imaga

Custom websites, software, web and mobile solutions for teams who care about
★ UX
and
★ AI strategy grounded in product needs

FOR TEAMS WHO CARE ABOUT
1) UX,
2) AI strategy grounded in real product needs. We implement everything end-to-end with a single team accountable for results. Our PORTFOLIO INCLUDES marketplaces, e-commerce, workflow automations, AI assistants, corporate websites, banking apps, data warehouse initiatives, and more. IF YOU ARE BUILDING A NEW PRODUCT OR SCALING A PRODUCT, we can start with an MVP to validate fit in real workflows, deliver measurable ROI early, and minimize the risk of wasted budget, missed deadlines, or business disruption. IF YOU ARE (RE)BUILDING A WEBSITE, we can deliver a fast, SEO-ready site and include GEO (for LLM visibility), analytics/tracking, CMS, CRM and payment integrations, and ongoing support. IF YOU ARE ADOPTING GENERATIVE AI, we help you evaluate feasibility, quality, and cost by measuring baseline time/cost/error rates, checking privacy and confidentiality, and then embedding agents into existing tools with logging and monitoring of time and cost saved. WHAT HAPPENS NEXT:
1) You write to us to clarify the details of your challenge.
2) We help define the scope.
3) We define key architecture choices during the proposal phase.
4) In 2–4 days, you receive a tailored proposal with project estimates. Our delivery is built around a DATA-DRIVEN CYCLE (Decisions ↔ Data) that connects Discovery to Delivery through product analytics and feedback loops. SERVICES:

— UX Design
— UI Design
— System Analysis
— Product Analytics
— Software Development
— AI Development
— AI Integration
— Quality Assurance
— DevOps
— Information Security
— SEO / AI Discoverability
— Support & Maintenance

We are AWARD-WINNING, with work regularly submitted to and recognized by competitive industry award programs. You can MEET OUR TEAM in person at our offices in Porto, Portugal and Dubai, UAE.

02/10/2026

5,700+ IT parts in the catalog. Not one of them could be bought online.

That was our client, an IT hardware supplier from Abu Dhabi that sold offline only. We built its first online store from scratch in five months.

With 5,700 SKUs, nobody browses. People search. So we built search for two kinds of buyers.

A buyer with a part number types it into the site search. "512743" brings up the matching HUAWEI drives right in the search bar.

A buyer who names the part their own way finds it through Google. We built the semantic core from scratch: "m.2 ssd", "m2 ssd", "nvme m.2" and four more spellings all lead to one product page.

The client went from no website to an online store open to buyers worldwide.

Read the full case study: https://imaga.ai/cases/anyitparts?utm_source=facebook&utm_medium=social&utm_campaign=case-post

29/09/2026

Between "done" and "you can review it" sits a step most clients never see

Someone has to prepare a test environment, and on many teams only a developer can do it. So your review waits until a developer is free.

One of our teams runs about 50 test environments. Preparing one meant logging in, installing Composer dependencies and building the markup by hand.

Now there's one field. Type the branch name, and the service updates the environment and pulls every dependency on its own.

The next step is giving that field to project managers. A manager will prepare an environment for business testing and send it to the client without pulling a developer off their work.

We treat the path from finished work to your review as part of the product, not a favour a developer does when they have a free minute.

Every day a finished build waits in that line, your feedback arrives a day later. So does your release.

How long does it take your team to put finished work in front of you?

25/09/2026

If you're putting together a shortlist of AI strategy partners in the UAE, Clutch has already ranked 49 of them. Imaga is #1.

Clutch is an independent B2B review platform. Its rankings rest on verified client reviews, and our position is not a sponsored placement. Behind it are 30 verified reviews with an average rating of 4.9.

Thank you to the clients who took the time to write those reviews, and to the team whose work they describe.

Before you add anyone to your shortlist, us included, read what their clients wrote. Our Clutch profile is in the comments.

22/09/2026

"Build us a funnel by tomorrow. No, you can't have the CRM data."

That request took our analyst 6 hours and $0.50 in tokens. The same report used to take 3 days in Excel — a full 40-hour week if done properly.

Six months earlier, the same analyst had written a piece calling LLMs "a junior who'll advise you to jump off a bridge."

Then a client we work with occasionally showed up with an ad-hoc request: a sales funnel built on CRM data, without access to the CRM, due the next day.

The savings aren't in the $0.50. They're in the Excel week that disappeared.

The model didn't replace the analyst — someone still has to say which hypothesis is good and which one is garbage.

Where does the human still sit in your AI workflow — before the model, after it, or nowhere?

16/09/2026

Document AI gets sold as writing. The hours come back from reading.

We built an assistant for tender paperwork.

A tender package runs 10 to 300 pages. Pulling out the list of 20–30 documents you have to submit took a person an hour and a half.

The assistant does that, plus contract-risk analysis, plus filling our company data into submission forms. (Form filling is where it errs most, so every filled form goes back to a human.)

The function that stuck is the last one. You hand it the folder with the assembled bid and it returns a list of what's missing, mis-filled, or mistyped.

There's a reason that one won. Extraction and checking are cheap to verify — the reviewer looks at the source line and confirms in seconds.

Generation is expensive to verify — you have to re-read everything the model wrote before you can trust any of it. The payback lands first where the work is reading, not writing.

That also makes it the part you can price before you start. Count how many times a year the routine repeats, multiply by the loaded hourly rate.

For a top-10 crop science multinational, trial-literature extraction went from two hours per article to fifteen minutes at over 90% accuracy. Ten reviews of fifty articles a year is about 875 analyst hours — roughly $48K at $55 an hour.

Our order is fixed: extraction and checking first, generation later, every extracted fact carrying a link back to the passage it came from. The analyst confirms. The model doesn't decide.

Which part of your document flow eats the most hours — finding the facts, or checking that nothing is missing?

08/09/2026

An AI agent can delete your files without anyone deciding to delete them.

A language model remembers nothing and writes one word at a time. Add a line to what you send it — "you can use these commands: list my emails" — and it writes back, in plain words, that it wants the list.

Nothing has happened in your inbox yet. Then a small program opens the mailbox, reads the subject lines, and pastes them into the text. The model never touched your email.

There was no order to use the command. The model wrote those words because those words looked likely. A delete command works the same way.

So the question is not which model you sign up for. What decides the damage is the list of commands you handed it, what each program can touch, and which of them run without a person saying yes.

Your team wrote that part. No model upgrade fixes it.

Design the agent from the command list backwards: every command, and what it can break.

Has your agent ever done something in a live system that nobody asked for?

04/09/2026

A redesign brief describes how the site looks. It rarely names what the site fails to do.

Ciudad Cayalá came to us for a redesign. Before we drew a single screen, we ran interviews with the three groups who land on that site: tenants, visitors, and business owners.

Cayalá is a premium developer in Guatemala — pedestrian-first streets, heritage-inspired architecture, events all year. One site was serving three different jobs at once.

The interviews changed the scope.

A large share of the buyers, tourists and business owners in the premium segment turned out to be English speakers.

The site was Spanish only. People also couldn't find their way around it, and there was no practical information about the venues on site — what's open, where it is, what's inside.

None of that shows up in a brief about visual style. All of it shows up in an hour of talking to the people the site is for.

So the work became: information architecture split into three sections by audience, a full English version, a buyer path that leads through sales materials to a qualification form matching apartments to what the person actually needs, and an interactive map of venues and services with search — including on mobile.

A redesign brief is written by the people who look at the site every day. The people who leave it never get a say — unless you go and ask them before the layouts exist, when changing the answer is still cheap.

Full case study — link in the comments.

01/09/2026

We built ten AI tools for our QA team. Then we wrote down the first rule: don't run them all.

QA is the people who check the product before your customer does.

Does the form submit? Does the data land where it's supposed to? Did last week's release quietly break something that worked fine before?

Requirements sit in one document, checklists in another, test results in a third, bugs in a fourth. A tester spends half the day stitching those together before checking anything at all.

So our QA lead assembled a pack: seven skills for narrow jobs — reading requirements, API contracts, bug reports — and three agents for the roles that execute.

One of them walks through the site in a real browser, checks the texts, titles, links and number agreement, then collects screenshots, console errors and dead requests.

The obvious move would have been one assistant that does all of it.

A tool that does everything does everything about average — and about average, as our QA lead puts it, is like shipping on a Friday evening. You can… but why?

The rule instead: run the shortest chain the task actually needs. Filing a bug doesn't require requirements analysis, API testing and the autotest writer. That's calling in a construction crew to hang one door.

The check before release runs faster and leaves evidence behind — screenshots, logs, a report with steps — instead of someone's verbal "looks fine to me."

The pack is a menu, not a set of levels you have to clear to reach enlightenment. Nobody orders the whole menu at once.

And that rule has nothing to do with testing specifically — it's how we'd wire AI into any process currently held together by people copying things between documents.

Worth passing to whoever on your team is currently scoping an "AI assistant for the whole workflow."

28/08/2026

Mawlid Al Nabawi Mubarak

To our clients, partners and colleagues observing the day —
we wish you a calm one.

Everything else can wait until Monday 🌙

18/08/2026

A greeting written by a machine reads like a machine. A reason to reach out, found by a machine and handed to a person with context attached, reads like attention.

We built an agent for our sales team that has never sent a single email to a lead. That's the whole point.

Four days before a public holiday in a lead's country, a calendar invite lands for our CEO, the deal owner, and the project manager.

Inside it: the country, the holiday, the date, and every contact we have there — name, company, deal stage, and for closed deals, when they closed and why. Each name links straight to the deal card in the CRM.

On the holiday itself, the same event fires a reminder. The event is marked "free," so it doesn't eat the day.

Then a person writes the message. From themselves, in their own words.

The numbers are what make this impossible by hand: 159 contacts, 25 countries, 255 holidays in the calendar.

This process wasn't slow before we automated it. It didn't exist.

There was no new software to buy. The CRM, the mailbox, and the calendar were already paid for.

There is no database and no code — the data sits in a handful of plain files, and three scheduled jobs read them: one refreshes the list from the CRM, one checks the dates every morning, one reads the replies.

Every sales team has work like this. It isn't skipped because people are lazy. It's skipped because no human memory covers it: renewal dates, anniversaries of a first meeting, the lead who went dark two years ago and would take a call today.

The default assumption about AI in sales is that it writes and sends. We think the useful part sits one step earlier.

A greeting written by a machine reads like a machine. A reason to reach out, found by a machine and handed to a person with context attached, reads like attention.

Which process on your sales team gets skipped because nobody could possibly remember it?

Address

Meydan Grandstand, 6th Floor, Meydan Road, Nad Al Sheba
Dubai
00000

Website

https://go.imaga.co/fb

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