CORE AX TECHNOLOGY

AI Knowledge Engineering

Turn scattered business informationinto knowledge AI can use.

We collect and structure information your business already has—websites, documents, FAQs, manuals, and operational data—into knowledge that helps AI find and use the right evidence.

Scattered business information organized into a unified knowledge structure

WHY AI KNOWLEDGE

Having informationdoes not mean AI can use it.

Existing business information

The information exists,but it is scattered.

Pages and documents use different formats, the same information is spread across multiple locations, and sources and relationships remain disconnected.

  • Website
  • Documents
  • FAQ
  • Manuals
  • Email
  • Business systems
  • DB/API
  • ···
A structural sphere bringing scattered information into a unified knowledge base

AI Knowledge

One knowledge basethat AI can find and use.

Structure scattered information into meaningful units and connect sources, context, relationships, and permissions to build knowledge AI can use.

  • Meaning
  • Context
  • Source
  • Relationships
  • Permissions

We do more than gather information.We turn it into a structure AI can use.

KNOWLEDGE PIPELINE

From source informationto searchable knowledge.

  1. INPUT

  2. STRUCTURE

    We do more than store documents and pages as they are. We structure information by its page, section, topic, and business context.

    Documents · Pages · Sections · Topics · Business context

  3. RETRIEVAL

We do more than store source content.We turn it into a searchable structure of meaning.

KNOWLEDGE STRUCTURE

We do more than store content.We connect the context behind it.

KNOWLEDGE=CONTENT+CONTEXT

CONTENT

  • Pages
  • Documents
  • FAQ
  • Manuals

KNOWLEDGE

Knowledge layers structured together for AI use

AI-READYKNOWLEDGE

CONTEXT

  • Source
  • Category
  • Author / Date
  • Access permissions
  • Original location

Knowledge Retrieval

Find knowledge that matches the meaning and conditions of each question.

Understand meaning beyond wording and apply the required conditions to find the most relevant evidence.

  1. User question
  2. Intent and context analysis
  3. Knowledge retrieval
  4. Relevant evidence selection
  5. LLM response
  • Semantic Search

    Find information with similar meaning, even when the wording differs.

  • Metadata Filter

    Apply conditions such as category, document type, permissions, and date alongside the search.

  • Hybrid Retrieval

    Combine semantic and keyword search when needed.

Candidate evidence

KNOWLEDGE LIFECYCLE

When your business information changes,your knowledge base must change with it.

  1. UPDATE

    Detect changes to source pages, documents, and data

  2. SYNC

    Reflect only changed information in the knowledge base

  3. VERSION

    Track when information changes and manage versions

  4. VALIDATE

    Continuously validate search results and supporting evidence

Great AI startswith great knowledge.

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