Private & secure AI
AI for confidential work,with defined controls.
We help you decide where each type of information is processed and who can access it. We implement the agreed configuration, including your own servers where appropriate, and train your team in Mexico City to use it.
Context
Start with the data and how you intend to use it
A contract, a case file and public copy require different controls. Before choosing a model, we examine the information a task needs, who authorizes its use and which providers process it.
A business account or API may offer useful controls. Its current terms, retention, access and configuration need to be checked for the intended use. A product label alone does not settle that assessment.
The project combines technical implementation and training. We document decisions, responsibilities and a process for reviewing new uses with your technical and legal teams.
The approach
Four things every firm has to settle
Public, private or on your own servers
Data handling rules
A usage policy to review and apply
Tool and provider selection
What you walk away with
Documented decisions and controls
A map of the data, tools and people responsible for each AI workflow.
A usage policy for review by your legal or compliance team, with examples your staff can apply.
Tools assessed against documented access, retention, data use and operational requirements.
The agreed configuration, with tests and documentation of the controls included in scope.
A procedure for reviewing incidents, provider changes and new uses.
Questions
What firms ask us first
Can we use an AI tool with confidential data?
That depends on the data, the purpose and the technical and contractual controls. Before information is uploaded, we review permissions, provider terms, retention and integrations. Where a use requires your own servers, we include that option in the assessment.
What does a private AI project in Mexico City include?
Scope may include data mapping, model selection, deployment, testing, documentation and on-site training with remote reinforcement. The proposal defines the functions and responsibilities to be delivered.
What data is used during training?
We agree on authorized materials and environments in advance. We can use fictional or de-identified examples; real documents require authorization and appropriate controls. The project defines access and material retention.
How is the LFPDPPP reviewed in a private AI project?
The relevant legal team reviews the data processed, purposes, notices, providers and controls. A policy or deployment alone does not establish compliance with all of a business’s obligations.
Can we run AI on our own infrastructure?
Yes, where the model, hardware and scope support it. We review capacity, access, maintenance and external connections. Operating a local model also requires controls for logs, backups and integrations.
How is private AI priced?
The quote depends on the models, infrastructure, integrations and support included. We separate those items so you can compare options and decide what to operate internally.
From the blog
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Let's begin
Define how your team will use AI
Tell us which tasks you want to support and what information they use. Together we identify the controls and scope the project needs.