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.
Need 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.
Scope this deploymentWhat it does
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
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.
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.
Models can deploy at the edge for latency-sensitive or network-restricted environments, running on embedded hardware or local inference servers.
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.
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.
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
- 1Requirements and hardware assessment
Task requirements, compliance constraints, and the target hardware profile are assessed to determine the appropriate base model and fine-tuning approach.
- 2Training data preparation
Proprietary data is structured into a fine-tuning dataset — examples, labels, and format specifications that encode the target task and domain knowledge.
- 3Model fine-tuning
The selected base model is fine-tuned on the prepared dataset, with evaluation checkpoints to verify task performance before deployment.
- 4On-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.
- 5Integration 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
- 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
- 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.
Tech stack
Don't see what you need?
Every listed product started as a custom engagement. If your problem isn't covered, describe it — Preisser Solutions scopes and builds to spec.