We turn one clearly scoped document collection or priority knowledge source into a secure AI search. The result includes permissions, citations, subject-matter test questions and a reliable recommendation for expansion.
Companies worldwide trust WZ-IT
As a clearly bounded RAG pilot, the sprint shows on real company content whether information is found reliably, how citations remain visible and which technical path can support an AI knowledge base with additional sources.
Employees ask natural-language questions and receive answers with traceable source references from the agreed content.
We document who may use the knowledge area, how it is updated and which permissions must remain intact as it expands.
Subject-matter tests, known limits and a clear recommendation show whether and how additional sources should be connected.
We deliberately limit the first step. This creates a testable result, a fixed timeframe and no open-ended integration project.
One document collection or standard source, the user group and the most important questions are agreed.
Formats, metadata, duplicates and the update path are reviewed and prepared for processing.
Indexing, retrieval, answer generation and citations are configured for the agreed content.
The access model and required separation of knowledge are documented and implemented within the agreed scope.
We review real questions, retrieval quality, citations and typical edge cases with a subject-matter contact.
You receive documentation, open points and a prioritised recommendation for further sources or user groups.
Deliberate boundary
The technical implementation follows the business questions. We do not build a stack first and look for a use case afterwards.
Source, user group, data path and 15 to 25 representative test questions are agreed together.
We configure indexing, retrieval, a local or approved model and source citations.
Results and answers are evaluated with the department and refined where required.
Outcome, limits, operational path and the next expansion stage are documented and handed over.
Clearly defined packages
A supplied document set requires less integration than an automatically synchronised source. Both packages end with subject-matter acceptance.
RAG Pilot
€2,900
excluding VAT, one-off
For one clearly bounded document collection supplied by your team.
Connected Knowledge
€4,900
excluding VAT, one-off
For one supported standard source with an agreed automated update path.
Before commissioning, we confirm source, scope and technical accessibility. Custom connectors or complex ACL logic receive a separate and transparent scope.
The sprint is a paid and reusable first step. The solution can then expand without rebuilding the initial content.
Local AI platform with hardware, model, operations and support for use inside your organisation.
Configure AI CubeMultiple knowledge areas, roles and assistants for everyday employee use.
View solutionCustom connectors, ACLs, workflows, evaluation and interfaces for complex data landscapes.
Explore RAG integrationRequest a RAG sprint
Tell us the source, user group and most important search or answer problem. We will check which package covers the right first step.
Scope, data access, permissions and next steps
The RAG sprint has a fixed content set, defined test questions, a clear deliverable and a fixed timeframe. Open-ended RAG consulting is appropriate when the data landscape, target design or integrations first need broader clarification. For complex starting points, we therefore define the right entry scope before work begins.
File upload provides a technical feature. The sprint also addresses data quality, updates, permissions, test questions, citations and acceptance criteria for a defined company knowledge set.
No. The sprint can run on an existing AI system, a suitable customer environment or as part of an AI Cube introduction. We agree in advance where data, index and model are processed.
Typical starting points include supplied file sets, network drives, Nextcloud, SharePoint exports, wikis or document management systems. Whether automated synchronisation fits the fixed price depends on the interface and permissions model.
That must be checked for each source. A simple pilot can use a bounded knowledge area. User-specific ACLs require a consistent identity and permissions path across source, index and interface.
We agree representative subject-matter questions and expected source passages. Retrieval, citation quality, completeness and recognisable edge cases are evaluated and documented rather than only shown in a demo.
You can continue using the tested area. Additional sources, roles, assistants, integrations or ongoing operations are added only when the outcome and priority justify expansion.
From local AI integration to architecture, data sovereignty and ongoing operations.