Artificial Intelligence & Agents
Maxxi builds and deploys AI models, copilots and agents connected to business knowledge, data, systems and rules, with engineering, governance and evolution designed in from the use case.
When AI needs to perform a real function in the business
The challenge shifts from accessing the technology to embedding intelligence in decisions and processes with value, control and the capacity to evolve.
Knowledge exists but does not reach the people who need to act
Fragmented information makes search, interpretation and response dependent on manual effort and specialists.
Repetitive decisions consume capacity
Classification, analysis and routing remain manual even when data and rules can support or automate them.
Automation stops when context changes
Rigid flows cannot handle tasks that require interpreting language, combining sources or deciding the next step.
The experiment works but does not enter the process
The technical proof remains isolated from the data, systems, users, security and responsibilities required for operations.
Scaling AI requires control
More models and agents increase cost, risk and complexity when access, quality, monitoring, approvals and accountability are undefined.
Lead with All intelligence in Data and AI
Maxxi combines data science, engineering and business knowledge to define the right way to embed AI in work:
AI and Machine Learning models
Maxxi builds models to classify, predict, recommend or support decisions, from data preparation through deployment and performance monitoring.
Generative AI and copilots
Maxxi creates experiences that retrieve, interpret, synthesize or generate content based on the organization's knowledge and context.
AI agents and intelligent automation
Maxxi connects agents to tools and systems so they can execute steps, orchestrate tasks and involve people when approval or judgment is required.
AI engineering, governance and operations
Maxxi structures architecture, integrations, testing, deployment, monitoring and evolution, with MLOps or GenAIOps practices when appropriate.
What turns AI into a business capability
Depending on the use case, complexity and risk, the solution may include:
The decision or process, use case, expected value and success criteria
AI architecture and integrations
Preparation of data, context and knowledge bases
A model, copilot, agent or automation and its interface
Evaluations, permissions, approvals and exception handling
Deployment, monitoring and direction for future evolution
BeeMaia: agents connected to context and operations
BeeMaia is Maxxi's platform for developing and orchestrating AI agents. It connects interfaces, knowledge bases, integrations and the AI engine in one environment.
Enterprise context
Agents access organizational knowledge and rules to respond and act in line with the process.
Integration and orchestration
Specialized agents share context and work across channels, tools and systems according to the task.
Control by design
Permissions, sources, retries, approvals and human handoffs are defined according to risk.
When appropriate, BeeMaia accelerates development, integration and governance without replacing the solution-specific design.
Choose the problem. Integrate the intelligence. Govern the evolution
Seven moves connect evidence, decisions and next steps. The depth of each one follows the goal of the work.
1Discover
Decisions, tasks, users, knowledge, data, systems, exceptions and risks.
2Plan
Expected value, the simplest suitable solution, success criteria, autonomy and approvals.
3Propose
Architecture, the roles of models and agents, integrations, experience and governance.
4Develop
Models, copilots, agents, interfaces, integrations, evaluations and defined controls.
5Activate
Validation in real situations with users, permissions, approvals and responses to exceptions.
6Operate
Deployment and monitoring of quality, cost, latency, usage and incidents by the owner or Maxxi's Data & AI Operations capability.
7Evolve
Performance, feedback and process changes guide adjustments and new use cases.
What sustains the way Maxxi works
Senior expertise that improves decisions
Senior professionals understand the business context and connect executive perspective with technical depth in critical decisions.
Executive oversight that anticipates risk
Maxxi leaders take part in critical moments, bringing benchmarks, options and recommendations to keep priorities aligned and correct course in time.
Outcomes that guide execution
Expected outcomes, timelines, cost and quality guide priorities, indicators and adjustments—not only scope completion.
Knowledge that strengthens internal capability
Maxxi structures knowledge, governance and support to strengthen the client's ability to operate, evolve and scale with confidence throughout the partnership.
AI applied to decisions, knowledge and processes
Recognized as a Rising Star in the ISG Provider Lens 2024, Advanced Analytics and AI Services, Data Science and AI Services category, Brazil.
CX 83.6above the industry average of 76.03
ISG Provider Lens 2024See the recognitionWhen the priority calls for another front
Data & AI Assessment
When maturity, readiness and priorities must be assessed before implementation.
Explore Data & AI AssessmentData Platforms
When the foundation must be built or modernized to support Analytics and AI.
Explore Data PlatformsData & AI Governance
When policies, roles, processes and controls need to scale.
Explore Data & AI GovernanceData & AI Operations
When production models, agents and AI products require continuous management.
Explore Data & AI OperationsWhat executives need to know about AI and agents
What is an AI agent?
An AI agent is a system that uses models, context and tools to decide and execute steps in a task. Its autonomy depends on permissions, approvals and risk.
What is the difference between Machine Learning, generative AI, a copilot and an agent?
Machine Learning identifies patterns to classify, predict or recommend. Generative AI produces or interprets content. Copilots support people; agents may also plan and execute steps using tools.
When should an organization use an agent instead of traditional automation?
Traditional automation works best for stable, predefined flows. Agents make sense when a task requires interpreting context, choosing steps or handling variation within defined limits.
What is required to put AI into production?
A use case, suitable data and context, architecture, integrations, evaluations, access, accountable owners and monitoring proportionate to the impact of the responses or actions.
How do governance and human participation become part of the design?
Permissions, sources, records, approvals, exceptions and human intervention vary with risk and the level of autonomy assigned to the solution.
How should the performance and value of an AI solution be evaluated?
Indicators depend on the use case. Quality, cost, latency, adoption, process outcomes and incidents provide a basis for monitoring value and risk.
Lead with All intelligence in Engineering, Data and AI.
In the conversation, Maxxi assesses the problem, the affected decision or process, expected value, data, knowledge, integrations and risks to define where AI should act, and with what level of autonomy and control.