Every business is being told it needs an "AI strategy." Few get help turning that into something that actually works day to day. Mota Systems' AI & GenAI Consulting practice focuses on identifying where artificial intelligence and generative AI create real, measurable value for your business, and then building it properly — grounded in the same data engineering, automation, and software delivery discipline behind our other services.
Nobody Has Ten Years of This
Agentic AI is new. The tooling that makes it practical is barely two years old, so any supplier implying a long track record with it is stretching. That makes "years of experience" the wrong thing to ask about. The useful question is narrower: have they shipped with it, or have they only trained on it?
Training teaches you what the tools claim to do. Shipping teaches you what they actually do at three in the morning, when the input is malformed, the model is confidently wrong, and something downstream has already acted on the output. We have built and run these systems, and almost everything we know that is worth paying for came from the second category.
Where AI Isn't the Answer
Plenty of the problems presented to us as AI problems aren't. If a process is high-volume and rule-based, conventional automation will be cheaper, faster and far easier to audit — and we'll point you at our Automation Development service instead. If the underlying data is inconsistent or scattered, no model will rescue it, and the honest first step is fixing the data. And if a task carries real regulatory or safety weight, "mostly right" is not a standard worth accepting.
We'd rather tell you that early than build something impressive that quietly gets switched off six months later.
Our AI & GenAI Services
AI Strategy & Readiness: Identifying where generative AI and machine learning create genuine business value for your organisation, rather than adopting AI for its own sake.
Generative AI & Multi-Agent Solutions: Designing and building multi-agent AI solutions, including with Microsoft Copilot Studio, to automate more complex, judgment-based work than traditional rule-based automation can handle.
Data Science & Predictive Analytics: Building the data engineering pipelines and predictive models that turn your organisation's data into forward-looking insight, not just historical reporting.
Prompt Engineering & AI Enablement: Designing effective prompts and workflows, and training your team to use generative AI tools safely and productively in their day-to-day work.
AI-Augmented Automation: Pairing generative AI with our existing RPA and workflow automation practice to build processes that can handle exceptions and judgment calls, not just fixed rules.
Responsible AI Practices: Building solutions with careful attention to data privacy, accuracy, and responsible use, and being upfront with you about where independent legal or regulatory review is the right next step.
Got an Idea You Want Sense-Checked?
Bring us the problem rather than the technology, and we'll tell you straight whether AI is the right tool for it.
Who You'll Be Working With
Two people with different halves of the problem: the strategy, data and multi-agent design, and the engineering discipline to actually ship it.
Bruno Gomes
Bruno leads Mota Systems' AI & GenAI consulting practice in Brazil. He brings over 21 years at Fundação Getulio Vargas, currently serving as Systems Manager responsible for Salesforce, Dynamics 365, satellite systems, and artificial intelligence projects. As Tech Lead for CRM and AI initiatives, he led AI projects focused on education alongside Salesforce and Dynamics 365 implementations, and his broader technical background spans data engineering, data science, and generative AI across Azure, AWS, and Databricks.
He holds an MBA in Artificial Intelligence in Military Systems and an MBA in Business Technology (AI, Data Science & Big Data), along with certifications in Copilot Studio multi-agent solution design and prompt engineering for generative AI.
Joao da Mota
Joao covers the delivery side. He brings over 20 years of software engineering and more than a decade leading teams, and works with agentic coding tools daily in his own delivery practice — Claude Code, Cursor, GitHub Copilot and Gemini. He has delivered agentic AI project work for clients and led engineering teams using these tools: shipped, not just trained on.
That distinction matters more than it sounds. The hard part of an AI project is rarely the model. It is the integration, the data plumbing, the error handling, and the question of what happens when the system is confidently wrong and something downstream has already acted on it. Those are ordinary engineering problems, they are where these projects fail, and twenty years of judgment about them does not go out of date because the technology is new.
Who This Is For
This service is for businesses that want to explore AI and generative AI seriously, but don't have anyone in-house who can separate a genuinely useful application from a novelty. It pairs especially well with our Automation Development service for organisations looking to extend rule-based automation with AI that can handle judgment calls, and with our Fractional Technology Leadership service for businesses that want ongoing technology leadership with AI expertise built in.
Related Services
Curious What AI Could Do for Your Business?
Let's talk about where AI and generative AI could genuinely help — and where they wouldn't.