Generative AI Solutions: Where They Pay and Where They Pretend

Every generative AI demo looks the same: a prompt, a pause, a page of fluent text. What the demo does not show is who checks the text, what happens when it is wrong, and what it costs at ten thousand requests a month. Generative AI solutions that survive contact with a real business answer those three questions before anything is written.

Uses that hold up

Uses that pretend

Unsupervised customer-facing answers about money, health or contracts. Generated reviews or testimonials. Content published without a person reading it. Anything where a confident wrong answer has a cost the business cannot absorb. GBCORP declines these, and says so early, because the failure arrives in month two when nobody is watching the output any more.

Guardrails that make it usable

Grounding. The model answers from your documents and shows where the answer came from. Review steps. Drafts wait for a person where the consequence is real. Brand rules. Tone, vocabulary and things never to say are written into the prompt and tested. Evaluation sets. A fixed set of real cases with known good answers, run on every change, so quality is a number rather than an impression. Cost control. Model choice and caching decided per use, with usage analytics from day one.

Inside the product, not beside it

The best generative AI feature is one the user does not think of as AI: a summary that is simply there, a reply already drafted in the console, a description written when the product is created. GBCORP builds these with the same engineers who build the app, so they appear where the work happens rather than in a separate window.

Checklist before a generative AI project

• One use, one queue, one owner who will read the output for the first month.
• Fifty to two hundred real examples with the answer you would have written.
• The rules for what must never be generated or published without review.
• The documents the assistant may answer from, and where they live.
• The acceptable cost per request, which decides the model.
• How success is measured: hours saved, errors avoided, response time.

The numbers to expect

AI development and automation on the GBCORP e-shop starts at a flat price for the first release; larger programmes are scoped in a week and quoted per release. A well-bounded drafting or assistant feature is in production within weeks and reports its own accuracy from the first day. Expand to the next use only when the first one is steady.

How a pilot is run

Pick the one task with the most repetition and the clearest right answer: replies to the ten most common customer questions, summaries of a daily report, product descriptions from a specification sheet. Collect the examples. Build the feature into the console where the task already happens, with the draft shown beside the source and a button to accept, edit or reject. Run it for four weeks with one person reading every output. Measure acceptance rate, edit distance and time saved, and compare cost per request against the hours it replaced. If the numbers hold, remove the review step only where the consequence of an error is trivial, and keep it everywhere else. Then pick the next task.

Data, privacy and where the model runs

Generated text is only as safe as the data that reaches the model. GBCORP decides per use where the model runs: a hosted large model for tasks where the text is not sensitive and quality matters most, a smaller open model in a private cloud where documents must stay inside the business, and on-device inference where the data may not leave the phone at all. Customer data is minimised before it reaches any model, prompts and outputs are logged for audit under the business's own account, and the choice is written down so it can be explained to a regulator or a customer who asks.

Multilingual output and brand voice

A business in several markets needs generated text that reads as if a colleague in each market wrote it, not as a translation. GBCORP builds brand rules per language: vocabulary, formality, terms never to use, and examples of good replies from each market's own team. Right-to-left languages are tested in the actual interface, because a correct sentence in a broken layout is still a broken product. The evaluation set contains cases in every launch language, so the accuracy number is per language rather than an average that hides the weakest one. Where a market's reviewer rejects an output, the case joins the set and the next release scores against it.

Questions we hear first

Which models do you use?

The one that fits the use: hosted large models where quality justifies the cost, smaller open models run privately where data must stay in place. Nothing is locked to a single vendor.

Will it use our data to train someone else's model?

No. Data flows are chosen so that your content is used to answer, not to train, and the choice is documented.

Can it work in several languages?

Yes, including right-to-left languages, with brand rules per language.

How do we know it is still working in six months?

The evaluation set runs on every change and the analytics show accuracy and cost weekly under the support plan.

Where to start

Read the GBCORP AI development and machine learning hub, then look at the priced GBCORP AI Development and Automation product. For automation of workflows rather than content, see where AI automation saves hours.

GBCORP operates across 17 countries on 4 continents through six pillars: AI & IT, Innovation, Sustainability, Infrastructure, Business Lounge, and EV's & Solutions.