AI Solutions & Custom AI Development
If everyone is telling you to add AI and nobody can say which hour of your week it would actually shorten, start with the task — not the technology.
Somebody asked in a meeting what your AI strategy is, and the room went quiet. Since then you have sat through demos, two vendors have offered to put a chatbot on your website, and a competitor has announced something on LinkedIn. None of it told you which hour of your week would get shorter.
Meanwhile the work a machine could genuinely take off your hands is in plain sight and nobody calls it AI. The same forty questions arriving in the support inbox every week. Someone retyping figures from supplier invoices into the accounting system on the last day of every month. A report that exists only because someone spends Thursday afternoon assembling it.
That is the honest starting point: the repeated task, not the technology. Some of those tasks suit a language model. Others want a search box or twenty lines of ordinary code, and cost a fraction as much. We would rather hand you the cheap answer than sell you the interesting one.
What we actually do
Working out what is worth automating
We list the repetitive work with the people who do it, then ask of each item how often it happens, how long it takes, and what it costs when it goes wrong. That last question decides most of it: a task whose wrong answer is easily corrected — a draft reply a human edits before sending — is a good first candidate; one whose wrong answer moves money or affects someone's treatment is not, unless a person signs off every time.
Building around your own material
A general model has read the internet. It has not read your price list, your return policy or the wording of your contracts, and when it does not know something it will often produce a confident sentence anyway. Most of the work in a useful AI feature sits around the model rather than inside it: getting your documents into a searchable form, retrieving the passage that answers the question, and showing the source so a person can check it.
Choosing the model for the job
There is no single best model and we have no favourite to sell you. Some work runs well on a small open model on your own servers, which matters when data is not allowed to leave them; other work needs a large commercial model through an API. We choose per use case on accuracy against your material, cost per request, and where your data may go.
What you get
- A written, ranked assessment of the repetitive work in your business, including tasks we recommend leaving alone
- An evaluation set from your real cases, with accuracy measured before launch and after every change
- The feature deployed, with source code in a repository under your account
- Prompts, retrieval logic and model configuration as readable, versioned code rather than a black box you rent
- A human review step wherever a wrong answer carries a real cost
- A written data-handling note: which provider processes what, what is retained, what stays on your infrastructure
- Readable logging, a handover session and short guides for daily users
Why Netronsoft
We say no to AI regularly. If a search box or a better-designed form solves the problem, that is what we will recommend, even though it is the smaller invoice.
We measure before launch. Nobody should put a model in front of customers on the strength of a demo that worked twice, so we build a test set from your cases and show you the numbers, failures included.
We are specific about Arabic. Quality varies by model, and again between formal Arabic and the way people actually type. We test on your content and say plainly when it is not good enough yet.
You own all of it — prompts, pipeline, evaluation set and data — and you talk to the engineers building it.
Questions people ask before they call
Do we actually need AI for this? Frequently not. Many problems described as AI problems are a missing report, an unindexed folder of documents, or a form that allows the wrong input. We say so before you spend anything.
What about wrong answers? A model can state something false with complete confidence, and that cannot be engineered away. It is reduced by grounding answers in your documents, showing sources, and letting the system say it does not know.
Where does our data go? Decided before we build. Processing can stay on infrastructure you own, or run through a provider under written terms: what is sent, what is retained, what never leaves your servers.
Does it work in Arabic? Sometimes very well, sometimes not well enough, depending on the model and on whether your material is formal Arabic or how customers really write. We test and report the result.
What does it cost to run? Two numbers: the build, and the per-request cost once live. We estimate the monthly figure at realistic volume during design, so it is a decision rather than a surprise.
Will this replace our staff? It removes the repetitive part of a job rather than the job. The useful question is which task leaves someone's morning.
Can it connect to our existing systems? Usually yes, through the same work described under our API Integrations service.
How do we know it still works after launch? Readable logging, the evaluation set re-run when the model or content changes, and an agreed routine for reviewing real answers.
Let's talk it through
Send us the task, not the technology. Describe what your team does over and over that feels like it should not need a person, and we will tell you honestly whether AI is the right tool — or whether something simpler gets you there.
Message us on WhatsApp, call +962 7 9087 9419, or write a few lines to [email protected]. A handful of the real questions or documents involved lets us answer far more sharply.
What's included
Benefits
AI where it pays, not where it demos
We start from the repeated task and its cost when it goes wrong, so effort goes into work that shortens someone's week rather than something that looks good in a meeting.
Accuracy you can see
A test set built from your own cases, measured before launch and after every change, with the failures shown to you rather than smoothed over.
A person in the loop where it matters
Wherever a wrong answer costs money or trust, review is built into the workflow — the system drafts, a human approves, and the record shows who did.
Data handling agreed up front
Before anything is connected you get it in writing: which provider processes what, what is retained, and what never leaves infrastructure you own.
Arabic tested, not assumed
Model quality in Arabic differs by model and by whether your content is formal or conversational, so we test on your material and report the result as it is.
Running costs estimated before you commit
You see the expected monthly cost at your realistic volume during design, so the per-request bill is a decision you made rather than a surprise later.
Our process
How we work
Assessment
Sessions with the people doing the repetitive work, then a written, ranked list of what is worth automating — including what we would leave alone and why.
Prototype on your data
A small working version on a slice of your real material, so the decision to continue is based on something you tried rather than a vendor demo.
Evaluation
We build a test set from your own cases and measure accuracy against it, agreeing with you the threshold that has to be met before anything faces a customer.
Build and integrate
The feature is developed into your existing systems with the review step, permissions and logging in place, and the code delivered to your repository.
Launch and review
A monitored release, a training session for the team using it, then an agreed routine for reviewing real answers and re-running the evaluation as things change.
Frequently asked questions
Very often there is a simpler answer, and we would rather you hear it from us than discover it after paying for a model. A large share of problems described as AI problems turn out to be a missing report, documents nobody indexed, or a form that allows people to enter the wrong thing. Those are cheaper to build, cheaper to run and easier to trust. We look for the boring solution first and only recommend AI when the task genuinely needs judgement over language or unstructured content.
We do not claim to eliminate it, because nobody can. A language model can produce a confident sentence that is simply wrong, and that risk is managed rather than removed. In practice we ground answers in your own documents, show the source next to the answer, keep the scope narrow, and design the system so it can say it does not know instead of guessing. Where a wrong answer would cost money or trust, a person approves before it goes out. Any supplier telling you their system never invents anything is not being straight with you.
That is settled in writing before anything is connected. Depending on what your data is and what rules you are under, processing can run on infrastructure you own using a smaller open model, or through a commercial provider whose terms we go through with you. Either way you get a written note covering what is sent, what is retained by whom, what is used for anything else, and what never leaves your servers. If your constraints rule out sending data outside your environment, say so early and it shapes the whole design.
Sometimes very well and sometimes not well enough, and the honest answer depends on the model and on your material. Formal written Arabic tends to be handled better than the mixture of dialect, transliteration and English that customers actually type. Rather than reassure you in general terms, we run your own content through the candidate models, show you where they succeed and where they fail, and tell you if the result is not yet good enough for the use you had in mind.
Two separate numbers. The build is scoped and quoted like any other project, in phases you can stop between. The running cost is per request and depends on the model, the length of your documents and your volume, so we estimate the monthly figure at realistic usage during design and pick models with that in mind — routine cases often run well on a cheaper model with the larger one reserved for the hard ones. You should never find out the running cost from your first invoice.
In the kind of work we build, it takes the repetitive part out of a job rather than the job itself. Drafting replies that a person edits, pulling figures out of documents that a person confirms, summarising a long thread before someone reads it — the human stays in the process, usually doing the part that needed them in the first place. The useful question is which task disappears from someone's morning. Anyone promising headcount reduction has not looked at how your operation actually runs.
Usually yes. An AI feature is rarely standalone: it reads from or writes to a CRM, a document store, an ERP or a support inbox, which is ordinary integration work of the sort described under our API Integrations service. If the system holding your data has no usable interface, we will tell you that before you commit, along with what the workaround would cost to maintain and whether we would advise against it.
Quality drifts. Providers update models, your own documents change, and customers ask things nobody anticipated. So the system logs what was asked and answered in a form you can read, the evaluation set gets re-run whenever the model or the content changes, and we agree a routine for reviewing a sample of real answers — weekly at first, less often once it settles. If accuracy falls below the threshold you agreed, that is a defined event with a response, not something you notice in a complaint.