Question 1

What data do we need before starting an AI project?

Accepted Answer

Less than most companies expect. A working assistant usually needs your product or service data, your policies, and a sample of real questions customers or staff already ask — often exported from email, a helpdesk, or WhatsApp. You do not need a data warehouse or a labelled training set to get value.

Question 2

How do you stop the AI from making things up?

Accepted Answer

Three layers: retrieval, so answers are drawn from your documents rather than the model's memory; guardrails in the system prompt that require the model to say it does not know and hand over instead of guessing; and an evaluation set that we re-run whenever prompts or models change. We also log every answer so drift is visible rather than discovered by a customer.

Question 3

What happens to our data, and who can see it?

Accepted Answer

Your content stays in your own database, and we design the flow so only the passages relevant to a question are sent to the model provider for that request. Business-tier API access from major providers does not train on your data by default; we document exactly what leaves your systems so your privacy notice and processing records match reality.

Question 4

Do we need to train our own model?

Accepted Answer

Almost never. Modern hosted models combined with retrieval over your own content outperform a small custom model for the vast majority of business tasks, at a fraction of the cost and time. Fine-tuning becomes worth discussing only for narrow, high-volume, highly repetitive tasks with plenty of clean examples.

Question 5

Can the AI work with our existing systems?

Accepted Answer

Yes — that is what makes it useful rather than a novelty. Through tool calling, the assistant can query live data such as order status, stock or a customer record via your existing APIs, and return a grounded answer instead of a generic one. Where a system has no API, we discuss scheduled exports or a small integration layer.

Question 6

What does it cost to run once it is live?

Accepted Answer

Running costs are usage-based: model tokens per conversation plus hosting for the database and application. Short, retrieval-grounded answers are inexpensive, and we log cost per conversation so you can see the real figure instead of estimating. We also select model tiers per task, using a smaller model for classification and routing.

Question 7

Is this realistic for a small business, not just a large one?

Accepted Answer

Yes, and small teams often see the clearest benefit because one person is handling everything. The right scope is one high-volume task — answering product and delivery questions, qualifying incoming leads, drafting replies — rather than a company-wide platform. Starting narrow keeps the budget small and makes the result measurable.

Question 8

When does the AI hand over to a person?

Accepted Answer

We define handover rules with you: complaints, refunds, anything with a legal or financial edge, repeated failed answers, or a direct request for a human. The conversation transfers with full history so the customer never repeats themselves, and the assistant stops responding on that thread until the person is done.

Question 9

How long before we see something working?

Accepted Answer

A focused prototype answering questions from your real content typically takes a few weeks, not quarters. Production launch depends on how many systems it must integrate with and how much review your team wants before it faces customers — we deliberately start with an internal-facing pilot when the risk warrants it.

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