Solutions · AI systems
Train an AI agent on your business data.
Training an AI agent on your own data rarely means building a new model. It means giving an existing model a knowledge base drawn from your documents, policies, CRM and past replies, rules for using it, and tests that prove it answers from that material instead of guessing. Althea Growth builds and maintains that layer for businesses in the UAE.
01 · What training means
Grounding, not a new model
When people say they want to train AI on their own data, they usually need the agent to know their business, not a model built from scratch. Fine tuning changes how a model writes. Grounding changes what it knows: the agent looks up the relevant passage in your material at the moment of the question and answers from it, with the source attached.
Grounding is easier to change and to audit, and a fact disappears the day you delete it. We fine tune only when there is a clear reason, and say so in the proposal.
02 · The material
What goes into the knowledge base
Service lists, contracts and terms, FAQs, product sheets, the email and WhatsApp replies your team is proud of, and the CRM fields the agent needs. We clean it first: outdated versions out, contradictions settled with you, and each document given an owner and a review date.
03 · Evaluation
Tested on your real questions
Before launch we build a test set from questions your team has already answered, the awkward ones included. We check that each answer is correct, comes from your material and cites it, and that the agent hands over when the material is silent. The same test runs again after every update.
04 · Updates
Kept current, not trained once
Businesses change every week: new listings, new terms, new opening hours. We connect the sources that change so updates flow in, review the questions the agent could not answer, and add the missing material instead of letting it improvise.
05 · Privacy
Your data stays yours
The knowledge base lives in accounts you control. Personal data goes in only where the task needs it, access follows your roles, and model providers are used on terms or settings that keep your data out of their training. If you leave, the knowledge base leaves with you.
Questions, answered.
Can I train an AI agent on my own data without making it public?
Yes. The material stays in your accounts and the agent reads it at the moment of a question. Nothing is published, and provider settings keep it out of model training.
Is this the same as uploading files to ChatGPT?
The idea is similar, with more control: which sources are used, who can ask, what the agent may do, how it is tested and where the logs live.
How much data do we need?
Less than most people expect. A clean FAQ, the service and policy documents and a set of good past replies are enough to start. Quality matters more than volume.
What happens when the answer is not in our material?
The agent says it does not know and passes the question to your team with the conversation attached. Those questions become the next additions to the knowledge base.
Start with a brief.
Tell us what you want to build or fix. We reply within one working day.