or deletions occur.
• This process is not fully automated and must be executed manually to ensure updated data is available.

Customizing Search Performance

• Parameters in Azure AI Search or other indexing services can be modified to improve response accuracy.
• Adjustments include chunk size, ranking methods, and indexing frequency to optimize performance.

Quick Replies & Hierarchical Navigation

• Chatbots often use quick reply buttons (pickers) to guide users through hierarchical categories.
• These pickers must be manually configured within the chatbot framework to align with the conversation flow.

Document Storage & Index Mapping

Below is a structured example of chatbot storage, search services, and indexed knowledge bases:

Category	Resource Group	Storage Account	Container	Search Service	Index Name
Healthcare	healthcare-chatbot	healthcare-docs	health-data	healthcare -search – service	healthcare-index
Finance	finance-chatbot	finance-docs	finance-data	finance-search	finance-index
Pharma	pharma-chatbot	pharma-docs	pharma-data	pharma-search	pharma-index

Conclusion

Building an AI chatbot leveraging Generative AI involves data collection, preprocessing, indexing, and AI model deployment. By integrating a robust search retrieval mechanism and API-based response generation, chatbots can provide contextually relevant, accurate, and efficient responses to user queries.

Regular maintenance of the knowledge base and search index ensures chatbot responses stay up to date. Future enhancements may include embedding-based vector search, multimodal AI capabilities, and dynamic knowledge updates, enabling even more intelligent and scalable chatbot solutions.

About Author:

Thrushna Matharasi

Professional Experience

2021–Present: Director of Engineering, Data and AI at Solera

• Leads a team of 20+ engineers, analysts, and data scientists to drive innovation and revenue growth.
• Developed predictive analytics models using XEBoost optimizing device installation success rates.
• Developed a unified GPS position solution that consolidated position records from the Omnitracs Application Platform (XRS, SmartDrive, ES). Leveraged Kafka and Flume to ingest and stream data into the Data Lake, enabling multiple applications to utilize the data for reporting and analytics.
• Lead snowflake cost optimization project saving $500 K in cost savings.
• Integrated multiple Looker contracts, saving $1M annually and enabling multi-million-dollar revenue growth.
• Spearheaded GDPR compliance automation, ensuring data security and trust.

2019–2021: Lead Data Engineer at Digital Turbine

• Achieved 30% cost reduction by leading the migration from Vertica to AWS Redshift.
• Implemented Kafka streaming for real-time data processing and REST APIs using Scala.
• Reduced Redshift disk space by $220K and improved query performance by 40% using data lakehouse architecture (Bronze, Silver, Gold layers).

2015–2019: Big Data Architect at Kasasa

• Designed enterprise data models and automated marketing campaigns, improving hit ratios by 20%.
• Implemented AWS Data Pipelines and optimized SQL scripts for billing systems, enhancing efficiency.
• Worked on near real-time analytics projects, integrating diverse data sources into AWS S3 and Redshift.

2014–2015: Reporting Analyst at Janus Capital Group

• Developed complex dashboards and interactive reports using Cognos and SSRS, improving daily reporting efficiency.
• Streamlined relational database schemas and optimized SQL queries for performance enhancement.

2012–2014: Cognos Developer at BCBS NJ

• Designed data models and frameworks for enterprise reporting using Cognos 10.x.
• Automated reporting processes, reducing execution time and improving user experience.

Key Research and Publications

• “ “Developing a Context-Aware Analytical Tool using ML Algorithms for Safe Driving Speeds in Adverse Weather Conditions”” (IEEE ICMCSI 2025 – Paper Accepted with corrections).
• “A Comparative Analysis of Machine Learning Approaches for Stock Price Prediction “ (IEEE ICPCT2025, accepted with Edits, In Process of Submitting the Final Paper).

Key Presentations:

• Springer CST 2024: “Integrating Near Real-Time and Batch Data into Snowflake for Scalable Analytics.”

Leadership and Mentorship

• Current Company: Lead a 20+ Team at Spireon across USA, Europe and India Timezones
• Mentorship: Top 30% contributor on ADPList, mentoring individuals across 5 countries.
• Session Chair/Reviewer: Served as Session Chair for CST 2025 and Reviewed 6+ journal papers for IEEE and Springer conferences.

Core Skills

• Technologies: AWS, Scala, Kafka, Apache Spark, Jenkins, Snowflake, Redshift.
• Data Engineering: ETL/ELT pipelines, Data Warehousing (Kimball, Inmon, Data Vault), and streaming solutions.
• BI Tools: Tableau, Looker, Cognos, SSRS,Looker.
• Research Focus: AI, ML, and IoT applications in healthcare and finance.