To choose the right AI provider, ask for evidence instead of capability lists: a named engineer who answers technically, at least one case with a measurable result, and a clear methodology from POC to production. Honestly compare in-house, agency, freelancer, and off-the-shelf, then drop any provider promising ROI before seeing your data.
- In short: choose an AI provider on evidence (cases with results), not on a capability list.
- Ask for a named engineer, project data, and a POC → production methodology.
- Honestly compare the four options: in-house, agency, freelancer, off-the-shelf product.
- Red flags: no named engineer, no case proof, vague capabilities, ROI promises with no data.
What makes an AI provider "serious" versus one that just sells?
A serious provider shows you how they solved a concrete problem, with numbers, and tells you what they cannot do. One that just sells shows you a long list of technologies and a slogan about transformation. The difference shows in three things: there is a named engineer who answers technically (not only an account manager), there is at least one documented case with a measurable result, and there is a clear methodology from POC to production. At Sapio, the founder — Vlad Tudor, AI engineer, Forbes 30 Under 30 and TEDx speaker — is the engineer who discusses the project, not a face on the "about us" page. That is not a quality guarantee in itself, but it is a good filter: if you cannot reach the person who will build, it is hard to judge whether they know what they are doing.
In-house, agency, freelancer, or off-the-shelf product — how do you compare them?
Before you look for an agency, ask whether you need one. There are four ways to get an AI solution, each with a different trade-off between cost, speed, control, and maintenance. The table below puts them side by side on the criteria that matter when you decide. An honest provider will tell you themselves when you would be better served by an off-the-shelf product than a custom project.
| Criterion | In-house team | AI agency / studio | Freelancer | Off-the-shelf product |
|---|---|---|---|---|
| Upfront cost | High (salaries, hiring) | Medium | Low–medium | Low (subscription) |
| Time to first result | Long (hiring + ramp-up) | Short–medium | Short, but fragile | Immediate |
| Control and customisation | Maximum | High (dedicated project) | Medium | Minimal |
| Continuity risk | Low (if you retain the team) | Low–medium (clear contract) | High (single person) | Low, but vendor lock-in |
| Best when… | AI is a strategic differentiator | You want custom without an internal team | You have a small, well-defined task | The need is standard, no specifics |
For market context: the business press (business24.ro) reports that the lack of qualified staff is one of the main barriers to AI adoption in Romania. That explains why many firms choose a studio or agency over building an internal team from scratch — hiring AI engineers is slow and expensive.
What questions do you ask in a first call with an AI provider?
Good questions quickly separate providers who deliver from those who sell. Use the list below in your first conversation; concrete answers are a good sign, evasive answers are a warning.
- Who will actually build the solution, and can I talk to that engineer?
- Can you show me a similar case, with the problem, the solution, and a measurable result?
- What does the path from POC to production look like, and what do we decide at each stage?
- What happens to my data — where is it processed and who has access?
- What can the solution not do, and where is human validation still needed?
- What does post-launch maintenance look like, and who is responsible if something breaks?
What are the red flags in an AI provider?
Some signs flag risk before you sign anything. None is an automatic disqualifier, but several together mean it is worth looking elsewhere. We collected these from what we see when a company comes to us after a failed project with another provider.
- No named engineer — you only talk to sales and never reach who builds.
- No case proof — impressive capabilities, but no project with a concrete result.
- ROI promises before they have seen your data or process.
- A long, vague technology list with no clear link to your problem.
- Not a word about data, GDPR, or what happens when the model gets it wrong.
- Refuses a small pilot and pushes straight to a large contract.
How a provider approaches a large project with sensitive data says a lot. We built ai-aflat.ro on 500,000+ indexed legislative texts, so we can show concretely how we think about data structure, validation, and production — see the ai-aflat.ro case study.
What is the next step once you have a shortlist?
Book a free initial conversation with each provider on your shortlist and apply the questions and red flags above. Compare concrete answers, not pitches. If you want to see how we answer, you can learn more about our AI services and then book a free initial call with the Sapio team. In that call we assess whether your project fits an AI Technical Audit (our paid 2–4 week service) or whether a direct pilot makes more sense. The initial call is free; the audit, if you choose it, is paid.
The lack of qualified staff is reported as one of the main barriers to AI adoption among companies in Romania — survey cited by business24.ro.
Frequently asked questions
Which is better: an in-house AI team or an agency?
It depends on how strategic AI is for you. If AI is a core differentiator and you have the time and budget to hire, an in-house team gives you maximum control. If you want results without building a team from scratch, a studio or agency delivers faster. The table in the article compares the four options on cost, speed, control, and maintenance.
How do I check whether an AI provider is serious?
Ask for three concrete things: a named engineer you can talk to technically, at least one documented case with a measurable result, and a clear methodology from POC to production. If you only talk to sales, see no case, and are promised ROI before your data is seen, those are red flags.
Is it normal for a provider to ask for a paid audit at the start?
Yes, if the audit is separate from the sale and has a clear deliverable. At Sapio, the initial call is free, and the AI Technical Audit (2–4 weeks) is paid and produces an assessment of infrastructure, risks, and an ROI roadmap. What is not normal is being charged for a simple first conversation.
What do I ask about my data in the first call?
Ask where the data is processed, who has access, and what happens to it after the project. A serious provider has a clear answer about the processing location and about GDPR. The absence of any mention of data or confidentiality is a red flag, especially if the project touches personal or sensitive business data.
How many AI engineers are available on the Romanian market?
Few relative to demand. The business press (business24.ro) reports the lack of qualified staff as one of the main barriers to AI adoption in Romania. That is why hiring an internal team is slow and expensive, and why many firms choose a studio or agency to get results faster.
Want to discuss a project?
Book a free discovery call with the Sapio team.