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dbEddie AI Expert System
Pilot Study

Architectures That Power AI Transformation

To become data-driven, enterprises must invest in robust, scalable data platforms that ensure high-quality, trusted data enabling AI-driven insights, automation, and an increase in value generation. Democratized AI depends on unified data platforms that provide self-service access to analytics and ML tools. Data fabric and data mesh architectures are key enablers for AI at scale
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What we do?

Data is like oil, unusable in its raw state. Our dbEddie AI expert system helps companies better understand and use their SAP systems and interact with Knowledge repositories. dbEddie lets you chat with your systems and apps, making your metadata, programs, and data flows crystal clear.
Product Description

Supported Systems

Primary Sources
  • SAP S/4HANA®
  • SAP BW/4HANA®
  • Atlassian Jira & Confluence
On Roadmap
  • Microsoft Sharepoint
  • Google Workspaces
  • Service Now
On Roadmap
  • Snowflake
  • Databricks
  • dbt

Research Components

Vector embeddings
Transformer-based embeddings running on CPU and NVIDIA GPUs for optimal performance
LLM integration
Support for both cloud-based (OpenAI, Anthropic) and local GPU-accelerated models
Hybrid architecture
Combines semantic search with structured data access for precise answers
Streamlit web interface
Interactive chat interface with real-time data flow visualization
Time-Saving
Huge reduction in time spent understanding complex data flows
Knowledge Access
Democratized access to technical BW knowledge for business users
Smart Documentation
Enhanced documentation through automated meta data extraction and relationship mapping
Faster Development
Accelerated development by providing instant answers about existing implementations

Vector embeddings

Transformer-based embeddings running on CPU and NVIDIA GPUs for optimal performance. Vector embeddings transform words, sentences, images, or any data into numerical vectors in a high-dimensional space, capturing the semantic meaning and relationships between items. In simple terms: Vector embeddings convert complex data (like text) into lists of numbers that computers can process while preserving the meaning and relationships in the data.

01.Natural Language Processing

Powers machine translation, sentiment analysis, and text classification by converting words and sentences into meaningful numerical representations.

02.Recommendation Systems

Represents products, content, and user preferences as vectors to find similarities and make personalized recommendations.

03.Image Recognition

Converts images into vectors that capture visual features, enabling similar image searches and classification tasks.

04.Search Engines

Powers semantic search by matching query vectors with document vectors based on meaning rather than just keywords.

LLM Integration for Enterprise Systems

Large Language Models (LLMs) are revolutionizing how enterprises process information, automate tasks, and engage with customers. Integrating these powerful AI tools into existing enterprise systems creates unprecedented opportunities for innovation and efficiency.

Enterprise LLM Integration: The process of connecting large language models like GPT, LLaMA, or Claude with enterprise software systems, databases, and workflows to enhance business capabilities through natural language processing and generation.

01.Enhanced Productivity

Automate routine tasks like document summarization, email drafting, and information retrieval, freeing employees to focus on higher-value work.

02.Knowledge Discovery

Extract insights from vast repositories of unstructured data across enterprise systems, surfacing connections humans might miss.

03.Improved Customer Experience

Deploy sophisticated conversational interfaces and support systems that understand context and provide personalized responses.

04.Process Augmentation

Enhance existing business processes with AI-powered analysis, prediction, and recommendation capabilities.

Hybrid Architectures for LLMs

Hybrid architectures for Large Language Models (LLMs) combine the powerful generative capabilities of foundation models with specialized components to overcome limitations, enhance performance, and create more reliable AI systems for real-world applications.

Definition – Hybrid LLM architectures integrate large language models with other systems, data sources, or specialized algorithms to compensate for inherent LLM limitations while leveraging their strengths in natural language understanding and generation.

01.Retrieval-Augmented Generation (RAG)

Combines LLMs with external knowledge retrieval systems to ground responses in verified information. The model generates responses based on both its internal parameters and explicitly retrieved contextual data.

Use case – Enterprise search, customer support systems, and domain-specific assistants

02.Tool-Augmented LLMs

Extends LLMs with the ability to use external tools and APIs, enabling them to perform calculations, execute code, query databases, or interact with web services to complement their reasoning.

Use case – Data analysis assistants, coding assistants, and AI agents

03.Multi-Model Orchestration

Combines multiple specialized models in a pipeline where each model handles specific aspects of a task. LLMs might work alongside computer vision models, specialized classifiers, or domain-specific models.

Use case – Multimodal applications, complex decision systems, and specialized industry solutions

04.Human-in-the-Loop Systems

Integrates human feedback and oversight into LLM operations, allowing for expert review, correction, and guidance in high-stakes applications.

Use case – Healthcare diagnostics, legal document review, and content moderation