Enterprise Knowledge Assistants
Grapefruit builds enterprise knowledge assistants that answer questions from your own documents, such as policies, contracts, product manuals and tickets, with sources cited for every answer. Built on retrieval-augmented generation (RAG) in your cloud, they respect existing access rights and hand over to a human when unsure.
Which documents and systems hold the answers, how current they are, who may see what, and what must be cleaned first.
Ingestion, chunking, search and re-ranking tuned on your content, kept in sync as documents change.
An interface where your people or customers already work: portal, app, intranet, Teams or the contact-centre desktop.
Every answer shows its sources, respects existing permissions, and routes to a human when confidence is low.
A test set of real questions scored before launch and re-run on every change, with dashboards for accuracy and unanswered questions.
Our method
Collect the real questions people ask, audit the sources that answer them and agree access rules and success metrics.
A working assistant over a representative document set, scored on real questions with the team that will use it.
Connect all sources, add permissions, hand-off, monitoring and the interface in your channels, then roll out by team.
Watch unanswered and low-rated questions, fix content gaps, re-run evaluations and upgrade models.
Proof
We have not published a client case study for this capability yet. Here is the AI work we have shipped and can walk you through on a call.
Ways to start
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides.
Find the 3 AI use cases worth funding in your business, with the numbers to defend them.
From validated prototype to a production AI system your team trusts and runs.
Your AI keeps working, stays compliant and keeps improving.
Industries
FAQ
RAG is a way of building AI assistants where the system first searches your own documents for relevant passages and then asks a language model to answer using only those passages, with citations. It keeps answers current and checkable without retraining a model every time a document changes.
Answers are grounded in retrieved passages and cite them, the assistant is instructed to say it does not know when sources are missing, and we score it on a set of real questions before launch and after every change. Where an error is costly, a person reviews or answers instead.
No. The assistant applies the same access rights as your source systems, so a person only gets answers from documents they could already open. We test this explicitly during the pilot with your security team.
Yes, usually after it proves itself internally. Customer-facing assistants must disclose that people are talking to AI under Article 50 of the EU AI Act, which has applied since 2 August 2026. We design that disclosure, plus a clear hand-off to a human, into the experience.
In your cloud tenant or an EU region we agree on. Documents and embeddings stay under your control, are not used to train public models, and can be deleted with the source. The data flow is documented for your DPO.
The number and messiness of sources, how often they change, access-control complexity, the channels it must live in, and the languages it must handle. Model usage is usually a small part. The pilot measures these so the production estimate is concrete.
Related
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides. 3 to 4 weeks, fixed scope, no obligation to continue.
We reply within 1 business day, usually the same day. Not ready to talk? Take the free AI assessment.
