Internal documents
Contracts, procedures, code, customer records, or operating knowledge that should not move into unapproved tools.
Private AI for companies
Private/local AI for companies with sensitive data
We help CTO, security, and operations teams evaluate and implement private AI with local models, controlled RAG, and internal assistant workflows without exposing sensitive documents or critical processes to unmanaged endpoints.
Problem/risk
Unmanaged AI creates quiet exposure.
Public copilots, isolated experiments, browser extensions, and team-by-team model choices can put internal documents into uncontrolled workflows, create untraceable answers, and leave operations dependent on tools no one formally owns.
Who this is for
Teams under pressure to adopt AI without losing control.
CTO, IT, security, operations, legal/compliance, and engineering leaders who need a governed route before scaling AI across sensitive knowledge, customer data, or critical internal workflows.
Local inference choices
Teams comparing workstations, internal servers, NVIDIA GPUs, Apple Silicon, or controlled hybrid options before budget is committed.
Private assistant routes
Businesses that need controlled RAG or assistant workflows with permissions, review gates, and operational ownership.
What we can implement
Private AI architecture and deployment with real controls.
We do not sell a generic demo. We define scope, stack, security boundaries, operations, and documentation so the solution can be sustained by the internal team.
Local workstation or serverInitial sizing for Mac, NVIDIA, internal server, or a justified hybrid option.
Ollama / LM StudioInstallation, model governance, user access, update criteria, and support boundaries.
Private RAG prototypeControlled retrieval over internal documents with known sources, permissions, and quality checks.
Internal assistant workflowSupport, analysis, or operations workflows with human review and rollback criteria.
Security boundariesIdentity, network, logging, retention, and data separation before broader rollout.
How engagement works
From assessment to controlled operation.
Assessment → target architecture → prioritized implementation → user validation → documentation and transfer to the internal team.
- Use-case and data inventory.
- Risk matrix for each AI workflow.
- Access controls and traceability requirements.
- Operating runbook and maintenance responsibilities.
Trust
What we do not promise.
We do not promise miracle automation, fake enterprise guarantees, public-client claims, or deployments without validation. The recommendation may be to start small, exclude sensitive data, or delay a workflow until controls are strong enough.
- Validation first: use cases, data, and risk before scale.
- No unsupported claims about compliance, productivity, or security outcomes.
- Clear documentation so internal teams understand limits and operation.
- Rollback criteria if a pilot does not show enough value or control.
FAQ
Frequently asked questions
Does private AI mean cloud is forbidden?
No. We evaluate local, private, and hybrid options by data sensitivity, operational continuity, latency, and internal control requirements.
Can this support multilingual teams?
Yes. We test model behavior, prompt patterns, and retrieval quality for bilingual operations where Spanish and English content both matter.
Do you include infrastructure hardening?
Yes. Identity, endpoints, admin access, backup posture, and server controls are part of the readiness review before AI expands.
Who operates the platform after handoff?
Your team should own the platform. We document decisions, runbooks, rollback criteria, and maintenance routines so operation is not dependent on undocumented consulting work.
Start with the controls before scaling AI usage.
Use a focused assessment to decide what should be local, what can be hybrid, and what must wait until governance is stronger.