Capability • AI Engineering

Custom Local AI Models — No Cloud, No Data Leaving the Network

Local AI models tuned for specific business operations. No cloud dependency, no third-party data egress, and full control over the proprietary logic that runs on top.

Local

Deployment, no cloud dependency

Data leaving the network

Custom

Fine-tuning on business-specific data

Edge

On-premises or edge deployment options

Local

Deployment, no cloud dependency

Data leaving the network

Custom

Fine-tuning on business-specific data

Edge

On-premises or edge deployment options

Before

Cloud AI is fast — and not always the right answer.

Sending business data through a third-party AI provider is the default path. It is also the wrong path in plenty of cases. Regulated industries restrict where data can travel. Trade-secret-heavy operations have proprietary logic they cannot expose. Air-gapped facilities cannot route through anyone's API. The general-purpose model is also rarely tuned for the operation being modeled.

Local AI changes that. The model lives on hardware you control, trained on your data, and the proprietary logic that sits on top stays in-house. The trade-off is engineering complexity — local deployment is not a check-the-box workflow.

What we built

Locally deployed models, custom-tuned, proprietary logic on top.

Preisser Solutions builds custom AI models that run on the client's own infrastructure — on-premises servers, dedicated machines, or edge devices. The model is fine-tuned on the client's business-specific data so the output reflects the actual operation rather than a generic baseline.

Proprietary logic — business rules, scoring frameworks, escalation criteria — integrates directly on top of the local model. Nothing leaves the network. Updates and re-training cycles happen on the client's schedule, in the client's environment. The pattern fits regulated industries, secrecy-heavy operations, and air-gapped facilities where cloud AI is structurally off the table.

Specifications

Capability surface.

Local model deployment with no cloud dependency

Custom fine-tuning on business-specific data

Proprietary business logic integrated on top of the model

On-premises or edge deployment options

Air-gapped network compatibility

Re-training cycles run on the client's schedule

Deployment options

On-premises server hosting
Edge device deployment for distributed operations
Air-gapped network configurations
Hybrid options where appropriate

What clients control

Training data — never leaves the network
Proprietary logic on top of the base model
Update and re-training cadence
Access policies and audit logs

Results

Outcomes the engagement actually produced.

Result 01

Local

Deployment with no cloud dependency

The model runs on the client's hardware — server, dedicated machine, or edge device. Cloud services are not in the loop.

Result 02

Data leaving the network

Training data, inference inputs, and outputs all stay inside the client's network boundary.

Result 03

Custom-tuned

Fine-tuning on business data

The model is fine-tuned on the client's business-specific data so output reflects the actual operation, not a generic baseline.

Result 04

Edge-ready

On-premises or edge deployment

Architecture supports on-premises hosting, edge devices for distributed operations, or air-gapped network configurations.

Tech stack

Local model fine-tuningOn-premises deploymentEdge inferenceAir-gapped deployment patternsCustom business logic integrationProprietary scoring frameworks

More work