AxonBuild - AI & Full Stack Solutions

Introduction

We designed an intelligent conversational AI platform that evolved from simple data query tools into advanced multi modal, graph enhanced, retrieval augmented systems. By merging multiple projects into a single innovation narrative, this platform demonstrates how RAG powered chatbots can scale across industries, ranging from legal workflows to sales enablement, customer service, and complex knowledge management.

Challenge

Organizations across industries struggle with knowledge accessibility and customer engagement. Traditional chatbots are often rigid, rule based, and disconnected from external knowledge sources. Businesses needed solutions that ranged from no code builders for small teams to advanced graph powered RAG systems for enterprises handling complex data relationships.

Solution

We built a flexible and scalable chatbot ecosystem that integrates multiple capabilities.

Graph Based RAG Systems

For industries with highly relational knowledge, we integrated graph databases such as Neo4j and Dgraph. This allowed the platform to go beyond text retrieval and reason over entity relationships. Query accuracy reached 96 percent with context relevance at 91 percent.

No Code Chatbot Builder

Enabled non technical users to design conversational flows with drag and drop tools. Integrated RAG fallback ensured that when scripted logic was insufficient, the bot could retrieve external knowledge dynamically. Setup time was reduced by 90 percent and adoption rates reached 88 percent.

Generative Lead Generation Chatbot

Deployed conversational agents specialized in qualifying leads and answering product queries. These reduced sales funnel friction and achieved 78 percent lead conversion rates with average response times of 2 seconds.

CSV and Document Focused Query Systems

Developed tools that allowed conversational interaction with CSV datasets and bulk documents. Non technical users could ask natural language questions and receive insights instantly. Processing was up to 5 times faster and accuracy exceeded 90 percent across benchmarks.

RAG with OCR and Multi Modal Capability

Extended the platform to handle PDFs, scanned images, and other formats. OCR pipelines extracted content, which was then embedded for retrieval augmented queries. Multi LLM output comparison gave users confidence in extraction quality with accuracy levels near 94 percent.

Technical Approach

Generative AI:Multi LLM integration with ChatGPT and open source variants

Vector Databases:Pinecone, FAISS, Qdrant, Milvus for semantic search

Graph Databases:Neo4j and Dgraph for entity relationship reasoning

OCR Pipeline:Tesseract for document text extraction

RAG Framework:LangChain for retrieval augmented generation

Stack:Python backend, React/Next.js frontend, Docker containerization

What we've accomplished

Successfully created a versatile, industry agnostic conversational AI ecosystem that adapts to the needs of small service businesses as easily as it scales to enterprise knowledge management. The platform consolidates innovations from multiple projects into a unified architecture, creating a solution that is both accessible and sophisticated, redefining how organizations interact with data.

Results & Impact

89-96%

Accuracy Range

78%

Lead Conversion

5% faster

Processing Speed

88%

Platform Adoption

90%

Setup Time Reduction

Project Narrative

This platform represents the natural evolution of conversational AI. It began with simple CSV chatbots and expanded into document retrieval, OCR enhanced pipelines, and multi modal capabilities. Later iterations introduced no code builders for ease of adoption and graph powered RAG systems for complex relational reasoning. The result is a versatile, industry agnostic solution that adapts to the needs of small service businesses as easily as it scales to enterprise knowledge management. By consolidating innovations from multiple projects into a unified architecture, we created a conversational AI ecosystem that is both accessible and sophisticated, redefining how organizations interact with data.