Insights · 9 min read
Most AI firms sell the same demo. Here is how to separate the ones that have shipped production systems from the ones with a slide deck, and the questions that reveal the difference.
By GGP Editorial
Search for an AI development company and you will find hundreds of firms making the same three promises. They know the latest models, they move fast, and they will turn your idea into a product. Most of them cannot show you a single system running in production. Choosing well comes down to one skill: telling the firms that have shipped from the firms that have a good slide deck.
I run an AI practice inside a software company, and I have also sat on the buyer's side for other projects. What follows is the checklist I actually use. Not a list of nice-to-haves.
An AI development company is not one kind of business. It covers three, and you need a different one depending on where your project sits.
The first is the wrapper shop. These firms build a thin layer over someone else's model API, usually one of the big-name models, and call it a product. For a quick internal tool that one team uses, that can be fine. For anything that touches real customer data, scales past a few hundred users, or has to meet security or compliance rules, it falls apart.
The second is the AI-native product studio. These teams know models deeply and are good at experiments and prototypes. Where they often struggle is the part that is not AI: database work, integrations, deployment, and keeping a system running for years after the launch party.
The third is a software company with an AI practice. These firms build the whole product and treat the model as one component in a larger system. If your project has to plug into existing software, handle payments, or survive an audit, this is the group that tends to deliver.
| Type | Good at | Typical weakness |
|---|---|---|
| Wrapper shop | Fast, cheap prototypes | Fails on real data, scale, compliance |
| AI-native studio | Models, experiments, novel features | Integration, operations, long-term support |
| Software company with AI practice | Full product, integration, deployment | Sometimes less flashy on research |
The firm that is hardest to evaluate is the one hired for "we want AI." Vague scope invites vague quotes and vague results.
Before you contact anyone, write down three lines. The workflow you want to change, the data it runs on, and the number that tells you it worked. That last one matters most. "Better customer support" is not a metric. "First response time under ten minutes" is.
When you can hand a vendor those three lines, the conversation shifts from a sales pitch to a plan. You also stop paying for discovery work you could have done yourself.
Most AI projects live or die on data, not on the model. The model is a commodity now; everyone calls the same few APIs. What differs from firm to firm is how they handle your data.
Ask what data the system needs, where it lives, and how it gets cleaned. If a firm treats data as an afterthought, the project will stall at the exact point it matters. The firms worth hiring ask about your data before they ask about the model. A firm that jumps straight to "we will build you a chatbot" without asking what the bot reads is selling you a demo.
This is also why you should be wary of anyone who quotes a price before seeing your data. Data quality is the usual surprise in these projects, and a price set before anyone has looked at it is a guess.
Four things tell you whether a firm has actually shipped AI, rather than run a workshop.
Production systems. Ask to see a live product, not a video. Ask how the system handles a slow model, a failed request, and a spike in traffic. Those are the questions a demo never answers, and they are where production AI lives or dies.
Data handling. Ask where the data sits, who can see it, and whether it is used to train models. If the firm does not have a clear answer, walk away. This one question separates professionals from hobbyists.
Evaluation. Ask how they know the model is working. A serious team can show you a test set, an accuracy measure, and a way to catch when the model starts drifting. A firm that says "we eyeball it" has not shipped anything real.
Ownership. Ask who owns the code, the prompts, and the data when the project ends. You should own all of it. If the answer is vague, get it in writing before you sign.
A short list that reveals a lot.
| Question | What a good answer sounds like |
|---|---|
| Show me a live production system | A URL and a walkthrough, no hesitation |
| How do you handle a failed model call? | Retries, fallbacks, and a measured cost per request |
| Who owns the code and prompts? | You do, written in the contract |
| How do you measure model quality? | A test set, a metric, and a drift check |
| Where does my data live? | A named location, with a clear policy |
Three signs you should end the call early.
A firm that promises a fixed price before understanding your data. Real AI projects depend on data quality, and data quality is usually the surprise. Anyone who prices before looking is guessing.
A firm that cannot name a model or an approach until they "explore." Some exploration is normal. A total blank is not. You want a team that has opinions.
A firm where every previous project is an internal prototype. Ask for a reference whose product customers pay for. If none exists, neither does a track record.
The cheapest way to de-risk the choice is a paid pilot with a tight scope. Pick one workflow, agree on a metric, and cap it at a few weeks. A firm that resists a small paid trial is telling you something.
A pilot also answers the question that matters most: how does this team communicate? You will learn more about working together in two weeks of a pilot than in any number of sales calls. Weekly demos, a named point of contact, and decisions made in days rather than weeks are the things to look for. If the pilot feels like shouting into a void, the full project will feel worse.
I should be honest about where GlobeSoft sits in this market. We are a software company founded in 2018 with more than 40 engineers and over 300 delivered projects, and AI is a practice inside that, not the whole company. We build the full product, and the AI component plugs into it the way a payment system or a database does.
We work in Java, Spring Boot, and Spring Cloud on the backend, Vue and React on the front, and we have shipped for clients in Brazil, South Africa, Singapore, and the US. We talk to clients in English and Portuguese, across time zones, with a fixed overlap window and a dedicated group so decisions do not die in email.
If you are still weighing options, the test is simple. Ask the four questions above of every firm, including us. The ones worth your time will answer them directly. We would rather be compared on that basis than on a polished pitch. If you want to understand the wider picture of what AI work costs and how it is priced, our AI development cost article and our piece on what you are actually buying with AI development services are worth reading first.
How much should an AI project cost?
It depends on the data, the workflow, and whether the model plugs into existing systems. The number only makes sense after someone has looked at your data. Anyone who quotes before that is guessing.
Should I choose a specialist AI firm or a software company?
If the project is pure research, a specialist studio is a good fit. If it has to integrate, scale, or meet compliance, a software company with an AI practice is usually safer. The difference shows up in the parts of the project that are not AI.
How do I know a firm's AI actually works in production?
Ask for a live product and a named reference whose customers pay for it. Ask how they measure quality and handle failures. A team that can answer those questions has shipped. One that cannot, has not.
Do I own the AI they build for me?
You should. Code, prompts, and data should all be yours, stated in the contract. Walk away from any firm that is vague on this point.
What if my project is small?
Start with a paid pilot on one workflow. You will learn whether the team fits before you commit to a full build.
Choosing an AI development company is mostly a matter of asking the questions that force a real answer. Do that, and the choice gets easier than the market makes it look. If you want a second opinion on your specific project, send us the outline and we will tell you whether it is a build, a pilot, or something you should buy off the shelf.
Tell us what you are building and where you are today. We typically reply within 24 hours.
