RAG Systems and AI Knowledge Search
Make company knowledge accessible, with traceable sources.
Policies, manuals and project knowledge are useful when people can find the information they need. We develop AI applications that retrieve relevant content from approved sources and use it to inform answers. A useful RAG solution combines data preparation, search quality, permissions and traceable references. Together, we test which questions it can support and where clarification or a direct document view is more appropriate.
Your options
Services that move your project forward
Prioritize knowledge needs and sources
We gather typical questions and select the relevant documents and systems. Freshness, quality, usage rights and ownership are clarified before connecting a source.
The knowledge base is based on real information tasks.
Prepare and structure documents
Content is imported, divided into appropriate sections and provided with source and version information. Tables, appendices and difficult formats are explicitly taken into account.
The search receives usable content with a traceable origin.
Combine search methods appropriately
Keyword search, vector search and reranking are compared using your questions. Search components such as OpenSearch are assessed against the existing platform and integration requirements.
The choice of approach is guided by the relevance of retrieved content.
Include permissions in the search
Identities, document rights and visibility are already taken into account when selecting the context. Revocation of rights, separate user groups and protected content are tested.
Answer generation uses context the user is authorised to access.
Check answers and sources together
We design answers with suitable source references and evaluate retrieval and answer generation separately. Unsupported statements, irrelevant results and missing information receive defined handling.
Users can more easily understand results and check the original if necessary.
Organise updates and operations
New, changed and removed documents follow agreed processing procedures. Index status, errors and quality trends are monitored, with clear ownership.
The knowledge base remains connected to its sources and ongoing needs.
Where to start
RAG Systems and AI Knowledge Search Use cases
Three example situations show how we can help.
Employees search across many manuals and repositories
We connect a prioritised body of knowledge to realistic questions and answers with traceable references.
A support team needs appropriate information about an issue
The solution supports research and response preparation within defined access rights.
An existing chatbot finds documents, but answers questions unreliably
We examine preparation, retrieval, context and evaluation as a coherent process.
From requirements to results
A clear process with agreed milestones
Select questions and knowledge
Users, sources, rights, and evaluation criteria determine the pilot scope.
Test preparation and search
Document processing and search procedures are compared on representative questions.
Implement response function and controls
Source display, permissions, and missing information behavior are integrated.
Evaluate quality and hand over maintenance
Tests, updates, responsibilities and next extensions are documented.
Your benefit
What you receive
- A connected and prepared knowledge base within the agreed scope.
- An evaluated search with documented sources and permission rules.
- An answer feature with source references and defined handling of information gaps.
- Test results and procedures for updates, deletion and ongoing support.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Assess knowledge base and search quality
For orientation: sources, typical questions, rights and suitable search procedures.
Request a quote: Assess knowledge base and search qualityImplement a RAG pilot with source references
For practical evaluation: a defined knowledge base, answer generation and documented results.
Request a quote: Implement a RAG pilot with source referencesIntegrate and expand knowledge search
For everyday use: additional sources, updates, permissions and defined operations.
Request a quote: Integrate and expand knowledge searchSYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Identities, secrets and policies
Keycloak · OpenBao · External Secrets · Kyverno
Sign-in, technical secrets and platform policies serve different purposes. We connect them with roles, limited permissions and documented exceptions. The selected tools form part of a common access and operating model.
Controlled access and more consistent platform policies.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
Questions before you get started
What does retrieval-augmented generation mean?
Retrieval-augmented generation adds previously retrieved knowledge to a language model request. Retrieval and answer generation are separate steps. We evaluate both for quality, authorised access and traceability.
Is vector search always better than classic search?
It depends on content and questions. Terms, product numbers, or exact designations can benefit from keyword search; semantic similarity can support other questions. We compare appropriate methods and, if necessary, a combination.
Which documents can be connected?
This depends on formats, interfaces, quality and usage rights. Text documents, websites or structured sources require different preparations. Scans, complex tables and special layouts are specifically checked in the pilot.
How does confidential information remain protected?
Source rights and user identity are integrated into the context selection. Authorization checks are carried out in the application and search layer. A note in the prompt does not replace this technical access control.
Does RAG completely prevent incorrect answers?
No. Missing or unsuitable search results, as well as answer generation itself, can cause errors. Source references, separate evaluation of retrieval and generation, and defined clarification or refusal behaviour help users assess the results.
What happens if documents are changed or deleted?
Update and deletion procedures are coordinated with the source systems. Changes also need to reach the index and other derived data. We test the behaviour and required freshness with appropriate cases.
How is the quality of a knowledge search measured?
We use representative questions and relevant content assessed by subject specialists. Retrieval results, support for answer claims, handling of missing information, response times and costs are evaluated separately. An aggregate score alone is insufficient.
Can existing search and platform services be used?
Yes, where interfaces, permissions, search capabilities and operations fit the requirements. Existing components are assessed before selecting additional products. We verify required extensions and practical integration in the technical evaluation.
Discuss your next step
Which information should your team find and understand more easily?
Share typical questions and the available knowledge sources. We define an initial scope and clear criteria for evaluating your RAG solution.
RAG Systems and AI Knowledge Search
Your next step
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