Private AI analytics means using AI to analyse business data without that data leaving your control. The key is a localised LLM: a language model that runs inside the analytics platform's own controlled environment, or on your own servers, instead of sending your questions and data to a public AI service. Combined with a guarantee that your data is never used for training, it lets you get AI-speed answers without the privacy trade-off.
AI has made data analysis dramatically faster. Anyone can ask a question in plain English and get a chart back in seconds. But that convenience raises a question many teams skip: where does the data go when the AI processes it?
For a marketing team analysing public campaign numbers, the answer may not matter much. For a finance team, a laboratory, a manufacturer or anyone handling customer data, it matters a great deal.
The hidden risk of “just ask a chatbot”
The fastest way to analyse data with AI is also the riskiest: copy it into a public AI tool. When you do that, you may be:
- Sending sensitive data outside your organisation, to servers you do not control and may not be able to locate.
- Losing control of retention: prompts and files may be stored for a period set by the provider, not by you.
- Contributing to model training, depending on the service and plan.
- Creating compliance exposure under data protection laws such as India's Digital Personal Data Protection Act, 2023 or the EU's GDPR, and under customer contracts that restrict where data can be processed.
Many analytics tools that advertise “AI features” work the same way under the hood: they pass your questions, and sometimes samples of your data, to an external AI API.
What is a localised LLM?
A large language model (LLM) is the AI that understands your question. A localised LLM is one that runs within the analytics platform's own controlled environment, or on your infrastructure, rather than being called over the internet from a public AI provider. Your questions, your schema and your data stay inside the boundary you have approved.
| Public AI API | Localised LLM | |
|---|---|---|
| Where the AI runs | The AI provider's servers | Inside the platform's controlled environment, or your own servers |
| Where your questions and data go | Sent to an external service | Stay within the approved environment |
| Training on your data | Depends on provider terms and plan | Can be ruled out entirely |
| On-premise option | Usually not possible | Possible |
| Best for | Public, non-sensitive data | Customer, financial, lab, HR and other sensitive data |
Who needs private AI analytics most
- Laboratories, pharma and life sciences, where test results and formulations are confidential and regulated.
- Finance and accounting teams working with revenue, margins, payroll and forecasts.
- Manufacturing, where production, quality and supplier data reveal how the business really runs.
- Healthcare and anyone processing personal or patient information.
- Companies with enterprise customers whose contracts limit where data may be processed.
- Public sector and regulated industries with data residency requirements.
Six questions to ask any AI analytics vendor
- Is my data ever used to train AI models? The only acceptable answer for sensitive data is a clear no.
- Where does the AI model run? Inside the platform (localised), or via an external public API?
- Can we deploy on-premise or in our own private cloud if our policy requires it?
- Who can see what? Is access controlled by role, and can we share dashboards without exposing raw data?
- Can we see how answers were produced? Inspectable queries make AI answers auditable.
- Are answers calculated from the data? A real query engine produces verifiable numbers; a text generator produces guesses.
Privacy doesn't have to mean slower answers
A common assumption is that private AI means weaker AI. In analytics, that is not the case. The AI's job is to understand the question and write an accurate query; the heavy lifting of calculating the answer is done by a query engine running on your data. That split lets a localised model deliver fast, accurate answers while your data stays where it belongs. (For how this works step by step, see our guide to natural language analytics.)
How WeTa Analytics keeps AI analytics private
WeTa Analytics was designed for teams whose data is too important to paste into a public chatbot:
- You choose the model, per organisation. Point WeTa Analytics at a model running inside your own network, restrict an external model to your table's structure with no data values, or block external models entirely.
- Customer data is never used to train AI models.
- SaaS or on-premise: deploy inside your VPC or data centre when data must stay on your network, with the same product experience.
- Role-based access and shareable links control who sees what.
- Inspectable queries: every AI answer is calculated with SQL (with a pandas fallback) and the query is available to review.
Frequently asked questions
What does “localised LLM” mean?
It is a large language model that runs inside the analytics platform's own controlled environment, or on your own servers, instead of being accessed through a public AI provider's API. Your questions and data stay within the environment you have approved.
Is it safe to use ChatGPT or other public AI tools for company data?
It depends on the data and your agreements. For sensitive information such as customer, financial, lab or HR data, a platform with localised AI, no training on your data and an on-premise option is the safer choice.
Does private AI analytics give less accurate answers?
Not when it is designed well. In WeTa Analytics the AI writes the query and the query engine calculates the answer from your data, so accuracy comes from the calculation rather than from the size of the model.
Does WeTa Analytics use my data to train its AI?
No. WeTa Analytics never trains AI models on customer data, and you control which model may answer for your organisation, including a model that runs inside your own network.
Can WeTa Analytics run on-premise?
Yes. You can deploy it inside your own VPC or data centre, including air-gapped or private-cloud setups, with the same features as the SaaS version.
Private AI analytics, on your terms
WeTa Analytics never trains on customer data, and you choose which model may answer, including one inside your own network. Deploy as SaaS or on-premise.
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