RAG-based AI knowledge management and enterprise search platform
Retrieval-Augmented Generation (RAG) is an AI approach that retrieves relevant information from a knowledge base and provides it to a language model so responses are grounded in source documents. A RAG AI platform helps an organisation ingest documents, build a knowledge base, search it semantically, and deliver context-aware answers.
Product snapshot
RAG AI Platform
Key capabilities
A clear explanation of the product, the workflow it supports, and where it fits.
Direct answer
Retrieval-Augmented Generation (RAG) is an AI approach that retrieves relevant information from a knowledge base and provides it to a language model so responses are grounded in source documents. A RAG AI platform helps an organisation ingest documents, build a knowledge base, search it semantically, and deliver context-aware answers.
Organisations accumulate policies, manuals, product documentation, service guides, and internal notes across shared drives and email. Finding a reliable answer often means knowing where to look or asking someone who does. A RAG platform makes that knowledge searchable and gives users an AI-assisted way to ask questions against approved documents.
Sortie Services builds RAG-based knowledge management and enterprise search applications. The platform can ingest documents into a knowledge base, use semantic search to retrieve relevant passages, and generate context-aware responses. Integrations and local language model options are confirmed according to the deployment architecture, data sensitivity, and infrastructure available.
This is an enterprise AI solution, not a generic chatbot. The focus is on grounding responses in source material, controlling which documents are available, and fitting the system into existing business workflows.
These are the operational gaps the product is designed to address. Your final module set is configured around your actual process.
Policies, manuals, and product documentation are spread across folders, drives, and email threads.
Employees rely on colleagues who know where information is stored, creating delays and inconsistent answers.
Traditional search returns documents but does not understand the intent behind a question.
General-purpose AI tools may answer without reference to the organisation’s approved documents.
A practical feature set for the day-to-day workflow of this product.
Bring approved documents into the platform through an ingestion process suited to the source formats in scope.
Organise ingested material into knowledge bases that reflect departments, products, or document collections.
Search by meaning and context rather than relying only on exact keyword matches.
Retrieve relevant source passages and provide them to the language model to ground responses in the knowledge base.
Generate answers that use the retrieved context and reflect the question being asked.
Let users ask questions against documents and review the source material used for the response.
Connect the knowledge platform to existing applications through APIs where integration is included in scope.
Control which users and knowledge bases are available based on the access model configured for the deployment.
Modules can be configured based on your operating process and the implementation scope agreed for your project.
Document intake, parsing, chunking, and preparation for indexing.
Collection structure, document metadata, and knowledge base administration.
Semantic search, retrieval ranking, and source passage selection.
Context assembly and language model response generation.
Display of source documents or passages used to support an answer.
API connections to business applications and workflow touchpoints.
User roles and knowledge base access controls where included in the deployment.
Review of questions, responses, retrieval quality, and operational usage.
Not sure if this product matches your business? Describe your workflow and our team will help you assess the fit before you commit.
Request a consultationAn illustrative preview of the kind of operational dashboard this product can provide. Actual screens and modules are configured for your deployment.
Illustrative interface — not live customer data
Knowledge bases
6
2 departments
Indexed documents
1,842
Ready for search
Questions today
126
Sources cited
Knowledge base activity
Activity
Illustrative interface only. Not live customer data.
Pricing is configured around modules, users, deployment, and support. Final amounts are confirmed in a written quotation.
A focused starting point for small teams adopting RAG AI Platform.
Monthly billing
Request a Custom Quote
Final quote based on your scope
Additional configuration for growing teams and more complex operations.
Monthly billing
Request a Custom Quote
Final quote based on your scope
Custom scope, integrations, and deployment planning for larger organisations.
Monthly billing
Request a Custom Quote
Final quote based on your scope
RAG platform projects are quoted after reviewing document volume, model hosting, integrations, and deployment architecture.
Compare all product pricingDirect answers to common questions about this product. For anything specific to your business, request a demo.
A RAG AI platform combines information retrieval with a language model. It searches a knowledge base for relevant passages and provides that context to the model so answers are grounded in source documents.
Documents are ingested and indexed. When a user asks a question, the system retrieves the most relevant passages, supplies them as context to the language model, and generates a response based on that retrieved material.
The platform is designed to ingest approved documents in supported formats. The exact file types, volume, and preprocessing requirements are reviewed during solution planning.
Local or private language model integration can be discussed where the deployment architecture and infrastructure support it. The model choice depends on data sensitivity, hardware, cost, and accuracy requirements.
Access control can be included where implemented in the deployment, so users see the knowledge bases and documents appropriate to their role. The access model is defined during solution design.
No. The platform is focused on document-grounded knowledge retrieval and enterprise search. The interface, integrations, and workflows are configured around the organisation’s use case.
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Book a free demo and share your current process. We will walk through the relevant modules and discuss deployment, scope, and next steps.
