We hear “do you do AI” in almost every first conversation. The more interesting question is different: what do you run it on yourselves, and how long has it been running without interruption, for real users who won’t forgive an outage.

Two kinds of companies that say they “do AI”

In practice, we see two approaches that look similar from the outside and work completely differently underneath. The first: a company implements AI only for clients. The project ends with a demo, a pilot, or a production rollout — but the operational risk (model mistakes, query costs, provider changes) stays with the client, or disappears along with the team once the project wraps up.

The second: a company builds and runs its own product where AI is part of daily operation, not just a presentation stage, but month after month, for users who won’t forgive an outage. That’s what it means for AI to run in daily use in the literal sense: the system has to work correctly every single time nobody’s watching.

The difference isn’t cosmetic — a company in the second category knows firsthand where AI gets things wrong and which control mechanisms are genuinely necessary, because it built them and is on the hook for them.

What changes when AI has to be maintained, not just deployed

An implementation project ends on handover day. Maintenance starts the day after, and it has no end date. Running a live product means facing questions that rarely show up in an implementation proposal: what happens when the model misreads a document? What does a single request actually cost at real volume, not in a ten-example test? Where exactly does the AI’s role end and deterministic logic — the kind that can’t randomly make a mistake — begin?

Those are the same questions we cover in our piece on separating what AI does from what code decides — in our systems, AI proposes and classifies, while critical decisions are always handled by ordinary, predictable code. That’s not a theoretical distinction from an article — it’s the architecture we had to build, because otherwise our own products wouldn’t hold up.

There’s also the quiet, ongoing work behind the scenes: monitoring model outputs, flagging edge cases, updating classification rules whenever a new document type or market shows up — work with no end date.

Three products, one pattern

WebET is an IT and AI innovation company: we don’t only implement AI for clients — we build and maintain three products where the AI model works every single day, and we carry that experience directly into dedicated implementations for other companies.

  • Qkwit. AI-assisted accounting and payroll for companies in Poland, today across four segments: sole proprietors, limited companies, payroll and HR, and accounting firms. AI reads and classifies documents, while a deterministic engine calculates taxes, VAT, and social security contributions, never the language model.
  • Taniej po Lek. A basket-based medication price comparator across 11 countries. AI recognizes basket contents from a photo or pasted text and suggests cheaper substitutes from a pre-filtered list of candidates, while a deterministic engine always calculates totals and prices.
  • Brokik. A rental management SaaS platform for landlords and tenants across 25 countries. AI adapts an existing lease or protocol to a free-text instruction, and a setup assistant drafts property and tenant records that the user approves.

Three different industries, three different uses of AI, one common thread: AI in a live system, not on a slide. We carry those same lessons — where AI gets things wrong, how to monitor answer quality, when to escalate a decision to a person — into dedicated implementations beyond our own products, including work with automotive and financial-sector companies.

What this difference means for a client commissioning an implementation

When a team runs its own AI-based products, that firsthand knowledge translates directly into the quality of a client implementation. First, realistic pricing: a company that has never paid the bill for model queries at real volume can easily underestimate maintenance costs. Second, architecture that assumes control from day one, not just functionality: the “AI proposes, code decides” split isn’t an add-on requested by a client, it’s our default way of building.

Third, a realistic timeline: we know from our own experience that an implementation needs a careful process audit before any code gets written, because we go through that stage regularly, across three different products.

For a client choosing an AI implementation partner, that’s a practical tip: it’s worth asking not “do you do AI,” but “what do you run it on yourselves, and how long has it been running without interruption.” The answer says more than any proposal ever could.

In short

A company that maintains its own AI-based products knows firsthand what another team can only guess at: what maintenance costs at scale, where the system gets things wrong, and which control mechanisms are actually necessary. Read more about our approach to AI implementation on the AI page.

Frequently asked questions

What’s the difference between “implementing AI for clients” and “building your own AI products”?

An implementation ends when the project is handed over. A company’s own AI product is maintained indefinitely, at its own cost and risk, which forces a more mature approach to error handling and cost control.

Can a team without its own AI product still implement AI well for a client?

Yes, but it has to earn that knowledge on the client’s project, often through trial and error. A team with its own products already in daily use brings that knowledge ready-made.

Why is AI in daily use more than a demo or a proof of concept?

Because a demo works on selected examples, while daily use has to work on every case, including the unforeseen ones, for years, at real volume and real cost.

How can you tell if a team actually uses AI rather than just talking about it?

Ask directly about a specific, working product, not a list of technologies on a slide, and how long that product has been running without interruption.

Does WebET implement AI in industries other than accounting, rentals, or price comparison?

Yes, we also work with companies in the automotive and financial sectors, applying the same pattern: AI reads and proposes, code decides. We carry the experience from maintaining our own products directly into those implementations.