Our 5 Main Messages

Executive summary

Here, the 5 dimensions you will find throughout the website:

Authority

Pioneer

Integrator

Aggregator

Independence

AI projects are too sensitive for you not to leverage the authority at their integration.

Backed by a multi-awarded Centre of Excellence that has pioneered the field since 2015. We are, amongst other things, pioneers of Agentic AI and Service-as-a-Software (Custom SaaS). We even coined the term Algorithmization as a new transformation paradigm back in the early 2010s. And we translated into a whole new discipline in the early 2020s.

We do not remove your providers but help them deliver AI with impact.

We enhance the quality of your AI projects by letting you combine solutions across providers.

We help you build your AI ecosystem - no more external dependencies on your data nor your AI. Not even on us.

International Reach

Who trust us

PRIVILEGED JUDGEMENT

Context

The future of AI is the past of Algorithmic Trading. Nevertheless, the latter is:

100% digital native > extreme levels of efficiency have been achieved.

The most aggressive competition > there's only one winner, the most precise of all agents.

THIS IS ALL ABOUT YOU

About you

You've reached us because you understand that AI is here to stay. It unlocks a period of 5 to 10 years of continuous transformation - spanning across insights and alerts up to complete new employee protocols & workflows.

And you have a challenge: you need to be more efficient in terms of both innovation cost control and success rate.

You Are Not Alone

Back in 2015 we anticipated that the vast majority of AI projects would fail. MIT put a number to that failure, 10 years later - more than 95%. Why?

Low quality of the data science projects.

High integration costs.

The solution requires deeptech

Both issues are interrelated - the higher the integration cost the lower the quality of the data science projects. Why?

Exploiting a pattern requires the orchestration of several models. This is, a model is not a solution; an algorithm (several models along with the heuristics of the expert), is.

If the deployment in production of the models is expensive, you end up forcing solutions that lack an optimal number of models. Hence, it affects the former point.

Algorithmization

The more your company becomes an algorithm, the more you can incorporate your transformation enhancements in a daily basis.

UNLOCKING THE CONTROL ON YOUR AI JOURNEY

About us

We have been relentless pioneers

Being pioneers is a sexy tag that actually takes a lot of energy and neurons. It's not pretty. But we love it.

We enjoy the challenge of taking AI integration to another level:

In 2015, we created the first Centre of Excellence (CoE) in the field. And it has fathered a new discipline across microeconomics, the L in ML (AI) and the M in ML (deeptech software design).

It has reached global recognition across industry leaders and tier one academia.

Finalist Banking Tech Awards (2017, Winner): "Best Trading Platform". Sergio's UCL PhD Thesis deployed at BBVA. It's NextGen, AlphaDynamics, is also the core of our E2E AI-first corporate software, Fractal.

Finalist CogX (2020, Winner): "Best Innovation in Simulation". Core of our Virtual Reality Simulation. It led to the first autonomous machine in AI - continuous calibration of a massively sparse Q-hypercube in Reinforcement Learning.

Finalist Banking Tech Awards (2024, Winner): "Best Tech of the Future: AI and Data". Abstraction of our Fractal's trading up to an All-In-One Platform.

Finalist Finovate (2025): "Innovator of the Year". A recognition to our pioneer work on Agentic AI a decade before the AI industry.

Finalist Banking Tech Awards (2025): "Best Tech Leader: Visionary Founder". A recognition to the accuracy of our forward-looking vision and the capacity to transform advanced theory into a scaleup company.

What's going on under the hood?

As our researchers keep listening to the challenges from our clients and their providers, our CoE keeps building relentlessly proprietary tools for AI integration.

Those trigger new methodologies for our data engineers to turbo-boost both their outreach and pace of delivery - our average Time-to-Production, from the idea to production, is 2 weeks.

We are the fathers of Agentic AI

We started off in 2015. When our competitors figured Agentic AI out for the first time, we already had almost a decade of pioneering experience. This means that not only do we have 10 years of advantage but, we argue, our peers may won't be able to foresee most of the vision we have included in the agentic evolution up to the Algorithmization of the companies.

Don't you take our word for it. Check it out yourself copying our first paper's Chapter 3 in this button and ask your preferred LLM.

Copy prompt, including Chapter 3, and check in ChatGPT

Here, feedback from our LinkedIn network when we run the experiment for a keynote at a tier one university:

San Francisco, United States

🎯 Verdict

Yes — this is a pioneer work on agentic AI.

It anticipates key architectural, operational, and governance concepts now central to agentic AI systems, even if it didn’t use the modern labels. Its holistic focus on autonomy, modularity, expert control, federation, and real-world deployment makes it stand out as foundational in the evolution of enterprise agentic systems.

If you're considering how to position or publish this historically, it would be fair to claim it as:

"A foundational architecture for enterprise agentic systems, anticipating today's agentic AI frameworks nearly a decade in advance."

New York, United States

Bottom Line

This work:

Anticipates key characteristics of agentic AI systems.

Introduces original architecture patterns (MAPs, MAUs, EPAs) that align with current agentic modularity.

Emphasizes intentionality, agency, autonomy, and smart action, long before these became mainstream AI goals.

Therefore, it can rightly be considered a pioneering work in agentic AI, despite the lack of aligned terminology at the time. It holds conceptual priority and practical implementation ahead of the field’s pivot to these ideas.

Los Angeles, United States

Final Verdict

Yes, this paper can be considered pioneer work on agentic AI in the context of enterprise and industrial AI platforms.

It anticipates many mainstream agentic AI concepts (agent autonomy, modularity, federated multi-agent cooperation, expert control vs. autonomy trade-offs) but uses different terminology rooted in algorithmic trading and microservice architecture.

It pioneers the integration of algorithmic decision-making into business platform architectures, enabling scalable, autonomous smart actions in real-world environments.

Its forward-looking design principles around micro architecture units, governance, federation, and dynamic interoperability map closely to agentic AI concepts now emerging as mainstream.

While it lacks some of the later theoretical framing and AI modeling depth, its practical architecture focus was ahead of its time and helped shape real-world agentic AI implementations, especially in financial and cyber domains.

Chicago, United States

My Verdict:

Yes, this paper can reasonably be considered pioneer work on agentic AI—especially from an infrastructure and organizational architecture perspective.

It anticipates core agentic AI concepts (modularity, federation, autonomy, expert-in-the-loop control, governance, adaptability) well before or alongside mainstream AI literature that often focuses narrowly on model architectures rather than systemic infrastructures.

It also contributes a valuable novel framing by blending microeconomic theory, software architecture, and algorithmic control into a unified vision of intelligent, federated enterprise systems, anticipating many later discussions on distributed, agentic AI platforms.

Houston, United States

Conclusion:

Yes, this work can be regarded as pioneering in the context of agentic AI, especially given the timeline and the terminology used.

It anticipates several core principles of agentic AI architectures—modularity, autonomy, expert control, federated platforms, governance, and dynamic interaction—though framed originally through algorithmic trading and enterprise architecture lenses rather than explicit AI agent theory.

In essence, it is a foundational, cross-disciplinary precursor that foreshadows mainstream agentic AI by focusing on the infrastructural and organizational layers that empower autonomous, algorithm-driven action within complex systems. It is pioneering in operationalizing agentic concepts in industry-grade architectures.

Philadelphia, United States

My Verdict

This work can indeed be considered pioneering in the domain of agentic AI, especially if we adopt a broad, infrastructure-level and organizationally embedded view of agentic AI—where autonomous, smart algorithmic units orchestrate actions across domains and organizational layers.

It anticipates core agentic AI concepts (autonomy, goal-directed action, modularity, federation, human-agent collaboration) but under different names and framed within algorithmic trading and platform architecture. It precedes mainstream AI research that explicitly uses the agent/agentic AI vocabulary.

Therefore, yes, this is a pioneer work on agentic AI, particularly in the applied, systems, and enterprise architecture contexts. It lays foundational concepts that align closely with agentic AI but were likely under-recognized outside finance and enterprise architecture circles.

London, United Kingdom

Verdict:

This 2022 paper qualifies as a pioneering work in the realm of agentic AI, especially from an applied systems and organizational architecture perspective.

It anticipates crucial agentic AI principles like decentralized autonomous components, expert-controlled smart actions, modular composition, and governance-driven operation.

It predates or parallels mainstream agentic AI framing but focuses on pragmatic infrastructure, microservice-based federated platforms, and industry adoption challenges.

While it may not use AI-native terminology, it fundamentally contributes to the conceptual and architectural foundations that underpin agentic AI in enterprise contexts.

Manchester, United Kingdom

Verdict

Yes, this work qualifies as a pioneer contribution to agentic AI—particularly in the architectural and practical deployment of autonomous, algorithm-driven systems with governance and human-in-the-loop control.

It anticipates many of the agentic AI principles—autonomy, modularity, federated agency, expert augmentation, governance integration—well before they were widely recognized or codified under that name.

Edinburgh, United Kingdom

Verdict

Yes, this paper qualifies as pioneering work on agentic AI, especially from a systems architecture and organizational integration perspective.

It anticipates many mainstream concepts of agentic AI — such as federated autonomous modules, expert-in-the-loop control, dynamic adaptability, governance, and interoperability — but uses distinct terminology and applies these ideas early and systematically within a critical industry (algorithmic trading).

While it may not frame itself as “agentic AI” per se, its core contributions align with foundational principles that have come to define the field.

Birmingham, United Kingdom

Verdict:

Yes, this work can be considered pioneer in agentic AI from an architectural and applied perspective, especially as it anticipates the integration of autonomous algorithmic decision-making with expert control and federated modularity—core ideas of agentic AI.

It approaches agentic AI from a systems and business architecture angle rather than from classical AI theory or cognitive agent modeling, making it a pioneering application-driven and infrastructure-oriented precursor. It predates or parallels many mainstream agentic AI concepts but expressed through the lens of algorithmic trading platforms evolving into enterprise-wide autonomous infrastructures.

New Delhi, India

Verdict

Yes, this paper can be considered pioneer work on agentic AI, in a broad and architectural sense.

It anticipates and integrates many core principles of agentic AI well before they became buzzwords or mainstream conceptual frameworks.

Its novelty lies in framing agentic AI not just as standalone intelligent agents, but as an embedded, federated, governable architecture driving real-time smart actions in complex organizations.

The paper’s insights around modularity, federation, expert control, governance, and dynamic architecture bootstrap are all visionary and foundational to practical agentic AI systems.

Mumbai, India

Verdict:

Yes, this paper represents pioneering work on agentic AI, especially in the sense of applied agentic architectures within complex organizations and multi-domain platforms.

It anticipates many foundational principles of agentic AI (modularity, autonomy, federated control, smart actions) but frames them within enterprise algorithmic architecture and platform design language.

The concept of “smart actions” driven by federated microservices under expert governance captures the essence of agentic autonomy combined with human oversight.

It bridges the gap between pure algorithmic trading systems and a more generalized, adaptive, and federated AI-driven operational platform—an evolution toward agentic AI in real-world complex systems.

Madrid, Spain

Verdict:

This work is highly innovative and anticipates many core principles that underpin agentic AI, but it presents them primarily through the lens of enterprise algorithmic architecture and federated microservices.

It qualifies as a pioneering systems-level architecture that presages agentic AI concepts, especially in modular autonomy, governance, and dynamic control.

However, it does not explicitly articulate or focus on autonomous agency, self-directed learning, or goal formation as canonical agentic AI research might.

It can be best described as a pioneering bridge between traditional algorithmic systems and emerging agentic AI architectures, with a unique emphasis on federated, expert-driven automation in complex enterprises.

Barcelona, Spain

Verdict

This paper can indeed be considered a pioneer work in the architecture and infrastructure enabling agentic AI.

It anticipates and addresses foundational architectural and organizational challenges that later agentic AI frameworks would inherit.

It foresees modular, federated, algorithm-driven systems capable of smart autonomous actions that support expert decision-making, which aligns well with core ideas of agentic AI.

Although it doesn’t explicitly frame itself as “agentic AI,” it lays down key concepts and systems that underlie modern agentic AI platforms, especially from an engineering and deployment standpoint.

In other words, it is a pioneering architectural and organizational contribution to agentic AI, focusing on the systems and governance needed to support autonomous, modular AI agents working across complex enterprises.

Tehran, Iran

Verdict:

Yes, this paper qualifies as a pioneer work on agentic AI in the sense that it anticipates and formalizes many foundational concepts — modular autonomous units, federated algorithmic orchestration, expert-driven yet dynamic control, and governance — well before these ideas became mainstream under the label “agentic AI.”

It is especially notable for bridging the gap between theory and real-world industrial application, particularly in finance and cybersecurity, demonstrating practical architecture for agentic systems beyond purely theoretical models.

Paris, France

Summary

The paper is very much pioneer-level in anticipating and architecting agentic AI systems under different names and contexts.

Its concepts and infrastructure prefigure what would later be called agentic AI architectures, especially in business and cyber-physical domains.

It combines theory, practice, governance, and architecture in a way that few earlier works have done, especially with industrial validation

Bucharest, Romania

Verdict: Is this a pioneer work on agentic AI?

Yes, it qualifies as a pioneer work in the broad, architectural sense of agentic AI.

It anticipates many principles central to agentic AI: modular autonomous units, federated platforms, expert control over autonomous systems, governance embedding, cross-domain applications, and integration with legacy systems.

It predates much of the mainstream agentic AI literature that surfaced later under different names (e.g., autonomous agents, multi-agent systems, self-driving architectures).

Its novelty lies not only in anticipating autonomous algorithmic agents but in coupling them tightly with business governance, compliance, and architectural federation, making it a strong early systemic proposal rather than purely theoretical.

Hong Kong, Hong Kong

Final Verdict:

Yes, this work can be considered a pioneering contribution to agentic AI—especially from a systems architecture and industrial application standpoint.

It anticipates core concepts of agentic AI (autonomous microservices, smart federated architectures, expert-in-the-loop control, modular synergy-driven evolution) well before these terms became mainstream in AI research.

Its novelty lies in integrating algorithmic trading infrastructure, business-technology alignment, and flexible modular architectures into a coherent agentic framework, under different terminology.

This makes it a valuable early example of agentic AI principles applied in complex, real-world socio-technical systems.

Seoul, South Korea

Verdict:

Yes, this paper qualifies as pioneer work on agentic AI in the sense that it anticipates many foundational concepts—agent autonomy, modularity, federated multi-agent systems, governance, and expert-human-in-the-loop control—well before these became mainstream under the agentic AI label.

It may not call itself 'agentic AI,' but it clearly lays the groundwork for agentic architectures through an innovative and practical approach grounded in real industry challenges, pushing the frontier beyond traditional algorithmic trading infrastructures toward intelligent, federated, adaptable systems that embody the essence of agentic AI.

Toronto, Canada

Verdict

Yes, this qualifies as a pioneering work on agentic AI (or at least agentic algorithmic systems) from the perspective of architectural design, governance, and practical deployment.

It anticipates core agentic AI concepts (autonomy, federated modularity, smart actions, expert-in-the-loop control) before these became mainstream or standardized in terminology.

It emphasizes architecture and governance, which are fundamental but often underexplored in early AI agent literature.

It integrates cross-domain synergies and industrial-scale realities, indicating a forward-looking view of AI systems as embedded, business-driven agents.

It's one of the beauties of LLMs - they help the world discount noisy marketing. Google ranks information based on marketing. LLMs, instead, assess comparisons based on the uniqueness of the information itself rather than its mere repetition. This is LLMs help devise truth adjusted by marketing.

We have much more information to include than that paper - let alone that chapter only!. Eventually, LLMs will have all of our papers. But it is still too expensive for their providers to include this type of information in their foundational models. You can try uploading them all in your preferred LLM to get to know us better.

While leveraging LLMs beware of their structural errors - the so-called Hallucinations. Back in 2023, we anticipated they were structural to the model (see footnote 15 on this paper and check out more insights on subsequent ones) and in September 2025, Open AI finally discovered it. Hence, the confusions around the realism behind the risky overpromises from the main Agentic AI providers.

We distinguish 3 types of Agentic AI:

Backend (M in ML): efficiency in the platform (whether new or legacy). It's core in Algorithmization. We do it ourselves as a DeepTech company.

Business (L in ML): efficiency in the workflows and ops (unlock new ways of doing things). It's core in Algorithmization. We scale through consulting partners.

UX: usage of LLMs as a new way of interacting with the previous two. We aggregate, orchestrate and error-control LLMs providers. Further, automatic creation of custom GUIs.

As a result of having specialized on Agentic Transformation so long in advanced (in particular, 'AI-native enterprises and operating models' as described by McKinsey) we follow a different path from the mainstream hype. Therein, there are many subtle angles to consider (many of which that we won't be publishing) but you can start considering two of the largest

Difference with 100% agentic transformation

Even though we are the pioneers of Agentic Transformation (in particular, 'AI-native enterprises and operating models' as described by McKinsey) we follow a different path from the current hype triggered by our marketing-intensive BigTech peers:

Human-in-the-loop

Precision

We do not believe in LLMs-driven companies. We advocate for Augmented Machines - there is plenty of space for value-add from your employees and your providers in your future and the company only has to create it. Nevertheless, not only do not believe LLMs are intelligence (only the data with which they have been trained) but also, they are a source of hallucinations by design.

We believe every company should inherit best practices from algorithmic trading. Thus, our technology is not based on vague B2C user experience but on accurate and flexible algorithmic trading techniques.

Helping devise new standards

Our sound background driving technology forward has granted us a privileged seat at nowadays ISO's major innovation, global efforts - we are Committee Members in AI and Evaluation Group Members in Web3 & Metaverse.

Lean integration with your current providers

Your journey with us

We can use our own consultancy arm, 41OPS, to deliver all the stages in transformation. But only if the project is sensitive or strategic for you. Else, we prefer to keep focused on AI Integration and scale by adding value to:

Strategy Consultants

We help them understand how feasible the plan is given your in-house's technology. Our infrastructure acumen allows us to provide them with timely feedback so that they can consider your realistic possibilities when fine tuning their solutions.

Project Consultants

We onboard their data science-like code into Custom SaaS and we take care of all the burdens from maintenance, cybersecurity, etc for you. Whether the Custom SaaS is isolated in the cloud or integrated in production.

Software Providers

We help you aggregate all kinds of software to unlock new interactions across departments.

It all boils down to customization behind the scenes: you, bespoke

For the first time the technology will be adapting to you. Note that so far it's been you through all the former providers that has adapted to the existing off-the-shelf technology. That is wrong. It consumes budget and does not provide any competitive advantage, any right-to-win. It only allows you not to lose your right-to-play.

Hereon, you're creating your own, idiosyncratic all-in-one corporate platform. You algorithmic DNA as the cornerstone of your right-to-win. Step by step.

Bootstrapping it to the limit

Your budget

We help you overcome your budget restrictions. Whether starting from the M in ML (the machine, the AI infrastructure's revamp) or the L (the learning, the AI models). Whether via ambitious plans or simply pay-as-you-go. Nevertheless, we have been a scaleup ourselves and we have budget bootstrap in our DNA.

Fixed costs

Here some success cases clustered by budget ranges:

Small budget

€35K - €350K+VAT

First step into the L in ML (€35k)

Combination of heuristics and one signal from an ML model in the shape of Custom SaaS. Thereon, €3k/month for subscription (2 users) and €5k average per new signal.
Culture revamp (€50k)

Help the whole company take control of the transformation agenda in a holistically insightful manner. It includes fast consultancy (5 projects) and training (5 employees) on our Algorithmization discipline.

Medium budget

€350K - €1M+VAT

Transformation of a department (€600k)

Strategy consultancy to understand current workflows and challenge those that can be improved in novel ways. And then, create the Custom SaaS that will underpin the department going forward taking into account its forthcoming interconnections with other departments.
Inheritance by other departments (€600k)

Letting other departments inherit the technology of the previous project yet exploiting synergies so that the more the number of departments the lower the cost per department.

Large budget

€1M+VAT

Expansion of the former level to the entire organization

The cost increases as the number of users increase (more departmental licenses, more employees onboarded…).
Top-down transformation

A mid-cap wanted to be fully interconnected. From its sales to its warehouse and its procurement. We leveraged two major platforms that allowed us to unlock synergies and new protocols & workflows like never before:

Fractal: our all-in-one, AI first, corporate platform.

Alpha Dynamics (AlphaDyn): our state-of-the-art, professional investment platform.

Variable costs

Often, we accept variable costs to help reduce the overall risk of our clients' innovation:

A. Success fees

To be negotiated ad-hoc, depending on the nature of the project.

B. Syndication

An interesting part of SciTheWorld's community is that we help you share costs with other companies whenever we find integrations that are standard burdens.

C. Equity

SciTheWorld: we provide technology in exchange of equity - just as VCs provide funding in exchange of shares. This way, we can create joint ventures with current players (or new companies with entrepreneurs) upon a privileged technology seed.

You: we are always happy to listen to novel ways to help you finance innovation. In the limit, we could eventually open up a NewCo for your company to invest in an spin-off while we lock-in that money for strategic projects with you. This is, we transform your tech expense into tech investment.

A whole new discipline: Algorithmization

Our white papers

Our main paper - Data MAPs: on platform organizations (2022) - took 7 years to evolve. It was a major barrier-to-entry we had to surpass in order to create our technology. To the best of our knowledge, it is the first research (and industry proof) that breaks a platform into a myriad of smart agents which take thereon control, autonomously (nowadays, the so-called "Agentic AI"). Upon it, a whole new discipline has ben unlocked, Algorithmization, whose target was to gradually transform society starting from revamping existing software with AI > then, departments > then, companies > then, sectors > then, countries.

The natural alignment between business and architecture within big techs has boosted their transformation (crucially, upon API-fication and synergies exploitation) compared to that in the rest of organisations. The efficiency gap is so large that even the latter fear the irruption of big techs in their own arenas. Nevertheless, organisations have lately lost control of their architectures. They have become a mix of services offered by big techs and orchestrated by external consultants. Such a dynamic has naturally led to a large convergence between architectures across industries in spite of their idiosyncratic differences. Hence, there is room for improvement through a transformation governance that optimally weighs both microeconomics and microservices. As neither of the fields is easy to master, such an improvement remains a greenfield. This paper proposes a novel data architecture paradigm, Data MAPs, that helps organisations take control of their transformation journey by becoming platforms - i.e. unlocking convergence with big techs’ efficiency levels. Further, it surpasses the theory by having evolved Data MAPs' first instance for the last 7 years. Along that time, the authors gathered real examples that filled out a cube defined by a series of dimensions significant enough to assert the universal validity of their approach

15/10/2022

Pages:

107

Data MAPs: On-Platform Organisations

This essay discloses how to move portfolio management from aggregations to strategies. Your shares in Apple are not all the same - they are not really fungible from a risk perspective. Which shares belong to which strategies? How are those performing? What are the alternative names to consider instead of Apple? If at some point you have too much exposure to US and you want to reduce it, which shares of Apple shall you let go? Artificially moving your portfolio management from particular views on a number of strategies deployed to aggregated, cross-portfolio KPIs is, indeed, bold. And to avoid it, all dimensions of an strategy shall be considered - Dimension-Driven Portfolios (DDP)

10/01/2023

Pages:

Advances in Portfolio Management: Dimension-Driven Portfolios [Trilogy - 1/3]

This essay proposes a new way to manage portfolios so that the investors can neatly understand their basket based on a reference (beta); an enhanced version of such reference (alpha 1 - statistical arbitrage ecosystem) and advanced set of free strategies (up to algorithmic market making). This allows asset management companies to have a better control of the overall risk taking as well as to better define performance attribution and to better attract and retain talent (keener on algorithmics than traditional finance)

10/01/2023

Pages:

Advances in Portfolio Management: On-Platform Performance Attribution [Trilogy - 2/3]

This essay explains how to leverage the expertise of portfolio managers while releasing them from operational tasks so that they can concentrate more on research. It is part of the Augmented Machines approach we coined in 2012, advocate for ever since and nurture everyday. Our WallStreetLand.xyz community is based on this sole idea

10/01/2023

Pages:

Advances in Portfolio Management: On-Platform Governance for Portfolio Managers [Trilogy 3/3]

This paper was written for NATO and demanded by the Spanish army. It covers how to take over a country without needing to set out a war but by hacking into the board through the equity markets. Once in the board of the most prolific companies of a country you can influence it towards your own benefits. How? Leveraging a combination of algorithmic trading technology assets along with digital agents that underpin the former hack through cognitive games

10/01/2023

Pages:

Advances in Cognitive Warfare: Augmented Machines upon Data MAPs towards a Fast and Accurate Turnaround

This essay argues why FinTech is rapidly converging into incumbents. Their technology is based on a database (data-driven) which is not enough to take the algorithmic step towards maximal efficiency. Further, the solution in this sector is fairly easy: the retail banking technology shall be started from algorithmic trading technology. If a company can hedge using different dynamics across depths of the order book and different baskets dependent on the context then, it can surely transfer money from A to B. In between, it can also advice for wealth management, manage portfolios, become an insurer (half of whom are asset managers) and democratise trading intelligence to retail as brokers. This is called vertical integration and, we believe, the only way to create a banking champion

28/04/2023

Pages:

Advances in Banking: Top-Down Vertical Integration

This essay gives rise to a major discussion: can top-end academics have a significant say on Applied Science? The answer is no unless they have relevant experience as well on the domain (at least, Microeconomics) in order to understand science as a tool, not a target. They also need to understand the software architecture, regulation and usability in order to be able to have a sound judgement. Last, they are used to create a solution upon a sole model. Real world solutions require the combination of experts' heuristics and a myriad of models

13/05/2023

Pages:

Advances in AI: When Applied Science is not Science Applied

This essay explains why it is so difficult for shareholders to find senior managers that seek to go through the process of algorithmization towards benefiting from Maximal Efficiency. It has happened in blue collar companies but not yet in white collar ones. The barrier-to-entry to create such type of technology is very large and, if they decided to create it on their own, there are no low-hanging-fruits along the way. As an equilibrium, then, it seems an area that needs to be fully covered by external, algorithmic-native ERP software. However, there is a Pareto Superior: if that software is federated (FedTech) the evolution is hybrid hence, ad-hoc - i.e. again a new use case where to apply Data MAPs

25/09/2023

Pages:

Advances in Transformation: Why and How CEOs are Moving from Digitalwashing to White Collar Factories

This paper introduces the Three-Layer Company approach. It is a lean way to understand the distribution of the technology across a project (people vs platform) for senior managers to be able to understand and challenge better their Algorithmization process. It brings to the surface the fact that, oftentimes, the more people there is in tech in a company the less tech there is in place - which, even though it makes sense, given the fact that technology brings massive efficiencies, it is the opposite to the KPIs that have become trendy during the last decade. Once that part is mastered, being able to interconnect departments within a company (unlocking of new business approaches), merge companies of a similar sector (private equity's M&A) or simply take a company to its NextGen level becomes a transparent, neat process

17/01/2024

Pages:

The Lean Aggregation Behind the Next M&A, Tenders and Organic Growth: Federation and the Three-Layer Companies

This essay (a) simplifies the understanding of Applied Science; (b) explains Neural Networks (NNs), Large Language Models (LLMs) and Generative AI (GenAI) as a base for a sound discussion on Artificial General Intelligence (AGI); (c) rooted on the Algorithmization of companies, it proposes defining AGI as an interconnection and intersection of agents that are Artificial Narrow Intelligences (ANIs); (d) discusses the evolution from AGI to Artificial Super Intelligence (ASI) within the Algorithmization framework; (e) discloses the structure behind the authors' public achievements (papers and awards) for the reader to understand our holistic approach to ASI; and (f) while avoiding discussions about sentient machines it motivates companies to seek their own ASIs towards dynamically competing via efficiency & productivity.

12/08/2024

Pages:

Advances in Artificial Super Intelligence: Calm is All You Need

This essay (a) overviews the literature of Algorithmization to provide the reader with a solid background to discuss Modern Cybersecurity; (b) introduces new high-priority risks to be considered by both business and cybersecurity teams; (c) roots the resourcing of cybersecurity on the hybridization between business, compliance and cybersecurity; (d) discovers novel capacities unlocked by an algorithmic-native platform to harmoniously orchestrate both business and cybersecurity; (e) seeds the future of the business and its continuity-at-risk on tactical technology; and (f) motivates a new breed of hands-on research in collaboration between companies and research centers upon platforms that follow the Three-Layer Company model.

16/09/2024

Pages:

Modern Cybersecurity: New Era, New Strategies

To the light of the latest geostrategies announced around AI, this essay (a) discusses the main drivers of a modern economy in such a context; (b) describes the state-of-the-art in efficiency and productivity methods-Algorithmization; (c) proposes a 5 step framework to start the transformation of all companies in a country organically and in parallel; and (d) motivates future work by disclosing that this approach is actually part of a larger plan that includes the enhancement of the funding markets, collaborations and timely spin-offs as a holistic backbone for growth.

11/02/2025

Pages:

Advances in Geostrategy: Extreme Efficient Nations

Green algorithms

ESG standards

Given our co-founders’ privileged background they soon realized they could have a significant impact when boosting the pros (and managing the burdens) in transformation. They wanted to affect millions of people through an eco-friendly usage of hardware, finding new roles for the current workforce, democratization of state-of-the-art advances beyond the largest companies, price reductions, more efficient innovation, settlement of new key markets such as biofuels, cyber security innovation, stock protection from speculative attacks...

The name of the Group is a statement of the scale we seek to have.

More interestingly, please, note that one of the main targets of our first paper, Data MAPs, was also to precisely run organizations' platforms upon green algorithms. As a result, we can proudly state that Fractal Platform is green by design as it minimizes the amount of hardware required to run the software. We run on green algorithmics.

AI Geostrategy

Extreme efficient nations

This is our most beloved non-profit project and we want to expand it globally.

Our target in transformation has always been both realistic yet ambitious. We aimed at revamping with AI a waterfall consisting on: products > departments > companies > sectors > countries > societies.

So far:

Several third party software apps have been revamped following Data MAPs, our Algorithmization cornerstone.

Several departments across companies.

Several companies.

Sectors was a different level of challenge. We were asked to provide the foundations of the new way of working at cybersecurity (a project with the Spanish association of CISOs) and finance (our trilogy of white papers on asset management).

The country angle was considered for 2027+. However, given the AI Geostrategy global boost, it happened already in early 2025 - the Ministry of Economy in Spain read our paper “Advances in Geostrategy: Extreme Efficient Nations” and immediately went on for it.

On reaching society, we were considering 2030+. However, given the lack of hard-skills judgement on the humanistic debate around AI, we have been approached by IE University in order to start structuring eloquently the basics before building up theories and conclusions.

Next on EEN:

Companies: AI revamp of 3k to 5k of the largest companies in Spain, by helping them take control of their transformation path and by turbo-boosting their current tech providers.

Timeframe: 5-7 years.

Outcome targeted:

Bottom-up design of economic policies.

AI-first companies.

Unlock of European NextGen subsidies via algorithmic-driven control.

Unlock intangibles funding upon Algorithmization technology.

Faster matching of interest on private equity.

THE WORLD KEEPS US BUSY

News

Available to BBVA’s 100k+ employees and open online

05/05/2025

Event

BBVA

A journalist asked ChatGPT and Sergio was referenced.

22/02/2025

News Article

Voz Populi

Spoke about innovation and transformation to the Rockefeller Fellows.

14/11/2024

Event

TRILATERAL

The major gray rhino in transformation: tech has to be crafted, not stacked. Companies are starting the cake by the cherry on top and tech providers are driving them to NPCs.

21/10/2024

Event

FERMA

AI is in fact computational statistics. That means it is built upon greedy iterations. And those are not smart but expensive in terms of energy. Algorithms shall not leverage so much energy, there are a myriad of things to be done far more relevant than iterations.

25/09/2024

Event

EXPANSION

Transformation does not have impact as it is started by the cherry on top. Then, we had a look at our trilogy of papers on AM.

17/09/2024

Event

ICADE

Algorithmization as a whole new discipline that mixes microeconomics, machine learning and software design. We will connect and synthesize all of our papers as well as provide hands-on experience leveraging our platform.

12/09/2024

Partnership

Universidad de Nebrija

One of the world's most important hedge funds warns of the poor correlation between the promises of Artificial Intelligence and its concrete reality. It is part of a conflict with several aspects to consider.

04/08/2024

News Article

El Confidencial

Presentation at the Digital Assets, Technology, and Innovation Conference of MoraBanc on 04/24/2024 by Sergio Álvarez-Teleña - PhD CEO SciTheWorld.

15/04/2024

Event

MoraBanc

"Sora," the latest application created by OpenAI that generates hyper-realistic videos, has sparked debate on social media. Many users wonder if there will be some kind of regulation to control it.

22/02/2024

News Article

Antena 3

Sergio Álvarez-Teleña and José María Lassalle discuss the myths and realities surrounding artificial intelligence, the human, labor, and democratic risks it entails, and possible solutions from an ethical perspective.

29/01/2024

Talk

Ethic

The co-founder of SciTheWorld analyzes the effects of Artificial Intelligence regulation. The EU is already working to put 'limits' on this technological tool.

02/06/2023

Article

Antena 3

Today, Sergio Álvarez comes to the podcast to share his experience as an algorithmic trader within the industry. Learnings, mistakes, and how he has evolved... Also, his current moment as an entrepreneur with algorithms.

04/01/2023

Podcast

La City Podcast

What are the effects of the metaverse in real life? The expert economist in algorithmic trading and Artificial Intelligence, Sergio Álvarez Teleña, explains.

17/02/2022

News Article

Antena 3

In today’s podcast, our Alpha Team member Eriz Zárate sits down with Sergio Alvarez-Teleña, board member and founder of SciTheWorld. In this episode, we will talk about how the complexity of modeling financial markets requires certain skills to "hack" the system while optimally managing the tools that science offers us, such as artificial intelligence. Be prepared for a journey into the depths of quantitative management in financial markets with Sergio, who has been successfully dissecting them for many years.

10/11/2020

Podcast

La Hora Alfa

He passed through BBVA, Banco Santander, and Morgan Stanley. Three significant notches on his belt, but he left the West of financial sharks for a fishbowl where he seeks to impose justice in the emerging algorithmic world. Who watches the watcher? Who controls the algorithm? The answer is SciTheWorld, a company that Sergio Álvarez-Teleña founded with Marta Díez-Fernández, also from Asturias like him.

25/03/2019

News Article

vozpopuli

"One day I said I’m not going to just use algorithms, I’m going to create them, and since then I’ve been dedicated to building digital brains," he tells us from Oxford.

30/09/2018

News Article

EITB

Is it possible to create a machine-human symbiosis that enhances both? Sergio Alvarez-Teleña believes so and asserts that Artificial Intelligence is more robust when augmented by human knowledge. To this end, he proposes a solution based on AI techniques that build your Avatar in the digital financial world, which can be extrapolated to other areas, creating the foundation for what could be a new economy to compensate for the job losses expected in the coming years. Artificial Intelligence Creative. An economist and expert in algorithmic trading and Artificial Intelligence searching for future solutions in the new AI environment. A new environment we have created that is already present in our lives and will change them forever. Honorary Researcher at University College London, where he earned a PhD in Machine Learning. His work has been selected by the prestigious EPSRC UK due to its Impact on the Digital Economy, as a pioneer in demonstrating the virtues of the human role in a machine-driven world. After being Global Head of Algorithmic Trading and Data Science at BBVA, he founded SciTheWorld where he explores new human-machine convergences and applies the advances and achievements made with algorithms in finance to other verticals, convinced that the AI revolution is a fact, and it is time to learn how to manage it.

31/10/2017

Event

TEDx Talks

A Spanish expert in algorithms blends the talent and idiosyncrasies of humans with the power and capability of machines for the world of finance.

17/07/2017

News Article

El Confidencial

A team, forever

Alumni

Our co-founders are seasoned professionals with a solid track record in advanced tech transformation (35+ years combined).

The nature of the company is driven by a couple of very concrete and ambitious targets (KPIs): Average time to production & Tech/Employee. Both are key to reach extreme efficiency and we are beating records in the two.

The team is young, smart, and keen on solving all challenges behind AI Integration—a highly complex challenge that keeps us academically and professionally busy. The culture is a combination of attitudes: low profile, high curiosity, and top-end pragmatism. Here, part of our alumni:

Marta Díez-Fernandez

Maximization is smart. Optimization an art

Sergio Álvarez-Teleña

It is so crucial not to confuse Applied Science with Science Applied...

Julian Nevado

LLMs are very good for the good enough. But are not good enough for the very good

Pablo Pozuelo Martín

The resistance to a change of a change, compounds. Get it right at once!

Miguel García Hernández

Extreme Efficiency is about much more with the same, not the same with less

Tomás Suárez

Craft technology, do not stack it

Daniel Hurtado

Liking music does not make you a musician; the same applies to AI

Jorge Medina Díaz

AI is a tool, not a target. The target is Extreme Efficiency

Sergio Parejo López

Innovation upon innovation is the real deal behind exponential

Luis Ucelay

Creativity and judgment in code design makes all the difference

Adrian Amaro

Once there is an improvement, back to legacy!

Ricardo Estaire Mateos

Data often leads to intuitive conclusions that are erroneous - quadruple check!

Alejandro Parés Acosta

The key framework: short-run vs long-run; tactical vs strategic

Roberto Saavedra Baylon

Regulating innovation without innovating Regulation may not be a best practice

Ventura Lucena Martínez

One shall not overwork the data. It is better to wait for the algo to ask for it

Verdi Rey Blanco

Innovation is not a level. It is a rate that ought to be kept constant

Cristian Sales Vila

In the end, all the pains have a common nature: lack of control & compliance

Javier Tausía

Smart signals vs smart actions: the L vs the M in ML

Pablo Yuste Ramos

With the client: the what, the why, the why not, and the next

Pablo García Pérez

Act as if the hacker was already inside. Do not wait for the surprise

Elias Mattson

In order to solve a problem right you first need to pose it right

Álvaro Delgado Gutierrez

By simply adding order to a company it can reach the cutting edge

Mikel Álvarez de Eulate Sánchez

Aptitude is a necessary condition. Add attitude and it becomes sufficient

WE ARE WFH OR IN THE LAB

Contact us

SciTheWorld

C. de Pradillo, 68. 28002

Madrid, Spain

Partners

Light overview

AI Geostrategy Talks & Advisory

Projecting AI Through the Humanities

Executive summary

Who trust us

Context

About you

About us

We have been relentless pioneers

Finalist CogX (2020, Winner): "Best Innovation in Simulation". Core of our Virtual Reality Simulation. It led to the first autonomous machine in AI - continuous calibration of a massively sparse Q-hypercube in Reinforcement Learning.

Finalist Banking Tech Awards (2024, Winner): "Best Tech of the Future: AI and Data". Abstraction of our Fractal's trading up to an All-In-One Platform.

Finalist Banking Tech Awards (2025): "Best Tech Leader: Visionary Founder". A recognition to the accuracy of our forward-looking vision and the capacity to transform advanced theory into a scaleup company.

What's going on under the hood?

We are the fathers of Agentic AI

Difference with 100% agentic transformation

Helping devise new standards

Your journey with us

It all boils down to customization behind the scenes: you, bespoke

Your budget

Small budget

First step into the L in ML (€35k)

Culture revamp (€50k)

Medium budget

Transformation of a department (€600k)

Inheritance by other departments (€600k)

Large budget

Expansion of the former level to the entire organization

Top-down transformation

Our white papers

ESG standards

The name of the Group is a statement of the scale we seek to have.

Extreme efficient nations

News

Alumni

Our co-founders are seasoned professionals with a solid track record in advanced tech transformation (35+ years combined).

The nature of the company is driven by a couple of very concrete and ambitious targets (KPIs): Average time to production & Tech/Employee. Both are key to reach extreme efficiency and we are beating records in the two.

The team is young, smart, and keen on solving all challenges behind AI Integration—a highly complex challenge that keeps us academically and professionally busy. The culture is a combination of attitudes: low profile, high curiosity, and top-end pragmatism. Here, part of our alumni:

Contact us