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.

18/06/2026

The internal tool a team needs most is usually the one IT never gets to.

Too small for the roadmap. Too specific to outsource. Too useful to keep doing by hand.

A client's sales team had exactly this with their own case archive.

Years of projects lived in people's heads, in a 250MB file, and in a slide deck you could only search with Ctrl-F.

Every new proposal started with the same question: have we done something like this before, and where is it?

So two of them — managers, no engineering background — built the fix themselves.

A two-day hackathon, a few weeks of mentoring. The result is a search service: ask a question, get a short answer and cards of the relevant past projects, links included.

Underneath, a vector index of the archive and an LLM to clean up the phrasing.

The first version held together with workarounds. The clean idea — auto-syncing the archive from the slide deck — never worked.

So one of them packaged the data from a presentation overnight and shipped that instead.

This is the work that never reaches the IT backlog. When a manager can build the first working version themselves, it doesn't wait three to six months for a spec-to-release cycle.

That's what our vibe-coding program is for: managers without a technical background build their own internal tools, while IT sets the guardrails — approved data sources, repositories, review.

Not shadow IT. Not replacing engineers. Closing the gap they were never going to get to.

What's sitting in your backlog right now, waiting for IT bandwidth that isn't coming?

16/06/2026

Every team has a process that "everyone knows how to do" — and nobody questions how long it takes.

For one crop science company, that process was reading scientific trial PDFs.

Analysts on the agronomy and regulatory teams pulled structured fields out of each paper by hand — Population, Exposure, Comparator, Outcome.

One article took two hours. They handle hundreds a year.

We built an AI assistant to extract those fields. But the part that mattered most wasn't the model.

Before writing any code, we had to lock the extraction schema. That surfaced disagreements between research leads about what even counts as a "correct" extraction.

Resolving that — agreeing on the schema and the evaluation scheme in week one — was the highest-leverage hour of the whole project.

Skip it, and you build a fast pipeline that produces answers nobody trusts.

The second non-obvious decision was the retrieval. A single field scatters across methods, results, and supplementary material.

So instead of fetching the three best passages, we widened the candidate set and let the model filter it down.

Missing a fact costs more than reviewing an extra candidate. Recall on per-field extraction climbed into the 90s with no precision loss we could measure.

And the analyst stayed in the loop by design. The interface puts each extracted fact next to the source passage it came from, so confirming or fixing it takes seconds.

The results:
1) Two hours per article dropped to fifteen minutes.
2) Over 90% accuracy.
3) Roughly 875 analyst hours saved a year.
4) Pilot delivered in seven weeks.

The AI didn't make the team smarter. It moved them from doing the work to checking it — and that only worked because everyone agreed on what "right" looked like before the first line of code.

What's the manual process on your team that everyone just accepts takes hours?

09/06/2026

A non-developer in HR built a working pay-analytics dashboard in two weeks — in the spare minutes between her real job.

For years, an HR team had the salary data they needed sitting right inside their hiring system.

They never used it. Pulling numbers by hand across thousands of candidate records took so long that the source just sat there.

This is a client of ours. The data was all there — pay by role, by seniority, by stage of hiring.

But there was no tool to read it. The vendor never built one, and a custom dashboard meant an IT project nobody had the time or budget to start.

So a non-developer on the team built it herself — in the gaps between her actual job, over about two weeks.

She didn't figure it out alone. We run a two-day format where a manager ships their first working tool, then keeps going with a few weeks of mentorship.

IT stays in the room and sets the guardrails: which data sources are fair game, where things get deployed.

What she ended up with imports a fresh export, maps the columns on its own, and breaks pay down by role, seniority, and team.

A source untouched for years is now something she opens on a normal Tuesday.

What data are you sitting on because "using it" sounds like an IT project?

04/06/2026

"Imaga treated our project like their own."

That's how the technical co-founder of an early-stage startup described the work — after we shipped his MVP.

He came to us with mockups and one goal: a working product he could put in front of investors.

We spent the first month on Discovery, before a line of code. Our analyst reviewed every task a week ahead of the developer, so gaps in the logic got caught early.

We split the build so the hard parts never blocked the rest, and demoed every two weeks.

Discovery to MVP: under five months.

When you're building something new, the partner you pick decides more than the timeline.

They decide whether the concept ends up solid enough to show the people who approve the budget — or fund the round.

"Their team fully embraced the product and brought strong expertise across several areas of development. Their work on our MVP helped us clearly demonstrate the concept and attract investment." — Alexandre Francisco, Technical Co-founder

03/06/2026

We took an e-commerce agent to ECOM1 and placed 1st overall in Lisbon

At Imaga we study how agents behave across different domains. This time — in e-commerce.

ECOM1 — a benchmark where 100+ participants connect an agent to a simulated store and get scored only on what the agent actually does.

The real events: payments completed safely, refunds kept within policy, no customer data leaked, no discount it wasn't allowed to give.

Our developers, led by CEO Dmitriy Alexeev, built an agent for the cases that drain support teams: processing returns, tracking down missing packages, recovering failed payments. All under a fixed policy book, while a "customer" pushes it to break the rules.

The agent took 1st place overall in Lisbon, where the award was handed out at Joom’s office, and ranked in the top 3 for speed worldwide among the 100+ participants.

Thanks to for running ECOM1 and to Joom for opening their Lisbon office for the finals and the awards.

28/05/2026

Six months ago she called LLMs “a junior that tells you to jump off a bridge”. Last week she built a three-day report in six hours.

A client asked one of our financial analysts for a conversion funnel built on their CRM data. They wouldn't hand over the CRM data, and they needed the funnel the next day.

She fed the model anonymized dashboard screenshots and asked for the funnel, the hypotheses, and the visuals.

It proposed — she judged. Half the hypotheses got cut by a human who knew the business.

The report took six hours and under a dollar in tokens, against three days of spreadsheet work.

The speed came from the model clearing the routine. A human still decided what was true and caught the stray characters it slipped into the output.

That's why Imaga keeps a human in the loop.

Forward this to whoever on your team is still doing the three-day report by hand.

Six months ago she called LLMs "a junior that tells you to jump off a bridge." Last week she built a three-day report in six hours.

She's a financial analyst at a client company — not a developer.

We'd just run our vibe-coding factory with her team: a two-day hackathon, then mentorship, teaching non-technical people to build their own tools.

Then a real task landed on her: a conversion funnel on the company's CRM data, due the next day, with no access to the CRM data itself.

Analyst fed the model anonymized dashboard screenshots and asked for the funnel, the hypotheses, and the visuals.

It proposed — she judged. Half the hypotheses got cut by a human who knew the business.

Six hours and under a dollar in tokens, against three days of spreadsheet work. The model cleared the routine; she still decided what was true and caught the stray characters it slipped into the output.

The win was hers, not ours.

That's the point of the format: people with no technical background ship their own discovery-MVPs, internal automations, dashboards, and micro-agents — with IT setting the guardrails, and anything sensitive starting on anonymized or synthetic data.

The goal of our vibe-coding factory is an internal champion in your team who keeps shipping after we leave.

If your managers are still waiting months for IT to ship the small things — that's what this is for. Tell us where it hurts: [email protected]

27/05/2026

Eid Al Adha Mubarak!

May this blessed holiday bring peace, kindness, prosperity, and joy to every home.

26/05/2026

branding.imaga.ai won at the 18th Web Excellence Awards 🎉

Our website for branding services won in the Website category, in the Design Agency and Professional Services subcategories.

Together with last year’s CSS Design Awards and Awwwards recognition, that brings branding.imaga.ai to three awards and five gold placements.

21/05/2026

A corporate site with 40+ companies is rich in content. That richness is exactly why visitors get lost.

AKFA Holding is one of Central Asia's largest conglomerates — 40+ companies across construction, appliances, tourism, healthcare, education.

We built them a RAG chatbot in one month. Four things make it work:

1) It grounds every answer in published site content and uploaded internal docs.

2) The LLM generates phrasing, indexed content provides the facts.

3) When nothing matches, the bot says so — no improvisation.

4) A visitor can ask a question without using a single word from the site. RAG does the matching.

Content managers run the bot without developers. They update content, system prompts, and error messages in three languages directly in the CMS. Changes go live within 10 minutes — only changed chunks get re-indexed.

Read the full case study — link in comments.

15/05/2026

Hiring: Data / Analytics Engineer.

The role is for a NASDAQ-listed US company. 1-year contract, full-time, fully remote.

Qualifications:
— 2–4 years in analytics engineering, data analytics, or data engineering
— Strong SQL for transformation and analysis
— Hands-on experience with dbt
— Working knowledge of Looker and LookML
— English B2 or higher
— Availability during core hours until 3:00 PM EST

Nice to have: Snowflake, GitHub + CI/CD, data quality testing, Tableau or Power BI.

Full job description: https://docs.google.com/document/d/1Yu30DFftClfkFVWMWJr_XDwfe7k-kNjI9T4YQwNEWmc

Send your CV to [email protected]

Address

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

Website

https://go.imaga.co/fb

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