An enterprise AI assistant that answers from your own data and respects who is asking
The Enterprise AI Assistant answers staff questions from your documents and business systems, cites where each answer came from, and checks permissions before it reads anything. It can run on your own servers so confidential data never leaves.
- Answers with sources from your documents and systems
- Permissions enforced before retrieval
- Private deployment on your servers or cloud account
Useful AI starts with what it is allowed to read
Public AI chat tools are good at general questions and unusable for most company ones, because the answer lives in a policy PDF, a contract, a ticket history or the ERP, and pasting those into an outside service is exactly what a careful business should not do.
The Enterprise AI Assistant retrieves from your own sources: document stores, policies, manuals, past tickets and, through defined connections, systems like ERP, CRM and HRMS. Every answer comes with the passages or records it was drawn from, so it can be checked rather than trusted.
Permissions are applied at retrieval, before the model sees anything. A branch manager asking about salaries gets their own team’s data or nothing; a contract visible only to legal is never retrieved for sales. This is the part most chatbot projects add last, and it has to be first.
It can run on open-weight models on your own hardware or in your cloud account, or on a hosted model where your policy allows. We measure answer quality on a set of real questions before rollout, and re-run that set whenever documents or models change.
Who it is for
- Organisations with large policy, process or technical document sets
- Finance, legal and healthcare teams with confidential data
- Support teams answering the same questions from manuals
- Businesses on our ERP, CRM or HRMS that want plain-language access
- Leadership that wants AI without sending data outside
What Enterprise AI Assistant does
Answers from your documents
Search and retrieval across your files with citations to the passage used.
Permission-aware retrieval
Access control applied before the model reads anything, mirroring roles in your systems.
Connections to business systems
Read-only queries into ERP, CRM or HRMS through defined tools, with results linked back.
Actions with approval
Where enabled, the assistant drafts actions such as a ticket or a purchase request for a person to confirm, and logs them.
Private model deployment
Open-weight models on your hardware or cloud account; hosted models only where your policy allows.
Measured quality
An evaluation set of real questions and expected answers, run before go-live and after every change.
Audit log
Who asked what, which sources were used and what was returned.
How work flows through Enterprise AI Assistant
- Step 01
Question asked
A staff member asks in plain language, in the web app or inside our platforms.
- Step 02
Permissions checked
The user’s role decides which documents and records can be retrieved.
- Step 03
Sources retrieved
The most relevant passages and records are found and ranked.
- Step 04
Answer with citations
The model answers only from those sources and shows them.
- Step 05
Logged and measured
The exchange is logged; quality is tracked against the evaluation set.
What changes in your business
- Answers grounded in your own documents, with sources
- Confidential data that stays inside your infrastructure
- Retrieval quality measured, not assumed
- Hours of document searching removed from routine work
Connected during setup
- Document stores and shared drives
- Our ERP, CRM and HRMS platforms
- Ticketing and knowledge-base exports
- Single sign-on with your identity provider
- Open-weight or hosted language models
Integrations are configured for your business during implementation, with the providers and hardware you already use where possible. Ask us about anything not listed.
How we deploy Enterprise AI Assistant
- 01
Feasibility on your documents
Two to three weeks on a real document set with real questions, ending in a measured result.
- 02
Hardware sizing
What a private model needs for your volume, compared honestly with hosted cost.
- 03
Pilot with one team
One department and document class, permissions enforced from day one.
- 04
Rollout
More sources and teams added once the pilot measures well.
- 05
Ongoing evaluation
The question set is re-run when documents, prompts or models change.
What we need from you
- A representative sample of documents
- Which roles may see which content
- Twenty to thirty real questions with correct answers
- Hardware or a cloud account for the agreed deployment
None of it has to be tidy. Discovering that your data is messier than expected is part of the job, not a reason to delay starting.
How a Enterprise AI Assistant quotation is worked out
We don’t publish a price list, because two businesses rarely need the same setup. After a short call you get a written quotation that separates one-time setup from yearly costs. It depends on:
- Volume and type of documents and systems to connect
- Private hardware, your cloud account or a hosted model
- Number of users and departments
- Permission model complexity
- Evaluation and ongoing support
Prices are quoted excluding GST.
When to choose something else
- A team that only needs a public website chatbot for FAQs: a simpler tool will do
- Organisations with no digital documents yet: digitisation comes first
Enterprise AI Assistant — questions buyers ask
Does our data go to OpenAI or another AI company?
Not unless you choose a hosted model. The assistant can run entirely on open-weight models on your own servers or cloud account, so documents and questions stay inside your infrastructure.
How do you stop it from making things up?
It answers only from retrieved sources and shows them, and quality is measured against a set of real questions before rollout. When nothing relevant is found, it says so rather than guessing.
Can it see data a user is not allowed to see?
No. Permissions are checked before retrieval, so restricted documents and records are never passed to the model for that user.
What does an enterprise AI assistant cost?
We quote after a feasibility step on your documents. Cost depends mainly on deployment (private hardware or hosted), the sources connected and the number of users.
Can it take actions in our systems?
Where enabled, it can draft actions such as a ticket or request for a person to confirm. Every action is logged, and nothing is executed without approval unless you decide otherwise.
How does this relate to the AI in your ERP and POS?
The assistant inside AI ERP and AI POS is this platform scoped to that product’s data. The Enterprise AI Assistant extends it across documents and several systems.
Ready-made editions and connected platforms
Before you decide
When you need it built from scratch
Test it on your own documents
Share a sample of documents and the questions your team asks. We will run a feasibility check and tell you, with a measured result, whether it is worth building.