RAG
Retrieval Augmented Generation
Giving AI the one thing it usually lacks
Context from your own data
Most AI systems speak confidently even when they are wrong. That happens because they depend only on patterns learned during training. When your people ask questions that require internal knowledge, the model guesses. Teams lose time verifying answers that should have been clear from the start.
Retrieval augmented generation fixes this by grounding responses in your real documents, your internal systems and your actual workflows. Instead of guessing, the system retrieves facts first and generates an answer only after it understands the source material.
RAG brings accuracy, traceability and trust into every interaction.
Why Standard AI Breaks Down
People expect quick, reliable information. Instead, they get
- answers that sound right but are not
- scattered sources
- slow multi-step searches
- separate knowledge bases
- constant fact-checking
- no way to see where an answer came from
Knowledge workers spend hours each week hunting through wikis, chats, emails and dashboards. This slows decisions and increases risk.
RAG changes this pattern by consolidating knowledge, grounding each response in real data and showing exactly where the information came from.
RAG becomes the layer that brings clarity into complex environments
What RAG Does in Practice
You get
- answers based on your sources
- citations to original documents
- confidence scores
- a consistent way to find knowledge
- safer decisions
- faster workflows
Your teams stop
- guessing
- digging through old files
- asking the same questions repeatedly
- switching between systems
- revalidating information
Key Outcomes
(Real numbers from typical enterprise deployments)
| Time spent fact checking | Reduced by up to 90 percent |
| User confidence | Increased by up to 75 percent |
| Time to insight | Improved by around 60 percent |
RAG supports a wide range of applications without needing separate tools.
Where This Capability Helps Most
Strong fits include
support teams answering technical questions
product teams exploring feature impacts
analysts reviewing large document sets
operations teams validating decisions
compliance teams checking rules
leadership teams needing fast summaries
Enterprise Features That Matter
Built from the ground up for enterprise environments, based on the capabilities described in the reference document.
Connectors for real systems
Supports SharePoint, Confluence, Salesforce, Google Workspace, Slack, Microsoft Teams, Notion and major cloud storage providers. Custom connectors available for internal databases.
Intelligent indexing
Handles text, images, tables and metadata. Creates vector embeddings for semantic search. Syncs continuously, so new content becomes searchable within minutes.
Source citations
Every answer links directly to the section of the document that informed it. Teams can verify context and see the origin of every claim.
Feedback loop
Users rate accuracy. The system learns from this behaviour and improves retrieval quality.
Advanced query understanding
Understands multi-part questions. Maintains context across follow-ups. Rewrites ambiguous queries to improve accuracy.
Access control
Respects existing permissions in your identity system. Ensures users see only what they are meant to see.
Architecture Overview
Below is a simplified view of how the workflow fits together.
Data ingestion |
Collects and normalises content from connected sources |
Vector database |
Stores embeddings for semantic lookup |
Retrieval engine |
Finds the right information for each query |
Generation layer |
Produces natural language answers grounded in retrieved documents |
API gateway |
Handles authentication, rate control and monitoring |
This structure is the backbone of enterprise RAG solutions that work across teams.
Integration Capabilities
These options help you create AI powered enterprise search, internal copilots, assistants for support teams and other intelligent tools.
REST APIs for custom workflows
Webhooks for real-time sync
SSO using SAML or OAuth
GraphQL endpoints
SDKs for Python, JavaScript and Java
Deployment Patterns
Fully managed cloud
Automatic updates, multi region redundancy and a high uptime guarantee.
Virtual private cloud
Dedicated infrastructure within your environment with full control over network boundaries.
On premises
Docker-based installation that works inside restricted networks.
Hybrid
Some data stays local. Some processing happens in the cloud. You choose where each piece belongs.
Data Protection and Compliance
Your data stays where it isDocuments remain in source systems. The platform stores only the embeddings required for retrieval. |
Enterprise-grade security
|
Compliance readySupports SOC 2, ISO 27001, GDPR, HIPAA and FedRAMP requirements. |
Knowledge Management That Finally Works
Knowledge is valuable only when it is accessible. RAG gives companies practical knowledge management by bringing everything into a single, unified layer.
People can ask questions in plain language and get answers backed by real documents. Teams spend less time searching and more time doing their actual jobs. New employees get up to speed faster. Leaders make fewer guesses. Information becomes reliable again.
RAG acts as the connective tissue between your knowledge and your AI stack.
How RAG Fits Into Your AI Ecosystem
This capability becomes the foundation for
internal chat interfaces
copilots
decision support systems
research assistants
workflow automation
policy enforcement
governance tools
Thinking About Bringing RAG Into Your Organisation?
If you want to see how this capability behaves with your real data, we can set up a guided demonstration. You get to see the retrieval quality, the citations and the speed with your documents, not generic examples.