Local models and private deployment

Private AI for software products

When data, latency or infrastructure control matter, the architecture must decide what runs locally and how it will be operated.

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The problem

Privacy and operations belong in the same design

Data that must stay in its environment

Identify what is processed, which services are involved and where each part runs.

Local models with real constraints

Evaluate quality, hardware, latency and maintenance for the product’s actual tasks.

Control and review

Define permissions, traces and human review points instead of assuming private deployment solves everything.

How we work

Technical decisions we scope

01
Data requirements
Map sensitivity, residency, access and third-party boundaries.
02
Test against the task
Compare local and hosted options against the same quality, latency and cost criteria.
03
Operations and handover
Document deployment, controls and what the team needs to maintain it.

Project evidence

Two distinct experiences with local AI

In the Irontec collaboration, Konect was adapted to process locally a planned workload of 500 calls per day. Biiak is a product co-founded by Aritz, using local AI for sensitive information.

The call count describes the workload considered for the adaptation, not a performance benchmark. Biiak is identified as a co-founded product, not a TenBeltz client.

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A fitting way to start