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.

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]

12/05/2026

You're comparing three AI agent proposals. One pitches GPT-4o. Another Claude. The third — a fine-tuned model.

If that's how you're evaluating, you're optimizing the wrong variable.

An AI agent is just a loop. The model looks at a task and returns one of two things: "call this tool" or "I'm done."

The orchestration code runs the tool, feeds the result back, and asks again. The loop repeats until the model says done.

❗️ The LLM has no memory between steps. It doesn't know it's an agent.

Everything that determines whether the agent works lives in the orchestration — the system prompt, the tool definitions, the rules for when to override the model with deterministic code.

Same agent, same task — 100% accuracy in the morning, 60% in the afternoon. That's not a bug to fix. The model is stochastic.

The fix is knowing which steps shouldn't be LLM-decided at all.

Three questions to ask any vendor before you sign:
1) Where in the loop do you switch from LLM to plain code?
2) What happens when the model returns the wrong tool call?
3) How do you catch four invoices going to the wrong email because one letter in the address differs?

If the answer is about which model they'll use — they're selling you the easy part.

The hard part is deciding where AI stops.

What's the one task in your product where a wrong answer costs the most? Curious where others are drawing the line.

04/05/2026

The cheapest way to make AI automation unreliable: give the model everything you know at once.

Good AI systems do the opposite — they decide what the model sees at each step. Claude skills are a clean example of how this looks in practice.

Under the hood, a Claude skill is a well-prompted link to a file with instructions. The model loads those instructions only when they're needed for the specific task — not all the time.

The same trick used to be done through references in CLAUDE.md, but skills added discoverability, invocation control, and arguments.

The principle stays the same: the model sees exactly what it needs for the step it's on.

For a business automating processes with AI, this is a dividing line. If your AI system gets the full context every time — it will fail unpredictably.

Systems that hold up over time work the other way around: the architecture decides which instructions and which data the model sees at each step.

When you evaluate a vendor for an AI project, ask them how their system decides what to show the model at each step.

If the answer is "we put everything in the prompt" or "the model figures it out" — the automation will work in the demo and break in production.

How is yours set up right now: does the AI system get one large context, or do different steps work with different instructions?

Address

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

Website

https://go.imaga.co/fb

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