Build agentic AI systems to solve problems and accelerate business results
Enhance digital products and experiences with AI agents for business, customized to your challenges and enterprise reality
Since the arrival of large language models in 2022, Artificial Intelligence agents have evolved from automating simple tasks to becoming components of coordinated, complex systems. As a result, the emergence of agentic AI and multi-agent architectures, capable of executing integrated workflows, is making it possible to redefine how work gets done in enterprises.
Despite the potential, while companies announce impressive results with the technology, numerous reports show the challenge is far from simple: as the MIT study indicates, 95% of projects never reach production. Not surprisingly, amid noise and promises, decision-making around Artificial Intelligence is shaped by experimentation and risk.
Rather than underestimate this, we take the context seriously. Not only did we develop the AI Sprint method as a way to break out of inertia — creating and validating a solution oriented to your business in just a few weeks — but we also adopted a posture of incremental deliveries in our solutions.
Inaction is just as risky as unconstrained experimentation. That is why it is essential to find structured ways to build solutions.
The continuous advancement of the technology and the complexities involved in building an enterprise solution demand incremental, business-oriented deliveries. Rather than starting big, our systems scale as they shift the metrics that matter for that specific business.
Even with its promise, success depends on an implementation that adapts to the business reality, not the other way around. The new paradigm demands technological updating, but also consistent integration with the company's vision and strategy, not a reaction to hype, or even a fear of missing out (FOMO).
How we develop multi-agent solutions:
Co-creating with business teams:
Immersed in the context and building a meaningful knowledge exchange with business teams, we validate a company-oriented proof of concept through our AI Sprint method.
Connecting the solution to specific, meaningful results:
Using lean analytics methodologies, we develop with a focus on key indicators with the highest potential for business impact, incrementing the solution with each validated result.
Aligning strategy, design, research, and engineering:
We bring together a team experienced in applying technology in large enterprises, combining design, engineering, research, and strategy throughout the process, always co-creating with the company's business teams.
Applying Artificial Intelligence where it truly generates results:
By blending deterministic and Generative AI models, we reduce exposure and the risk of hallucinations, amplifying AI capabilities where they actually impact the business.
Developing complementary autonomous AI systems and agent architectures:
Rather than a large system oriented toward task automation, we work with specialist agents — optimized to take responsibility for specific tasks — that, working in a coordinated way, solve problems.
Prioritizing integration and company autonomy with the solution:
Beyond empowering the company's people with the technology, we adapt our systems, frameworks, and models to the technological reality and needs of each company, preserving their autonomy.
Using advanced Prompt Engineering techniques:
Following our team's continuous curiosity, we work from advanced Prompt Engineering techniques, always updated on new possibilities, as we explore in this article on APE.
Building with validated frameworks suited to different enterprise contexts:
Over the years, we have not only tested multiple frameworks, but also validated proprietary structures using advanced long chain graph techniques, adaptable to different enterprise realities.
Making progressive, not reactive, improvements:
Using proprietary observability techniques and platforms, our systems grow in line with results achieved, supported by continuous testing and exposure and risk alerts.
Mapping the impact and ethical responsibility of the solution:
Our systems are built with a guided ethical concern, where responsibility and any adaptation initiatives progressively accompany the development of the solutions.
From business to technology
Our experience shows that to win fast, prioritization and close collaboration with business teams are essential.
Enthusiasm for the technology must not override business strategies, much less minimize the challenge of growing an enterprise. As we have learned, through iterations of our solutions, the true potential of the new paradigm is achieved in parts: from the specific to the broad.
As our experience applying the technology at companies like Mars, Movida, and Reckitt reveals, the best solutions are those that configure and adapt to the company's processes, infrastructure, and strategies.