Scaling AI
We transform AI prototypes into robust and reliable enterprise agentic applications designed to increase productivity and usability, revolutionising the way business applications are used.
THE PROBLEM OF COMPANIES TODAY
The evolution of enterprise software applications means that Artificial Intelligence is no longer just another new feature, but is completely revolutionising the user experience by introducing an “agent layer” that acts as an interface and orchestrator between users, data and applications. The transition from a Software as a Service (SaaS) model to an Agent as a Service (AaaS) architecture represents a major strategic opportunity for companies that need greater flexibility and responsiveness.
However, the path is difficult. Prototypes based on Artificial Intelligence technologies often demonstrate promising effectiveness in controlled environments. The real challenge arises when attempting to scale these solutions for real-world applications:
- The increase in the quantity and variety of information to be managed puts a strain not only on the intrinsic performance of LLMs, which can exhibit increasing latency or inaccuracy, but also on the efficiency of upstream data ingestion and processing architectures.
- The non-deterministic nature of LLMs introduces an inherent error that, while acceptable in a PoC, becomes critical when it impacts core processes and business decisions.
- As the amount of data processed increases, so does the risk of inadvertent exposure of sensitive data or malicious attacks, introducing new security and privacy concerns.
- The scalability of AI models leads to an exponential increase in computational costs in terms of GPUs, memory, and computing power.
- PoCs often operate in isolation, but scalability requires integration with existing IT infrastructure and business systems, which must be managed.
FROM PROTOTYPES TO ENTERPRISE AGENT APPLICATIONS
Our distinctive value lies in our ability to engineer and transform ideas and prototypes into robust, scalable AI solutions that are integrated into business processes. We do this by following a methodological approach based on two fundamental pillars.
1. We start from our customers' business and technological domain. Our unique ability to understand and design evolutions in companies' application portfolios allows us to go beyond stand-alone innovations and conceive them within the customer's technical and business ecosystem:
- Data: we analyse customers' various data sources, their structure and quality, to build efficient and reliable ingestion and processing pipelines.
- Applications: we understand the architectures of existing systems, ensuring smooth integration and avoiding the creation of technological silos.
- Processes: we map the workflows that AI will enhance to ensure that the solution integrates seamlessly and brings real value.
2. We engineer robust, flexible and secure solutions. To overcome the intrinsic challenges of AI (non-determinism, costs, security), we apply a rigorous development framework to guide and contain application behaviour through targeted engineering practices:
- Agile development and continuous feedback: we use short iterative cycles (sprints) with frequent demos. This incremental approach allows us to gather constant feedback from stakeholders, quickly correct the course and deliver business value from the earliest stages.
- Reliability, transparency and control: through an Adaptive Test Driven Development approach, we define objective performance metrics and subject the system to continuous testing to identify and mitigate unexpected behaviour, ensuring consistency of responses. We design systems in which it is always clear when the user is interacting with AI and we provide checkpoints where a human supervisor can validate or correct crucial decisions, ensuring control and accountability. We implement security mechanisms that, in the event of malfunction or abnormal AI responses, ensure service continuity by passing control to predefined logic.
- Compliance and Privacy by Design: from the architecture phase onwards, we analyse the constraints (such as GDPR) to which the company is subject, on a case-by-case basis. This analysis guides the choice of tools and platforms to be used, which guarantee the confidentiality of private and confidential information and the protection of intellectual property.
3. Conscious and human-centric approach. We adopt a co-design approach that actively involves users and stakeholders in shaping intelligent agents that reflect the real operational, decision-making and relational needs of the context in which they will operate. This approach allows us to integrate artificial intelligence with human intelligence, enhancing critical thinking, contextual intelligence and judgement. Only in this way is it possible to create solutions that are truly scalable in terms of adoption, sustainability and impact on the organisation.
BENEFITS
Thanks to integration with corporate systems and information storage capabilities, agents are revolutionising the way enterprise applications are used. Business logic is shifting towards a new AI layer capable of interacting directly with corporate data.
In particular, the benefits can be classified into the following types:
PERSONALISATION OF SOLUTIONS
Thanks to integration with company systems, AI agents can support specific use cases for different industrial sectors and individual companies.
INCREASED BUSINESS PRODUCTIVITY
An AI agent can make decisions based on real-time business data, reducing the number of errors, optimising and speeding up processes.
USABILITY REVOLUTION FOR USERS AND CUSTOMERS
AI agents interact in a personalised way based on the relevant application domain, improving interaction between systems and users.
GREATER SCALABILITY
AI agents allow you to manage a virtually unlimited volume of operations and requests without having to proportionally increase staff and fixed costs, enabling you to handle peaks in demand in an efficient and controlled manner.
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