Connect your knowledge
Define the user, the task and the conditions a successful outcome needs to meet.
RAG & Knowledge Systems
RAG knowledge systems.
Answers with context.
Connect AI to your documents and business knowledge with retrieval, source references and access controls designed around your users.
CYGNABYTE / BUILD SYSTEMWORKFLOW
From source to grounded answer
A clear path from input to outcome.
01
Connect your knowledgeDocuments & databases
✓02
Retrieve relevant sourcesSearch & permissions
✓03
Answer with referencesSources & evaluation
✓Designed around your usersBuilt for real workflows
Documents & databasesConnect your knowledge
Search & permissionsRetrieve relevant sources
Sources & evaluationAnswer with references
The right starting point
Answers grounded in your information
Retrieval-augmented generation, or RAG, searches relevant material before a model writes an answer. It can support an internal knowledge assistant, document search or a product that explains domain-specific information. Retrieval does not guarantee correctness, so source references and evaluation remain essential parts of the experience.
Retrieve relevant sources
Connect the interface, data and application behavior around the agreed workflow.
Answer with references
Evaluate representative cases, handle failures and prepare a practical release.
Inside the build
Make knowledge easier to use
Bring proprietary information into a searchable product experience.
Discuss your requirements →Prepare the knowledge pipeline
Start with an inventory of documents, databases and update schedules. The pipeline may need text extraction, normalization, chunking and indexing. Preserve source identifiers and metadata so results can be traced and updated. Scanned documents may need OCR, and different layouts should be tested before choosing an extraction approach.
Search quality and answer quality
We consider keyword, semantic or hybrid retrieval depending on the content and task. A useful evaluation set includes questions with known answers, questions that span multiple documents, and questions the collection cannot answer. Measure relevant-source retrieval separately from answer correctness so improvements target the right part of the system.
Respect document permissions
A search result must not reveal content that the user cannot access. Apply document and tenant permissions during retrieval and when opening cited sources. Define how removals, permission changes and new versions reach the index. Treat retrieved text as untrusted input and constrain any connected tools.
Plan ongoing updates
A knowledge system needs maintenance when documents change. Agree how updates are ingested, when stale content expires, and how failed extraction jobs are handled. Your discovery brief should include sample documents, access rules, expected question types and the required freshness of answers.
From the studio
Engineering behind the experience.
Explore the product interfaces and project work featured in our portfolio. A useful AI experience connects the interface to the data, workflows and systems underneath it.
Explore our work →We work with Python, Go, Laravel, Node.js and NestJS application backends, and with MySQL, PostgreSQL and MongoDB data layers. Retrieval infrastructure is selected separately around the content, access rules and evaluation requirements.
Start with the idea
Your next chapter starts with a conversation.
Tell us what you want to make possible. We’ll help you define a useful next step.
Connected capabilities
Built to work together.
Most products combine a few capabilities. Explore the services that support the next part of your roadmap.