AI collaborator
Lumo · AI collaborator
Supports analysis, engineering and validation under human oversight. A digital collaboration identity, not a person or an executive officer.
How we work
A method to move from understanding to scale.
We work in stages, with evidence-based decisions and teams shaped around the goal.
Operating model
From a real problem to a repeatable capability.
We move in stages. Each one builds on evidence from the previous one; not every initiative needs to reach the last.
Understand
Context, goals and constraints before proposing.
Solve
A solution that works in the real context.
Repeat
Confirm the result holds in other cases.
Standardize
Turn what is proven into practices and components.
Automate
Reduce manual work where a standard exists.
Productize
Scale what is proven as an accelerator or product.
How the capabilities connect
Consulting discovers problems, engineering solves them and operations reveal what repeats. Patterns become prepared talent and, once proven, accelerators.
Engagement models
Teams shaped around the goal.
We do not start from a fixed headcount. We define the engagement by what needs to be achieved and adjust it as it progresses.
Assess
Diagnosis and roadmap before committing to delivery.
Build
Design and development of a solution with delivery accountability.
Embed specialists
Specific profiles who join your team and ways of working.
Cross-functional team
A complete product, engineering and data team around one goal.
Operate
Operation, support and continuous improvement of production solutions.
Transform
Modernization programs that combine several capabilities in stages.
Responsible AI
AI proposes. Evidence proves. Humans own.
We use AI where it adds value and treat it like any critical system: explicit criteria and tests. Every result has a named person who reviews it, approves it and is accountable for it.
Quality evaluation
Test sets and agreed criteria before production.
Tool use
Scoped permissions for what the model can query or execute.
Security
Data protection, access control and risk review.
Latency
Response times suited to the real workflow.
Cost
Consumption measured and controlled against the value it creates.
Human oversight
People accountable for reviewing, correcting and deciding.
AI with people accountable.
AI proposes. Evidence proves. Humans own. Evaluate quality, permissions, exceptions and task cost before expanding use.
Entry point
Technology and AI assessment.
A structured way to start. We review your situation and define what to do, in what order and with which capabilities.
Scope, duration and investment are defined with you after a first conversation.
Possible deliverables, depending on scope
Current state
Systems, data, processes and teams as they operate today.
Risks
Technical, operational and security failure points.
Opportunities
Where technology and AI add measurable value.
Target architecture
Where to evolve and why.
Gaps
What is missing in technology, data and capabilities.
Roadmap
Prioritized initiatives and recommended sequence.
Tell us what you need to solve.
You don't need to arrive with the architecture or the team already defined. We start by understanding.
Assess my opportunity
Or email us at sales@arvix.com.mx