Generative AI Connected to Your Business Knowledge
Cognic builds Generative AI and RAG solutions that connect language models with your private documents, knowledge bases, databases, and business systems.
Your Business Has the Information. Your Teams Still Spend Time Finding It.
Business knowledge is spread across documents, policies, SOPs, databases, emails, applications, and internal systems. Employees often spend time searching, comparing information, asking colleagues, or manually reviewing documents before they can take action.
Scattered Knowledge
Important information lives across multiple systems and document repositories.
Slow Information Retrieval
Employees spend time searching through documents and internal systems.
Knowledge Gaps
Teams struggle to find the right information when they need it.
Repeated Questions
The same internal and customer questions are answered repeatedly.
Give AI Access to the Right Business Knowledge
Retrieval-Augmented Generation, or RAG, connects an AI model to trusted information sources. The system retrieves relevant information before generating a response.
RAG helps ground AI responses in your organization's information instead of relying only on the model's general knowledge.
Generative AI Solutions Built Around Your Knowledge
Enterprise Knowledge Assistants
AI assistants that answer employee questions using approved company information.
RAG Applications
Connect LLMs with documents, databases, knowledge bases, and business data.
AI Document Q&A
Ask questions across contracts, policies, manuals, reports, and other documents.
Enterprise AI Search
Search internal knowledge using natural language instead of traditional keyword searches.
Private Knowledge Bases
Create controlled AI knowledge environments around company-specific information.
AI-Powered Business Applications
Embed Generative AI into existing software, portals, workflows, and internal applications.
From Company Knowledge to Grounded AI Responses
RAG helps ground responses in approved information rather than relying solely on standard LLM training data.
Bring Your Business Knowledge Into One AI Experience
Documents
PDFs, Word documents, spreadsheets, presentations, and other approved files.
Policies & SOPs
Internal procedures, policies, guidelines, and operational documentation.
Databases
Structured business information from approved databases.
CRM Data
Relevant customer, lead, and account information where appropriate.
Knowledge Bases
Existing internal knowledge repositories and structured content.
Web Content
Approved websites and public information sources where required.
Reports
Operational, financial, technical, and business reports.
Application Data
Information from approved enterprise applications through APIs or integrations.
Where Enterprise Generative AI Creates Value
Internal Knowledge Assistant
Help employees find answers across approved company knowledge.
Customer Support Assistant
Provide grounded answers using approved product, service, and policy information.
Document Q&A
Ask questions across large document collections.
Policy Assistant
Help employees find relevant policy information and procedures.
Research Assistant
Retrieve and summarize information from approved knowledge sources.
Sales Assistant
Help sales teams access product, customer, proposal, and company information.
Employee Support
Answer routine internal questions using approved HR, IT, and operations information.
Business Intelligence Assistant
Allow users to ask natural-language questions about approved business data.
RAG or Fine-Tuning?
- Company documents
- Policies and SOPs
- Knowledge bases
- Product information
- Internal data
- Frequently changing information
- Specialized behavior
- Output formatting
- Task-specific patterns
- Domain-specific model adaptation
RAG and fine-tuning solve different problems. Cognic selects the approach based on the data, workflow, model requirements, security needs, and business objective.
Connect Generative AI to Your Existing Business Systems
Cognic connects Generative AI to your existing technology environment instead of isolating AI inside a standalone chatbot. See how this works with AI Agents and AI Automation.
Give Your Team a Faster Way to Find Trusted Information
* UI example demonstrating grounded answers with source context. Not a live system output.
Build AI Around Your Data Governance Requirements
Enterprise AI needs controls around data access, permissions, retrieval sources, monitoring, and usage.
Access Controls
Control who can access specific AI applications and knowledge sources.
Source Controls
Define which information the AI system is allowed to retrieve.
Data Protection
Design data flows around your organization's security requirements.
Monitoring
Track usage, retrieval behavior, response quality, and system performance.
Human Review
Route sensitive or uncertain workflows to people when required.
Measure More Than Whether the AI Gives an Answer
Cognic evaluates RAG systems around retrieval quality, answer relevance, grounding, response behavior, and business workflow outcomes.
Retrieval Quality
Did the system retrieve relevant information?
Answer Relevance
Did the response address the user's question?
Grounding
Is the answer supported by the retrieved information?
Workflow Outcome
Did the AI help the user complete the intended task?
Enterprise AI Built Around Industry Knowledge
Healthcare
Knowledge assistants, document Q&A, internal workflows, administrative knowledge, and approved information retrieval.
View →Real Estate
Property documents, leases, SOPs, tenant information, operational knowledge, and property-management workflows.
View →Financial Services
Financial documents, policies, internal knowledge, customer information, and operational workflows.
View →Insurance
Policy information, claims documentation, internal knowledge, and customer support workflows.
View →What Enterprise Generative AI Helps Your Team Do Better
Faster Knowledge Access
Help employees find relevant information faster.
Less Repetitive Searching
Reduce time spent manually searching through documents and internal systems.
More Consistent Answers
Ground responses in approved company information and configured business rules.
Better Knowledge Access
Make organizational knowledge easier to use across teams and applications.
RAG Workflow Example
PROBLEM
Support teams spending excessive time searching through thousands of PDF manuals and SOPs to answer specific customer and internal questions.
SOLUTION
Cognic connected the company's secure document repository to an internal knowledge assistant using a RAG architecture, allowing staff to ask natural language questions and receive grounded answers with exact source citations.
TECHNOLOGY
- RAG & Vector Search
- Enterprise LLM Integration
- Knowledge Base Ingestion
- APIs & Workflow Automation
RESULTS
- Instant knowledge retrieval
- Grounded, source-cited responses
- Significantly reduced search time
- Consistent procedural compliance
Generative AI Built Around Your Business Knowledge
Business-First AI
Start with the business question and knowledge requirement, not the model.
RAG + AI Engineering
Combine retrieval, LLMs, knowledge systems, APIs, and software engineering.
Integration-Focused
Connect AI to your existing applications and data environments.
Controlled Enterprise AI
Design access controls, source controls, monitoring, evaluation, and human review into the solution.
Generative AI & RAG FAQs
What is Generative AI?
What is RAG?
How does RAG work?
What is the difference between RAG and fine-tuning?
What business data can RAG use?
Can RAG work with private company documents?
Can Cognic build an enterprise knowledge assistant?
Can RAG connect with existing business systems?
How does RAG help reduce unsupported AI answers?
How do you evaluate RAG performance?
How do you control access to enterprise knowledge?
Can Cognic build a private Generative AI solution?
How long does a RAG implementation take?
Ready to Connect AI to Your Business Knowledge?
Tell us where your information lives and what your teams need to find. Cognic will help you design the right combination of Generative AI, RAG, knowledge sources, integrations, governance, and workflows.