Reduce AI hallucinations

By grounding AI outputs in structured, governed knowledge models, Semaphore improves the reliability of tools like Copilot.

As organizations increasingly adopt generative AI tools like Microsoft Copilot, ChatGPT, and other large language models (LLMs), a critical challenge emerges: hallucinations. These are instances where AI confidently generates incorrect, misleading, or fabricated information. While LLMs are powerful, they lack inherent grounding in enterprise-specific knowledge, which can lead to:

• Inaccurate answers to business-critical questions

• Misinterpretation of policies, procedures, or terminology

• Erosion of trust in AI-assisted workflows

Semaphore addresses this challenge by acting as a semantic grounding layer---anchoring AI outputs in structured, governed, and explainable enterprise knowledge.

What Causes AI Hallucinations?

AI hallucinations typically occur when:

• The model lacks access to authoritative, up-to-date information.

• It attempts to fill gaps in knowledge with plausible-sounding but incorrect content.

• It misinterprets ambiguous terms or context (e.g., "Apple" as a fruit vs. a company).

This is especially risky in regulated industries, customer-facing scenarios, or decision-making contexts.

How Semaphore Grounds AI in Enterprise Knowledge

Semaphore mitigates hallucinations by enriching enterprise content with semantic metadata derived from a centralized, curated knowledge model. This metadata includes:

• Concepts: Defined terms with multilingual labels and synonyms.

• Relationships: Contextual links between concepts (e.g., "GDPR" → "Data Subject Rights").

• Attributes: Structured facts extracted from unstructured content (e.g., dates, names, monetary values).

When AI tools like Copilot are integrated with Semaphore-enriched content, they can:

• Retrieve more relevant and accurate documents.

• Understand the context of enterprise-specific terminology.

• Generate responses that reflect the organization's actual policies, models, and language.

Use Cases for AI Grounding with Semaphore

Copilot in Microsoft 365

• When a user asks Copilot to summarize a contract, Semaphore ensures the document is tagged with concepts like "Termination Clause" or "Confidentiality," guiding the AI to focus on the right sections.

AI Chatbots and Virtual Assistants

• A customer support bot can use Semaphore-tagged knowledge base articles to provide accurate, policy-aligned answers---reducing the risk of misinformation.

AI-Powered Search and Recommendations

• Semantic metadata improves the precision of AI-driven search, ensuring that recommendations are based on meaning, not just keywords.

Explainability and Trust

Unlike opaque AI models, Semaphore's rule-based classification and fact extraction are:

• Deterministic: Every tag or fact is the result of a specific rule.

• Auditable: Decisions are logged and traceable.

• Governed: Models are version-controlled and reviewed by domain experts.

This transparency builds trust in AI-assisted workflows, especially in compliance-sensitive environments.

Multilingual and Cross-Domain Support

Semaphore supports multilingual classification and NLP, ensuring that AI tools can operate reliably across global content. It also harmonizes metadata across silos---so AI can access consistent, structured knowledge whether content lives in SharePoint, a CMS, or a data lake.

Business Impact

By grounding AI in Semaphore's structured knowledge, organizations can:

• Reduce hallucinations and misinformation

• Improve the accuracy of AI-generated content

• Accelerate adoption of AI tools by increasing user trust

• Ensure compliance with internal policies and external regulations