What does a technology company
look like after AI?

We have been building the answer since 2015. A deep tech venture studio — partnering with ambitious organisations to build AI-native products and companies. Real partners. Real ventures. Real customers. The work is below.

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Thinking

The technology model
has run its course too.

For years, the model made sense: you had the domain, the market, the customers. We had the technical depth — the software, the data pipelines, the AI systems. We built, you deployed, and value was created at the handover. That handover is now the problem. AI moves faster than any delivery cycle can follow. The gap between what we could build and what the market already expects is measured in weeks, and no project-based model survives that.

The deeper shift is this: software and data are no longer the product — they are the fuel. The companies that understand this are not commissioning better technology. They are embedding AI into the operating layer of the business itself, into how revenue is generated, how operations run, how talent is deployed. Continuously, not in releases. The vehicle is yours — the domain, the expertise, the relationships, the market reach. What we bring is the engine, and the conviction to run it alongside you.

So we changed the model. We deploy AI by building and operating the systems that drive your growth, not by handing them over. We eliminate the operational drag that consumes your best talent without creating value. And we help you stay on the front of the technological wave — not as advisors watching from the shore, but as partners with skin in the game. We share the risk. We grow with you — or we do not grow.

01
Deploy AI into your revenue layer

We build and operate the AI systems that directly drive growth — not prototypes handed over to your team, but running infrastructure we stay responsible for. The output is measured in outcomes, not deliverables.

02
Turn operational drag into capacity

The work that consumes your best people without creating value is solvable at a level that was not possible two years ago. We eliminate it systematically — so that what remains is almost entirely the work that matters.

03
Stay on the front of the wave

The technological curve is not slowing. Most companies are already locked into a catch-up cycle — reacting to change rather than shaping it. We help you build the capacity to move with the frontier instead of behind it.

The consulting era is over. This is what comes next.

Ventures

We lead with
our own bets.

AI Revenue Operating Systems

What does business consulting become in the AI era? This is our answer.

We built the engine for AI-native business transformation. Not a methodology, not a framework — a complete revenue operating system that embeds into how a company grows, sells, and operates. Transformia is our conviction, running in production: that the future of consulting is not advice, it is capability deployed continuously alongside the business that needs it.

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Data Intelligence · Green Energy

A powerful data asset, built with a partner. Now turning into value.

We crafted a deep intelligence layer for ESG permitting and environmental data — in partnership with one of Spain's leading environmental consultancies. What started as structured knowledge is becoming something more: an insights delivery service for the green energy ecosystem. DueData is our proof that a traditional business model can be transformed into a data intelligence bureau — the way Moody's Analytics transformed credit, but for the permitting and environmental complexity of renewable projects.

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Enterprise AI · Financial Operations

Software as a vessel for services. Built by brilliant people, for operations that cannot fail.

We believe the future of enterprise software is not a product you buy — it is a service layer you operate. We are part of a team of builders delivering the next generation of back office automation for financial institutions: document validation, lending origination, the processes where error has consequences. Built on open source foundations, designed for the FSE industry, and engineered to the standard that sector demands.

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Partners & clients

Companies that trusted us
to do something unusual.

Mapfre
Unicaja
Heineken
Forwardkeys
Tecnalia
Ideas
Eraneos
IDSA
XRF
Medyther
Cade
Surpan
IEBS
Sistrol
JCCM
How we work

We don't advertise services.
We start conversations.

— Let's talk about:

01 Capability building

AI Enablement

Stop buying software. Start building capability.

02 Process transformation

AI Adoption

Change the process. Not the tools.

"We bought three AI tools last year. None of them stuck."

That pattern is almost universal — and it's not a tool problem. Tools work when your team has the mental models to use them well, the domain knowledge to prompt them precisely, and the confidence to adapt when outputs are wrong. We build those things. Not a software rollout. An embedded curriculum, tied to your actual work, that compounds as your team does.

What does an AI-capable team actually look like?Is this training, consulting, or something in between?How do we start if our team is sceptical?

"We ran a pilot. People used it for a week, then went back to how things were."

Adoption fails at the process layer, not the tool layer. If AI is dropped into an existing workflow without changing the workflow, it becomes an extra step — and extra steps get skipped. We redesign from the knowledge model outward: how your domain expertise is structured, where AI fits naturally, and how to make the new way of working feel inevitable rather than imposed.

What is a knowledge model and why does it come first?How do you change processes without breaking what works?What does adoption look like six months after go-live?
03 Data & intelligence

AI-Native Data Strategy

Your data lake was built for a world that no longer exists.

04 Industrial scale

AI at Scale

Where off-the-shelf ends, we begin.

"We have data everywhere. We're not sure it's working for us anymore."

It probably isn't. Most data architecture was designed for recovery, compliance, and quarterly reporting — not the kind of contextual, real-time reasoning that AI systems need. The shift is from storage to meaning. We help you reorient what you already have: unstructured knowledge surfaces, domain context made machine-readable, the BI dashboard becoming an agent that can reason about your business.

Do we need to rebuild everything, or can we reuse what we have?What does agent-ready data actually look like in practice?Where do you start when everything feels interconnected?

"The demos work perfectly. The moment we deploy, everything breaks."

Consumer AI products are built for the average case. If your requirements are regulated, high-stakes, or genuinely complex, average isn't good enough. Off-the-shelf ends where your specific problem begins. We design and build AI systems for industrial use: hardened for regulated environments, architected to survive the next wave of the technology, built to outlast a vendor's product cycle.

What does a production-grade AI system need that a prototype doesn't?How do you build for regulation without getting stuck in the past?What does industrialising AI actually mean for our team?
05 Frontier work

Deep Technical Partnership

A novel idea. A serious technical problem. We work on those.

06 European perspective

AI Economics, Ethics & Regulation

European regulation is not a burden. It is a head start.

"We have an idea that doesn't fit any service description."

That's often where the most interesting problems live. If your challenge sits at the intersection of a novel domain, hard technical constraints, and genuine uncertainty — we want to hear about it. Not every engagement needs a predefined shape. Some start as a conversation, sharpen into a focused technical problem, and become something neither party anticipated. We are comfortable working in that space.

What kinds of problems have you taken on this way?Is this a venture, a contract, or something else?How do you decide what's interesting enough?

"We keep hearing about the AI Act. We don't know what it means for us yet."

AI regulation in Europe is moving faster than most companies' internal roadmaps. The AI Act, the European Data Act, and the emerging data space infrastructure are not just compliance frameworks — they represent a structural shift in how data is owned, shared, and monetised across sectors. Companies that treat them as checklists will spend resources on risk avoidance. Companies that treat them as market infrastructure will build on top of them. We help you understand what the regulations actually require, identify where your products and data practices need to adapt, and — where the opportunity exists — position you to participate in the emerging European data economy.

What does the AI Act actually require from a company like ours?How do European data spaces work, and are they relevant to our sector?Can regulation genuinely become a competitive advantage, or is that just spin?
Contact

Interested?
So are we.

We are a small team with a limited number of active partnerships. We are not looking for clients — we are looking for the right kind of problems. If you think yours qualifies, we would like to hear from you.

info@taidy.io