Model-agnostic explainability
Works with major foundation models and enterprise LLMs without modifying the underlying model architecture.
Explainability Layer for Generative AI
Generative AI delivers powerful results, but the reasoning of Large and Small Language Models (LLMs and SLMs) often remains a black box—making outputs difficult to trust, validate, and govern.
DeepXAI-4-LLM explores a lightweight, model-agnostic explainability layer for generative AI systems. The prototype is designed to make outputs more transparent and traceable, helping reveal how answers were formed, how reliable they are, and where risks may exist.
Works with major foundation models and enterprise LLMs without modifying the underlying model architecture.
Identifies uncertainty, unsupported reasoning, and potential hallucinations to improve decision confidence.
Helps organizations understand how prompts, inputs, and context influence model outputs.
Provides traceable explanations of how responses are generated, supporting governance, compliance, and risk management.
Seamlessly integrates into existing AI pipelines, applications, and enterprise systems.
As generative AI moves from experimentation into production, organizations are increasingly deploying both large and small language models (LLMs and SLMs) in critical workflows, enterprise systems, and edge environments. However, these models operate as black boxes, making it difficult to understand, validate, and trust their outputs.
Lack of transparency creates risks related to compliance, safety, and reliability—especially in regulated industries and decision-critical applications.
Our DeepXAI-4-LLM prototype explores approaches to making generative AI more transparent, auditable, and trustworthy, supporting organizations as they evaluate and scale LLM and SLM systems.
The need to understand and validate generative AI outputs spans many industries and use cases.
Consulting firms, legal practices, audit firms, and private equity using LLMs for research, analysis, and due diligence.
Companies building AI copilots, enterprise assistants, and customer-facing LLM applications.
Banks, insurers, and fintech firms using LLMs for advisory, underwriting, fraud analysis, and customer interaction.
Healthcare providers, pharmaceutical companies, and digital health platforms deploying LLMs for clinical support and research.
Organizations using AI assistants for engineering support, maintenance, and operational decision-making.
Companies deploying LLM-based systems to support infrastructure operations, autonomous systems, and industrial workflows.
Government agencies using LLMs for citizen services, internal workflows, and policy support.
Across every industry where generative AI is transforming operations and decision-making.