Choose an AI development company by how it handles production, not by its demo. The right firm builds into the systems your team already uses, tests on your real data, keeps a person approving the output, logs every action, names who runs the workflow after go-live and leaves the accounts and the off switch with you. Ask for each of these in writing.
#Key takeaways
- A demo on prepared data proves little. Ask to see it run on your inputs.
- The build should sit in your tools and your accounts, not in a platform you rent.
- Ask how quality is measured after go-live, not just before it.
- Security questions for AI include prompt injection, not only the usual checklist.
- Know who fixes it when it breaks, and how your team takes it over.
#What does a custom AI build need that a demo does not?
A demo shows a model can do a task once. A production workflow has to do it every day on messy inputs, with someone accountable for the output. That gap is where projects stall: Gartner expected 30% of generative AI projects to be abandoned after proof of concept by the end of 2025 (Gartner, july 2024).
Ask each company what it adds between demo and production: test sets built from your data, a review step where a person approves, logging, monitoring of error rates, and a plan for exceptions the AI should never handle alone.
#Which technical questions should a non-technical buyer ask?
- Where does the model run, which accounts is it in, and who owns them?
- How do you measure accuracy on our data, and what happens when it drops?
- How do you protect against prompt injection? It is the first risk in the OWASP top 10 for LLM applications.
- What is logged, who can see the log, and how long is it kept?
- Which steps are automated, and which does a person approve?
You do not need to judge the engineering. You need specific answers, written down, that your it or security lead can check.
#How do you compare an AI development company with an offshore dev shop?
Many firms that rank for "AI development company" are general software agencies that added AI to their service list. That can work for a simple feature. For a workflow inside your operations, ask whether the firm will map the process with the people who run it, test in Arabic and English on your files, and stay accountable after go-live. If the answer is "we deliver the code", the running is left to you.
We are
Stennir builds one workflow into your own tools, tests it on your data in Arabic and English, keeps a person approving, then runs it as managed AI operations.
We aren't
Stennir is not software you rent and log into, and not a dev shop that hands over code and leaves.
#Who runs the workflow after go-live?
This is the question most proposals skip. Models change, inputs drift and staff turn over. Someone has to watch error rates, update rules and add the next workflow. At Stennir that is managed AI operations, with a named owner. In AI implementation, you pay for finished work, and the terms are agreed on the call.
#What should an operations lead do next?
Pick the workflow you want built, then send the questions above to each company. See how Stennir approaches it on AI implementation and AI for operations, compare Stennir with hiring in-house, or book a 30-minute call.
#Common questions
Should we build custom AI or buy an off-the-shelf tool?
Buy when a tool already does your workflow the way your team runs it and your data rules allow its hosting. Build when the workflow crosses several of your systems, depends on your own rules or must run in a specific region. Many companies do both: a bought tool for common tasks, a custom build where the process is theirs.
Who owns the code and the accounts after a custom AI build?
Ask before you sign and get it in writing. At Stennir the accounts are in your name, the data stays under your control and your team holds the off switch. If you stop working with Stennir, the workflow keeps running or stops, as you choose. Hosting follows where your rules require.
What could go wrong with a custom AI build, and how is it caught?
The AI can misread a document, apply a rule wrongly or be manipulated by text hidden in an input. That is why a person approves the output, why every action is logged and why test sets from your own data are rerun after each change. Mistakes get caught at approval, and the log shows where to fix them.
Does Stennir work in Arabic as well as English?
Yes. Arabic and English are fully supported, and other languages are available on request. Stennir tests every workflow on your real documents and messages in both languages before your team relies on it, rather than translating an English build at the end. Mixed-language inboxes are common in the Gulf, so they are tested too.
