Vector Databases Explained
A vector database stores numeric representations of content and finds the closest matches by meaning, not exact keywords, making it the retrieval backbone behind most RAG and enterprise knowledge systems.
Read articleRAG & ENTERPRISE KNOWLEDGE SYSTEMS
RAG Systems That Turn Enterprise Knowledge Into Answers
A general-purpose AI model does not know your company's documentation, and asking it to guess produces confident-sounding but unreliable answers. Cortex Texas AI builds retrieval-augmented generation (RAG) systems that ground every answer in your actual source material.
- Ingest
- Index
- Retrieve
- Ground
- Answer
Quick Answer
What Is RAG & Enterprise Knowledge AI?
Retrieval-Augmented Generation (RAG) combines a search/retrieval step over your own indexed documents with a language model's generation step, so answers are constructed from passages actually retrieved from your knowledge base rather than from the model's general training data alone. Enterprise Knowledge Systems apply this pattern at the scale of an organization's full documentation, wikis, tickets and manuals.
The Challenge
Where Enterprise Knowledge Gets Lost
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01
Scattered Knowledge
Institutional knowledge is spread across documents, wikis, tickets and manuals with no single place to search.
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02
Document Search Difficulty
Keyword search often misses the right answer if it isn't phrased the way the document is written.
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03
Outdated Information
Static documentation and cached answers can drift out of date as source content changes.
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04
Generic AI Responses
A general-purpose AI model has no knowledge of your organization's own content unless it's explicitly given access.
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05
Knowledge Silos
Different teams often maintain separate, disconnected sources of the same underlying information.
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06
Access-Control Requirements
Not every employee should be able to retrieve answers grounded in every document.
Capabilities
What Our RAG Engagement Covers
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RAG Architecture
Designing the retrieval-plus-generation pipeline that grounds AI answers in your own content.
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Enterprise Search
Search across your organization's documentation that understands meaning, not just keywords.
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Knowledge Assistants
AI assistants that answer questions using your own indexed content as their source.
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Document Ingestion
Bringing documents, wikis, tickets and manuals into a searchable, retrievable index.
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Vector Search
Similarity-based retrieval that finds relevant passages even when the wording doesn't match exactly.
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Source-Grounded Answers
Responses constructed from retrieved passages, with the source available for verification.
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Knowledge Synchronization
Keeping the retrieval index up to date as source content changes.
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Access-Controlled Retrieval
Retrieval that respects existing document permissions, so users only see what they're already allowed to.
In Practice
Where RAG Applies
Internal Knowledge Assistant
An assistant staff can ask questions of, grounded in your organization's own documentation.
Policy Assistant
Answer questions about internal policy directly from the policy documents themselves.
Support Knowledge System
Ground support responses in your product documentation and prior resolved tickets.
Technical Documentation Search
Make technical or engineering documentation searchable by meaning, not just keyword.
Employee Copilot
Help staff quickly find answers buried across multiple internal systems.
Research Assistant
Help a team query and synthesize a large body of specialized content.
Client Document Portal
Let clients or partners query a defined, permissioned set of documents.
Compliance / Procedure Retrieval
Retrieve the exact procedure or compliance document relevant to a given question.
Connectivity
Knowledge Connected From the Sources You Already Maintain
KNOWLEDGE SOURCES
- Document Stores
- Wikis
- Ticketing Systems
- Content Management Platforms
RETRIEVAL INFRASTRUCTURE
- Vector Databases
- Access Control Systems
Under the Hood
Retrieval Architecture Built Around Your Knowledge
The models, frameworks and infrastructure Cortex Texas AI configures for this service -- pulled directly from our technology catalog, not a claim of vendor partnership.
INTELLIGENCE
How We Build It
How We Build the Knowledge System
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01
Knowledge Discovery
We identify what content actually needs to be searchable and who needs to query it.
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02
Source Assessment
We review the quality, structure and access rules of the source documents.
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03
Content Preparation
Source content is cleaned and structured so it can be indexed effectively.
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Retrieval Architecture
We design the indexing and retrieval pipeline, including how it respects existing access controls.
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Model / Retrieval Integration
Retrieval is connected to the language model that generates grounded answers.
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Evaluation
The system is tested against real questions to confirm it retrieves and answers accurately.
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07
Deployment & Knowledge Maintenance
The system goes live with a defined process for keeping the index in sync as content changes.
Security
Controlled by Design
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Access-Respecting Retrieval
Retrieval is scoped so users only retrieve answers grounded in documents they're already permitted to see.
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Encrypted Storage
Indexed content is stored encrypted, consistent with the sensitivity of the source documents.
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Query Log Confidentiality
Query logs are retained for quality review under the same confidentiality terms as the underlying documents.
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Source Attribution
Answers can be traced back to the specific documents they were retrieved from.
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Index Freshness Controls
The retrieval index is kept in sync on a defined schedule so answers don't drift from current source content.
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No Model Retraining on Private Data
RAG retrieves from your content at query time; it does not require retraining or fine-tuning a model on your private data.
Industries We Apply This In
Frequently Asked Questions
RAG grounds each answer in specific passages retrieved from your own indexed documents, and can cite those sources, which substantially reduces the risk of fabricated answers compared to a model relying on general training knowledge alone.
Yes; retrieval can be scoped so users only receive answers grounded in documents they already have permission to access, rather than exposing everything indexed in the system.
A RAG assistant is only as current as its underlying document index, so we design the ingestion pipeline to re-index source content on a schedule appropriate to how frequently it changes.
Related Insights
RAG vs. Fine-Tuning: Choosing the Right Approach
RAG and fine-tuning are two different ways to make a general AI model useful for your specific data and needs. Most enterprise use cases are better served by RAG; fine-tuning solves a narrower, different problem.
Read articleWhat Is RAG (Retrieval-Augmented Generation)?
RAG (retrieval-augmented generation) grounds an AI model's answers in passages retrieved from your own documents at query time, rather than relying solely on what the model learned during training.
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