Retrieval-augmented knowledge assistant for an anonymous client
Netbase delivered a retrieval-augmented generation (RAG) knowledge assistant for a client, in a role that combined AI consulting, engineering and integration. The client is not named. This anonymous portfolio record describes only the kind of AI system and what Netbase did; it publishes no client name, logo, location, dates, model choice, figures or results.
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Record at a glance
- Record type
- Anonymous portfolio record, not a case study
- Client
- Organization, name withheld
- Kind of AI system
- Retrieval-augmented knowledge assistant
- Netbase role
- AI consulting, engineering and integration
- Related solution
- AI knowledge assistant
- Related services
- Enterprise knowledge RAG and generative AI development
- Industry
- Not published; filed under professional services for navigation
- Location, period, team and models
- Not published
- Published metrics
- None
Why this record is anonymous
Until now the only named AI work in Netbase's portfolio was the recommendation engine delivered for 4over4. Netbase has also delivered AI projects for clients that are not named, including RAG knowledge assistants, document AI and MLOps pipelines. At David (CEO)'s direction, each of these is recorded anonymously: the kind of AI system and the Netbase role, nothing that could identify the organization or its data.
What a RAG knowledge assistant usually covers
A knowledge assistant lets people ask a question in plain language and get an answer drawn from the organization's own documents, with the sources shown so the reader can check them. Retrieval-augmented generation is the pattern behind it: documents are ingested and indexed, the passages relevant to a question are retrieved, and a language model writes the answer only from those passages. Access rules decide which documents each person may see, and an evaluation set tests answer quality before changes go live.
That is a description of the category. The scope, sources and model of the client's assistant are not published.
What Netbase did
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AI consulting
Shaping which questions the assistant should answer and which knowledge it should draw on, in line with how Netbase leads: consulting, solutions and custom development first, delivery capacity second.
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Engineering
Building the retrieval and answer pipeline for this organization's knowledge.
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Integration
Connecting the assistant with the client's existing systems; which systems is not disclosed.
What the record does not disclose
- The organization's name, sector, logo or location.
- Documents, questions, answers or screenshots that could expose client knowledge.
- The models, vendors or hosting used, dates, team size, budget or contract details.
- Answer accuracy, usage, time saved, return on investment or any other result.
Planning a similar assistant
Our AI knowledge assistant page walks through sources, permissions and cited answers, and the enterprise knowledge RAG service covers ingestion, retrieval and evaluation. The tools behind them are described on our data and AI stack page, and the enterprise AI readiness assessment guide helps decide whether your knowledge is ready. Most Netbase projects are delivered on fixed-price contracts, with scope and price agreed after discovery; milestone-based and retainer models are also offered.
See more proof in Netbase work, or contact Netbase to review a similar project.
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