Our Technological Positioning: Co-creating Securely
Beyond the Black Box: Exploring the critical challenges and integration hurdles of today's generative AI technologies
Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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The Limitations of Current Generative AIs
The products currently available on the market, such as ChatGPT, utilize a closed technology approach. This approach limits their market reach because the models and their training data are kept confidential and can only be accessed through APIs.
This situation presents several significant challenges for the market:
Security and Privacy of Client Information: Companies looking to adopt this disruptive technology must feed a "black-box" model with their proprietary data and sensitive customer information. This poses major security concerns, as closed models cannot be audited nor can their outputs be assured. Furthermore, regulations concerning the transfer of personal data to legal entities outside Brazil could create compliance issues.
Integration Difficulties with Proprietary Models: By exposing only the outputs rather than the entire model, it becomes difficult to integrate these systems with other client applications (such as databases, CRM systems, and multimedia). Most companies are transitioning to modular architectures, and the opacity of "black-box" models hampers the effective adoption of this technology.
Secretive Training Data: The data used to train "black-box" models are kept confidential, leading to reliance on a system with unknown sources that might produce unpredictable results. Efforts to filter and manage this risk provide only a weak assurance that the model will not generate inappropriate content based on its training data. As of April 2023, concerns over these issues led to the ban of ChatGPT in Italy.
Inability to Scale Proprietary Data Usage: A significant limitation of current Generative AI is its training with proprietary and private information. If these powerful models could be trained securely using companies' own information, knowledge, and data, a significant increase in productivity could be achieved. However, this is not feasible with the current technology.
Infusion AI
Infusion AI was created to develop and train state-of-the-art models in an integrative way, strategically leveraging the currently available models and customized models created for specific needs. Our vision is to become the market leader in Generative AI in Brazil.
Initially, we will focus on the Brazilian market to train models with our culture and language, enabling the creation of efficient automated agents aligned with the country's reality. Once we build a community of talents, we can expand our offerings to Latin America and the world
Our Technological Positioning: Co-creating Securely
Our primary differentiators, which capitalize on the oversights of major players, include:
Hybrid Model Construction: We develop proprietary/customized models that work in conjunction with existing "black-box" models, as well as open-source or licensed models.
Architecture and Parameter Release Post-training: We provide clients with the architecture and parameters of models after training, which allows for (i) more efficient integration of existing workflows and applications, and (ii) the ability to inject proprietary content into various parts of the customized model. This approach offers a significant advantage by preventing the current practice of content "serialization" through the APIs of "black-box" models.
Enhanced Focus on Data Sources and Quality Controls: Our attention extends to both the customized (core) models and the adjacent (open/licensed or black-box) models. We plan to incrementally enrich our models with specialized data, facilitating access to premium databases and information sources (e.g., Bloomberg GPT).
Real-time Differentiated Access Levels: We establish different access levels for various employees in real-time, adhering to the company’s intellectual property policy.
Guaranteed Privacy: Our test models operate on private clouds, and we deploy customized models either on private clouds or on infrastructures managed by us, depending on client requirements.
High Level of Security: Beyond the security offered by private clouds, we are committed to reducing the size of hybrid models. We are developing models capable of operating directly on dedicated devices (e.g., Raspberry Pi), which eliminates workflows with potential security vulnerabilities.
We help your business bring AI to your customers with our customizable AI products and proprietary tools. We also offer strategic advice and use case identification.
