Preparing Data With AI

Most businesses that want to set up a chatbot or a custom AI assistant actually already have data — past support conversations, FAQ lists, product descriptions — but it’s scattered, repetitive, and disorganized. An AI tool can’t be “fed” that directly.

I don’t process this data by hand, one by one — I direct AI tools to categorize the conversations, extract the repeated question-answer pairs, and turn them into an organized table. This is both faster and more consistent.

What this service does

I take your scattered text (support conversations, FAQs, product descriptions) and break it into organized columns — category, question, correct answer. The resulting table can be used directly as the “knowledge source” for a chatbot or a custom GPT.

You can also think of this as a preliminary step to the custom chatbot/GPT setup services I already offer — if you don’t have organized data, we prepare it first, then set up the chatbot.

How we work

You send me your data (Excel, text file, message export). I direct AI tools to process it in bulk, review and correct the output, and deliver it to you as an organized file.

Time and price depend on the volume of data — a few hundred rows can be done in a few hours, larger datasets can take a day. That’s why I don’t quote a fixed price; I look at the data first and give a clear quote.

What kind of data qualifies

Anything mostly text-based qualifies: support conversations, emails, FAQs, product catalogs, internal procedure documents. Visual/audio data (photo tagging, audio transcription) isn’t within the scope of this service, it’s evaluated separately.

How do we start? First, a free 20–30 minute intro call. We talk about what you want and what’s realistic. If it makes sense, I send the scope and price in writing, then we start. You can reach me from the contact page.

What the table I deliver looks like

Let me be concrete: the resulting file usually has these columns — category (shipping, returns, price, technical…), question (as the customer actually asked it, not your internal phrasing), answer (single and clear), source (which conversation/document this answer came from), last updated, and status (approved / pending review).

The source and status columns get skipped in most places, but they’re the two most critical. Without a source, no one can verify six months later whether an answer is still correct; without status, an unreviewed answer enters the system looking approved.

The Four Problems I Run Into Most

1. Ten different versions of the same question. “When does shipping arrive,” “where’s my order,” “when will it reach me” — these three should be one single record, with the different phrasings grouped under it.

2. Contradicting answers. It happens a lot that two different reps gave two different answers to the same question. I filter these out and bring them to you: “which one is correct?” I can’t make that call, and shouldn’t.

3. Outdated information. Campaign terms from two years ago, prices that have changed, discontinued products. This is a chatbot’s most damaging mistake — stating outdated information in a confident tone.

4. Personal data. Conversations contain names, phone numbers, addresses, order numbers. None of this should go anywhere without being cleaned first.

What Actually Determines Chatbot Quality Isn’t the Model

The biggest misconception I run into doing this work: “if we use a better AI model, it’ll give better answers.” Usually it doesn’t. A chatbot’s answer can’t exceed the quality of the data it’s fed.

The clearest sign of this is the ability to say “I don’t know.” In a properly prepared dataset, when an out-of-scope question comes in, the system can say “I don’t have information on this, let me connect you to a representative.” A system fed with scattered data fills the gap by making something up — and does it in a very convincing tone.

So data preparation may look like the “before” of a chatbot setup, but it’s actually the step that determines its quality.

How Personal Data Cleaning Is Done

Filtering personal data out of conversations isn’t something to do by hand — it slips through in thousands of rows. What I do is first automatically catch and mask name/phone/email/address/order-number patterns, then manually spot-check a sample by eye.

The important point: masking doesn’t make the data useless. What the chatbot needs to learn isn’t “Mr. Ahmet’s order,” it’s how that question was answered. Once the personal part is removed, exactly what you need is left behind.

Frequently Asked Questions

Do you read my data by hand, or is it all automatic?

Both together: an AI tool processes it in bulk, I check and correct the output. It’s neither fully manual nor fully unsupervised.

Why isn’t the price fixed?

A 100-row FAQ list and a 5,000-row support archive are very different amounts of work. I look at your data first and give a clear price and timeline based on its volume — so you don’t overpay and I don’t lose money.

What happens to the privacy of my data?

The data you share is used only to do this work, and isn’t stored or shared for any other purpose. If there’s sensitive/personal data, we discuss this upfront and decide together how to handle it.

Can I get this service alone, without setting up a chatbot?

Yes. You can ask me to just organize your data, a chatbot setup isn’t required. But usually the two together make more sense.

Is it hard to update my data after it’s organized?

No, the structure I deliver is designed for this. Adding a new question-answer pair is just writing one row into the table. The hard part is setting up the initial structure; after that your own team can maintain it.

How many question-answer pairs are enough?

Coverage matters more than count. The vast majority of incoming questions usually cluster around a few dozen topics; capturing those correctly is worth more than adding hundreds of rare questions. When I see the data, I also figure out which topics are actually being asked.

Can the data be multilingual?

Yes. I often work with mixed Turkish and English archives. Keeping the languages in separate columns is usually healthier, because the answer to the same question in two languages isn’t always a literal translation.