Enterprise Knowledge Assistant
A grounded, citation-backed assistant over enterprise documentation, tickets and procedures.
What it does
Enterprise knowledge is spread across documentation, wikis, ticketing systems, code repositories and shared drives. Finding the right answer means knowing where to look, and for support teams, implementers and new staff, the same questions are asked repeatedly.
The Rabbit Cloud Enterprise Knowledge Assistant is a retrieval-augmented assistant that answers from the organisation's own corpus and cites the source passage for every response. It is built on Amazon Bedrock Knowledge Bases with Amazon OpenSearch Serverless or pgvector as the index, and is designed around the parts that determine whether such a system is trusted: chunking tuned to the document types, hybrid retrieval with reranking, explicit refusal when the corpus does not contain an answer, and an evaluation suite that runs on every change.
Built for
- Implementation support for platform and open-source products
- Internal engineering and policy knowledge bases
- Customer support deflection with escalation handoff
- Sales and pre-sales enablement across solution libraries
From input to auditable output
Synchronise
Connectors pull content from documentation, wikis, tickets and repositories on a schedule, respecting source permissions and version history.
Index
Content is chunked according to its structure, embedded and indexed for hybrid keyword and vector retrieval.
Retrieve
A user question is used to retrieve candidate passages, which are reranked for relevance before generation.
Answer
Amazon Bedrock generates a grounded response with citations. If retrieval confidence is low, the assistant declines and offers an escalation path.
Evaluate
A curated question set with expected answers runs on every corpus, prompt or model change, gating release.
AWS services
Controls built in
- Source permissions enforced at retrieval, so users only see content they are entitled to
- Refusal behaviour defined for questions outside the corpus
- Prompt injection defence and content guardrails on both input and output
- Every answer traceable to source passages and a model version
- Usage and feedback logged for continuous evaluation
How the solution is delivered
Each stage defines scope, cost and outcomes before the next commitment. Qualifying engagements are structured to AWS partner programme requirements.
| Stage | Duration | Outcome |
|---|---|---|
| Assessment | 2 to 4 weeks | Use-case scoring, data readiness review, evaluation set design and a cost-per-transaction model. |
| Proof of concept | 4 weeks | Working pipeline on the client's data against agreed success criteria, with measured accuracy and cost. |
| Production build | 8 to 16 weeks | Hardened deployment in the client's AWS account: infrastructure as code, guardrails, monitoring and integration. |
| Operate | Ongoing | Evaluation on every change, model and cost review, and managed operations under SLOs. |
Frequently asked
Which sources can be connected?
Confluence, SharePoint, Google Drive, GitHub and GitLab, Zendesk and Jira, Amazon S3 and public documentation sites. Additional sources are added through custom connectors.
How is hallucination controlled?
Through grounding, citation and refusal. Responses are generated only from retrieved passages, each response cites its sources, and when retrieval confidence is below threshold the assistant declines rather than guessing.
Can it be deployed inside our own AWS account?
Yes. The assistant is deployed in the client's AWS account and Region, with data remaining under the client's control.
Discuss Enterprise Knowledge Assistant with a solutions architect
A structured assessment determines feasibility, data readiness and the appropriate delivery path before any commitment to build.