Data & AI Operations
Maxxi operates and evolves production pipelines, data products, models and agents, combining observability, automation and engineering to keep quality, performance, cost and risk under control.
When production Data and AI require specialized operations
Production does not end the work. Data changes, integrations fail, models lose performance and costs vary. Without clear signals and responsibilities, impact appears before the cause.
Users see the problem first
Business functions notice missing data, unsuitable responses or interrupted processes before operations identify the deviation.
Pipelines deliver without trust
Completed runs may conceal delays, losses, duplicates or changes that compromise data products and decisions.
Models and agents change behavior
Quality, performance, latency and cost can change with the data, context, users and integrations.
Deployments and fixes depend on manual intervention
Releasing versions, validating results and recovering failures require recurring effort and increase operational variability.
Incidents are resolved, but causes remain
The response restores service, but recurring problems continue to consume capacity when they do not reach engineering.
Lead with All intelligence in Operations
Maxxi connects operations and engineering according to the production asset:
DataOps and pipeline operations
Maxxi monitors ingestion, transformation, orchestration and delivery, using signals for freshness, quality, dependencies and incidents suited to each flow.
MLOps and model operations
Maxxi organizes versions, deployment, performance, degradation, feedback and retraining when they are part of the lifecycle and scope.
Copilot and agent operations
Maxxi monitors quality, latency, cost, integrations and incidents according to each solution's use, autonomy and risk.
Observability, automation and evolution
Maxxi correlates signals, automates recurring routines, investigates causes and converts operational evidence into improvement priorities.
Visibility and control for operations and evolution
Depending on the assets, criticality and service model, the work may include:
Scope, critical assets, coverage, responsibilities and service objectives
Observability architecture, indicators, dashboards and alerts
Deployment automation, versioning and traceability
Incident, problem and change flows, runbooks and escalation
Quality, performance, latency, cost, usage and risk measures suited to each asset
Improvement priorities, documentation and knowledge transfer
Establish visibility. Respond with control. Evolve with evidence
Seven moves connect evidence, decisions and next steps. The depth of each one follows the goal of the work.
1Discover
Critical assets, dependencies, incident history, owners, routines and available signals.
2Plan
Scope, coverage, service objectives, indicators, roles, escalation and transition.
3Propose
Operating model, observability, automation, service flows and improvement priorities.
4Develop
Instrumentation, dashboards, alerts, automation, controls and runbooks included in scope.
5Activate
Operations transition, access, baselines, response validation and communication among owners.
6Operate
Monitoring, incidents, problems, changes, approved releases and service-status communication.
7Evolve
Causes, recurrence and trends in quality, performance, cost, usage and risk guide improvements.
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.
The proof of how Maxxi operates
No published case study for this service yet. The corporate proof stands on its own, and no case study from another practice is presented as if it belonged here.
CX 83.6above the industry average of 76.03
ISG Provider Lens 2024See the recognitionWhen the priority calls for another front
Data Platforms
When the foundation, pipelines and technical components must be built or modernized before recurring operations begin.
Explore Data PlatformsArtificial Intelligence & Agents
When the priority is to design and implement models, copilots or agents before structuring their production operations.
Explore Artificial Intelligence & AgentsData & AI Governance
When responsibilities, policies, criteria and controls need to guide asset use and lifecycle.
Explore Data & AI GovernanceApplication Support & Evolution
When the application is the primary object of support, correction and ongoing development.
Explore Application Support & EvolutionWhat executives need to know about Data and AI operations
What are Data & AI Operations?
The continuous management and evolution of production pipelines, data products, models and agents, using observability, response, automation and engineering practices suited to each asset.
What is the difference between DataOps, MLOps, GenAIOps and AIOps?
DataOps structures data flows and quality. MLOps covers the operational lifecycle of models. GenAIOps applies equivalent practices to generative solutions and agents. AIOps uses AI to improve IT operations and is not a synonym for the other terms.
What should be monitored in data pipelines?
The set of signals depends on the flow and may include freshness, completeness, volume, errors, duration, dependencies, quality and delivery to the intended consumer.
What should be monitored in models and agents?
Depending on the use case: performance or quality, degradation, latency, cost, incidents, adoption and impact. Signals may guide changes or retraining within the agreed scope and approvals.
Does the service include an SLA or 24×7 coverage?
Not as a universal standard. Coverage and service objectives must reflect criticality, impact, hours of use, internal response capacity and contractual terms.
What is the difference between Data & AI Operations, Application Support and BPO?
Data & AI Operations manages pipelines, products, models and agents. Application Support & Evolution focuses on the application. Managed Services & BPO operates recurring business processes.
Lead with All intelligence in Engineering, Data and AI.
In the conversation, Maxxi assesses critical assets, dependencies, incident history, available signals, responsibilities and the current model to define what must be observed, operated and evolved.