Buy vs Build Software – ‘To be or not to be that is the question’!

The Buy v Build software dilemma for AI is a very pressing challenge for our financial clients. Skills and software are needed to build an inhouse solution, whereas purchasing the solution can be faster, though it might be more difficult to customise the solution to fully meet business requirements and there might be concerns about data security and privacy.

We asked our Our Head of Sales Frédéric Moioli, his thoughts on the Buy vs Build Software debate.

1) Lingua Custodia’s solutions are a ‘buy’ option, so how can we ensure our solutions match our clients’ needs?

Lingua Custodia’s solutions are uniquely positioned to match client needs due to several key factors:

Expertise and Innovation

Lingua Custodia leverages extensive expertise in Natural Language Processing (NLP) and AI ensuring solutions meet specific client challenges. Our dedicated Research & Development department, The LAB, drives innovation by developing new applications and keeping our products at the forefront of technology.

End-to-End Control

We maintain control over the entire value chain, from creating custom Large Language Models (LLMs) to rigorous data management and training. Our secure Retrieval-Augmented Generation (RAG) tool addresses crucial data security concerns.

Verto Platform : the one stop shop platform

Our advanced platform, Verto, serves over 10,000 users, integrating AI-powered tools for translation, transcription, data extraction, and efficient document analysis. By combining expertise, continuous innovation, comprehensive control of AI development, and a powerful platform, Lingua Custodia effectively aligns its solutions with the evolving needs of financial sector clients

2) How does Lingua Custodia help its clients considering a ‘build’ option?


At Lingua Custodia, we’re not a consulting house ; instead, we leverage our extensive expertise in AI technologies to support clients in the financial sector. Our LAB partners with the innovation labs of our clients on AI research projects.

Since our founding in 2011 by finance professionals, we have developed a deep understanding of our clients’ pain points. This allows us to customize AI models to meet their unique requirements effectively. The LAB’s innovations include the development of our cutting-edge Document Analyser, a generative AI tool designed for efficiency without requiring massive investments.

By providing secure, innovative solutions, we empower clients to enhance their operational efficiency while ensuring data security and compliance. This approach enables us to deliver high-quality, domain-focused solutions that align with the needs of the financial industry.

3) What is Lingua Custodia’s competitive advantage?


Lingua Custodia’s competitive advantage stems from several key factors:


At Lingua Custodia, we offer unparalleled security for our clients. Unlike many competitors, we don’t rely on public cloud services. Instead, our solutions are hosted on physical servers located in Europe, ensuring the highest level of data protection and compliance with strict financial industry regulations.


Our team consists of dedicated and versatile professionals with deep expertise in both finance and AI technologies. This unique combination allows us to understand the intricacies of financial operations and develop tailored AI solutions that address specific industry challenges. We are ultra-specialized in finance, having been founded by finance professionals in 2011. This means we don’t just understand AI; we intimately know how the financial sector works. Our deep industry knowledge allows us to create solutions that seamlessly integrate into existing financial workflows and address real-world pain points.


Our LAB, our dedicated Research & Development department, keeps us at the forefront of AI innovation. It continuously develops cutting-edge applications tailored to the financial industry’s needs.


By combining our secure infrastructure, specialized team, deep financial expertise, and continuous innovation through our LAB, we offer a unique value proposition that addresses the specific needs of financial institutions in today’s rapidly evolving technological landscape.

How to understand the core concepts of AI, LLMs and RAG!

If you find some of the different terminology used for Large Language Models (LLMs) and AI confusing, you are not alone!

This is the first in a series of articles about AI, LLMs and Retrieval Augmented Generation (RAG) where we aim to explain clearly and succinctly, some of the key terminology you might be hearing about. We hope you find these posts helpful!


What are foundation models?


A foundation model is an AI model, trained on huge amounts of data (documents, audio, images, text….). It is trained to ‘generate’ the next word as it ‘learns’ the language. It should then be specialised and fine-tuned for a wide variety of applications and tasks, which then means it is no longer a foundation model!


What are LLMs?


A LLM is an umbrella term used for all foundation and specialised models.

For example:

In the case of Llama, the foundation model is not usable directly but serves as the foundation for all the subsequent specialised models. Llama instruct is a question and answering model and code Llama is a coding assistant.

All three models are LLMs.

What are the benefits and challenges of a foundation model?

In terms of benefits: 


Flexibility and adaptability

Foundation models are flexible and adaptable as they can be be fine-tuned for a wide range of tasks, saving time and resources compared to building new models from scratch for each specific task.

Cost efficient

While foundation models are costly, once you have them, you can adapt them as many times as you want on new tasks.

Accessibility

Open source foundation models are accessible as smaller companies with less access to computational resources can leverage these models to create innovative AI applications. (Note that there are many closed models which are not accessible!)

(Note – Open source foundation models – almost anyone can use, access the source code and customise the foundation model which in theory, improves accessibility, transparency etc.  Meta’s Llama 2 is an open source foundation model.  Chatgpt is not open source. 

As for the challenges: 

Bias

Foundation models are trained on large and diverse data sets which may contain biases present in the data, and which will be mirrored in the model’s outputs.

Security and privacy

The huge amounts of data needed to train a foundation model naturally raises security and privacy concerns.  The data should be secure and handled responsibility.

Lack of transparency

Foundation models can be a ‘black box’ .  The issue with data has already been highlighted.  In addition, it is important to understand how the foundation model generates its outputs to identify any potential errors or bias.  This is a hot topic with ongoing empirical studies.

Lingua Custodia wins the Large AI Grand Challenge Award organised by the European Commission!

AI award

Lingua Custodia wins the Large AI Grand Challenge

The French Fintech company Lingua Custodia, a specialist in Natural Language Processing (NLP) applied to Finance since 2011, was delighted to receive an award in Brussels yesterday. This award, which was presented by EU Commissioner Thierry Breton, is designed to reward innovative start-ups and SMEs for devising ambitious strategies and making commitments to develop large-scale AI foundation models that will provide a competitive edge for Europe.

Together with 3 other technology SMEs, Lingua Custodia will share a prize of a total of €1 million and access to two of Europe’s world-leading supercomputers, LUMI and LEONARDO for 8 million hours. This challenge was highly competitive and received 94 proposals.

Lingua Custodia’s AI foundation models

Lingua Custodia’s winning proposal focused on developing a series of AI foundation models with 3 major objectives, using the company’s existing skills and known expertise in the AI arena:

  • Build very cost effective, fast and efficient models to run on smaller servers and democratize the technology while reducing energy consumption
  • Ensure the models can handle multilingual queries and make them available to non-English speakers
  • Tune the models for the retrieval of information (RAG) to enhance the usage of generative AI for multilingual knowledge management.
Lingua Custodia’s focus on cost and energy efficient AI foundation models


Olivier Debeugny, CEO of Lingua Custodia, declared to Thierry Breton: “Lingua Custodia is an AI company, that has raised a modest amount of capital since its launch. This has been a catalyst for our creativity and resourcefulness and we therefore have the skills to optimize everything we develop. This is why we have been working on the design of multilingual, extremely cost and energy efficient models to be applied to an AI use case with a high Return on Investment.”

A propos de Lingua Custodia

Lingua Custodia is a Fintech company leader in Natural Language Processing (NLP) for Finance. It was created in 2011 by finance professionals to initially offer specialised machine translation.

Leveraging its state-of-the-art NLP expertise, the company now offers a growing range of applications: Speech-to-Text automation, Linguistic data extraction from unstructured documents, etc.. and achieves superior quality thanks to highly domain-focused machine learning algorithms.

Its cutting-edge technology has been regularly rewarded and recognised by both the industry and clients: Investment houses, global investment banks, private banks, financial divisions within major corporations and service providers for financial institutions.