AI that runs on your hardware — no cloud, no data exposure.
Custom AI models fine-tuned on your proprietary data and deployed on-premises or at the edge — no API calls to external providers, no sensitive data leaving the network, full operational control.
Cloud-dependent AI has a ceiling for businesses where data sovereignty, compliance requirements, or network constraints make external API calls a non-starter. Healthcare systems processing patient records, financial institutions with regulatory constraints, manufacturers with proprietary process data, and any organization operating in air-gapped or restricted environments cannot route sensitive data through external model providers.
Custom Local AI Deployment builds and fine-tunes models that run entirely on the client's hardware. The base model is selected for the hardware profile and task requirements, fine-tuned on the client's proprietary data and business logic, and deployed in an on-premises or edge configuration. After deployment, the system operates with no cloud dependency — inference runs locally, data stays on the network, and there are no external API calls in the data path.
The proprietary logic encoded in the model during fine-tuning — domain terminology, decision frameworks, document formats, classification rules — is the model's knowledge, not a prompt that could be intercepted or a configuration that requires cloud services to apply. The intelligence moves with the deployment.
Capabilities
- 01
On-premises model deployment
Models deploy to the client's hardware and run entirely locally — no inference traffic routed through external providers, no internet dependency in the processing path.
- 02
Custom fine-tuning on proprietary data
Base models are fine-tuned on the client's specific data — documents, terminology, decision rules, and output formats — so the deployed model reflects the business's actual knowledge.
- 03
Edge deployment support
Models can deploy at the edge for latency-sensitive or network-restricted environments, running on embedded hardware or local inference servers.
- 04
Zero data-leaving-network architecture
The full inference path — data in, model processing, output out — runs within the client's network boundary. No external API calls are made during production use.
- 05
Proprietary logic integration
Business-specific decision frameworks, classification rules, and domain knowledge are encoded into the model during fine-tuning rather than applied as prompt engineering at inference time.
- 06
Hardware-profile-matched model selection
Model selection accounts for the target hardware profile — GPU memory, inference latency requirements, and throughput targets — to match the deployment capability to the operational need.
How it works
- 01
Requirements and hardware assessment
Task requirements, compliance constraints, and the target hardware profile are assessed to determine the appropriate base model and fine-tuning approach.
- 02
Training data preparation
Proprietary data is structured into a fine-tuning dataset — examples, labels, and format specifications that encode the target task and domain knowledge.
- 03
Model fine-tuning
The selected base model is fine-tuned on the prepared dataset, with evaluation checkpoints to verify task performance before deployment.
- 04
On-premises deployment
The fine-tuned model deploys to the client's hardware with an inference API layer — fully self-contained, no external dependencies in the serving path.
- 05
Integration and validation
The deployed model integrates with downstream business systems via the local API, and production accuracy is validated against the client's operational data before go-live.
Inputs & Outputs
What it takes in
- Proprietary training data (documents, records, labeled examples)PDF / CSV / JSON / structured database export
- Hardware specification for target deployment environmentTechnical specification
- Task requirements (classification, extraction, generation, etc.)Requirements document
- Compliance and data handling constraintsCompliance documentation
- Inference latency and throughput requirementsRequirements document
What it sends out
- Fine-tuned model weights deployed to target hardwareModel files / container
- Local inference API endpoint for downstream integrationREST API / local socket
- Deployment documentation and operational runbookDocument
- Fine-tuning evaluation report with accuracy benchmarksReport
Use cases
Use this when regulatory requirements prohibit patient, financial, or operational data from being sent to external AI providers — but you need AI-powered automation in those workflows.
Use this when latency requirements make cloud inference impractical — edge deployments require sub-100ms inference that cloud round-trips cannot reliably deliver.
Use this when the domain knowledge required for the task is sufficiently proprietary that fine-tuning on internal data produces materially better results than general-purpose cloud models.
Use this when network constraints (air-gapped environments, limited bandwidth, unreliable connectivity) make cloud-dependent AI architectures operationally unreliable.
Use this when intellectual property concerns mean that prompts and model inputs should never leave the organization's network boundary.
Related works
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Custom & InfrastructureCompliance Agent
Everything runs through compliance before it ships. Reads regulatory documents and internal policy, flags risk on every artifact, produces audit-ready summaries.
Operations & Back-OfficeNeed AI that runs inside your network — not through someone else's cloud?
Preisser Solutions scopes on-premises AI deployments from hardware assessment through fine-tuning and integration. The first conversation covers your task requirements, hardware environment, and compliance constraints.
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