Jyper vs ChatGPT

Jyper runs on the same class of frontier models behind ChatGPT, so “whose AI is smarter” is the wrong question — that part is level, and it keeps improving for everyone. The difference is what surrounds the model. When Jyper costs a trip, the numbers do not come from the model’s imagination: purpose-built tools look each rate up in your supplier database, convert currencies at real rates, and apply your margin rules as arithmetic. The model orchestrates; the tools compute. Every figure on the sheet traces back to the contract line it came from.

A general chatbot has none of that scaffolding. Ask ChatGPT to cost a Dubrovnik incentive for forty people and it will produce something confident and plausible — with numbers it invented, because it has never seen your contracts. Our own playbook is blunt about this: rates, availability, inclusions and anything contractual must always be verified by a human. That rule exists because in this industry, one improvised rate on a signed quote is real money. Jyper was built so the improvising cannot happen.

The quiet limitation of every chat assistant is that it belongs to one person. The prompts that work live in someone’s head; the useful threads live in someone’s history; the “company knowledge” is whatever that person pasted in this morning. When your best ChatGPT user goes on holiday, the capability goes with them. Nobody else can see the work in progress, review it, or take it over.

A quotation desk needs the opposite: a shared file everyone can open, a supplier database and CRM the whole team draws from, a review-and-approve step before anything reaches a client, work management for bookings, payments and deadlines — and analytics on the proposals themselves, so you learn which quotes get read and what converts instead of sending documents into silence. That layer is not an add-on to Jyper; it is most of what Jyper is.

For one person, a ChatGPT subscription is cheap and we happily recommend it. Running an operation on it is a different sum. The visible line is seats — one for every person quoting. The much larger, invisible line is time: skilled people pasting context in, checking numbers out, reformatting text into templates, and rebuilding it all again for every revision. The assistant made each step faster, but every step still consumes your team’s hours — so your cost per quotation still scales with headcount, which is precisely the ceiling you were trying to remove.

Jyper’s model is built for the opposite curve: unlimited seats, no usage meter, and a commission only on business you actually confirm through Jyper. No confirmed booking, no fee. The economics are the employee’s, not the software’s — which is the honest way to price something that claims to do the work rather than assist it.

Standardise your team on one assistant and you quietly marry one company’s roadmap. The frontier changes hands every few months — OpenAI, Anthropic and Google keep leapfrogging each other — and the custom GPTs, saved threads and habits your team builds inside one product do not move when the leadership does.

Jyper is deliberately model-agnostic: it routes each task to whichever frontier model currently does that job best, and upgrades under the hood as the frontier moves. When a better model ships — from any lab — your AI employee simply gets smarter that week. You never migrate, retrain the team, or rebuild a workflow.