Pilardin Enables On-Premise AI Deployment Inside the Customer Network

What Changed: On-Premise Deployment Is Now a Supported Path in Pilardin

Pilardin has enabled on-premise deployment support across its business AI workspace, allowing organizations to run document chat, live-system connectors, and Matrix AI modules inside their own data centers or private cloud instead of a vendor-hosted environment. The change addresses the question that stalls most enterprise AI projects long before model quality is even discussed: where the data lives, who can reach it, and what an auditor can reconstruct afterwards. For security architects and compliance teams, on-premise support converts a policy argument into a deployment option they can test inside their own network.

The headline is not that on-premise AI exists as a concept. Self-hosted inference and local retrieval are well-established engineering patterns, and many large enterprises already run internal model gateways. What is new here is that the same workspace, retrieval pipeline, and module set can now be operated under customer-controlled infrastructure, so a team does not have to choose between capability and control. The boundary moves, not the product. In practice, the vector index, the document store, the model endpoints, and the access logs sit on infrastructure the customer's own platform team can inspect, patch, and defend.

How an On-Premise Pilardin Installation Works

An on-premise Pilardin deployment is delivered as a versioned software bundle that the customer's infrastructure team installs on its own Kubernetes cluster, virtual machine fleet, or private cloud tenancy. Retrieval runs locally: documents are ingested, chunked, embedded, and indexed inside the perimeter, and queries are answered from that local index rather than from a remote service. Model inference is pointed at endpoints the customer controls, typically self-hosted open-weight models behind an internal inference gateway, which lets platform teams swap models as hardware, licensing, or performance conditions change.

Because the same connector layer is used in the hosted product, on-premise installations can still reach operational systems such as databases, shared drives, and enterprise file storage, provided network policy allows it. Administrators retain control over egress: an installation can be configured to permit outbound traffic only for license validation and update checks, or to run in a fully air-gapped mode where releases are imported as offline packages. Identity generally maps to the customer's existing directory and single sign-on, so document permissions reflect the groups an organization already maintains rather than a parallel access list that drifts out of date.

• Deployment target — customer-managed Kubernetes or VM infrastructure, with no requirement to expose the workspace to the public internet.
• Model layer — internal inference endpoints or a self-hosted model gateway, so model choice stays with the customer's platform team.
• Retrieval layer — embeddings, index, and permission mapping remain inside the perimeter and follow existing document access controls.
• Update path — versioned release bundles and a scheduled upgrade window, which matters in regulated environments where changes need a formal change record.

What On-Premise Changes for Business Teams

The strongest driver is data residency and auditability. Organizations in banking, insurance, healthcare, defense supply chains, and government-adjacent work often cannot send document text to an external endpoint, even under contract, because the classification of the source material does not allow it. Running retrieval and inference locally removes that objection at the architectural level instead of negotiating it clause by clause. It also simplifies evidence gathering: the prompt, the retrieved passages, the model version, and the acting user can be logged in one place inside the customer's own logging pipeline.

The trade-off is that infrastructure and operational responsibility shift to the buyer. On-premise AI changes the cost shape from a subscription line item into a combination of compute, storage, backup, monitoring, and staff time — GPUs or high-memory CPUs for inference, snapshot and retention policy for the index, and a named owner for upgrades. That is a reasonable trade for a regulated workload with steady demand, and a poor one for a small team that wants to validate an idea this quarter. Most enterprises will end up running both: hosted workspaces for exploratory and low-sensitivity work, and on-premise installations for document sets that can never leave the building.

• On-premise deployment — best for regulated data sets, strict residency rules, air-gapped sites, and steady high-volume workloads.
• Hosted workspace — best for fast pilots, elastic demand, and teams without dedicated infrastructure staff.
• Hybrid — best for organizations that want central governance with local execution at branches, plants, or subsidiaries.

Practical Steps Before You Plan an On-Premise Rollout

Start with a data and permission inventory rather than with hardware. The retrieval layer inherits access rules from source systems, so an on-premise deployment is only as clean as the folder structures, group memberships, and database roles it reads from. Teams that skip this step often discover during the pilot that the index is answering from documents half the pilot group was never meant to see. Mapping sources, owners, and sensitivity levels first makes the later configuration work mostly mechanical and shortens the security review considerably.

Sizing comes next, and it should be driven by concurrency and document volume rather than by a generic reference architecture. A departmental pilot covering a few hundred thousand pages with a handful of simultaneous users can run on modest hardware with quantized open-weight models. A company-wide deployment with thousands of daily users and frequent re-indexing needs a different plan for GPU capacity, storage throughput, and failover. Sequencing then matters: pilot with one function, measure answer quality against a fixed question set, define the rollback path, and only then expand scope. Change management is not optional either, because an on-premise system still needs owners, escalation paths, and training for the people expected to use it daily. Teams that want to see how licensing and support map to a self-hosted installation can review options at /pricing, while those evaluating specialized modules can start at /matrix.

What to Watch

The practical test of on-premise AI is the update cadence. A local installation only stays useful if retrieval improvements, connector additions, and security patches reach it on a predictable schedule, and that is harder in air-gapped environments where every release is a manual import. Buyers should ask what the release rhythm looks like in writing, how long each version is supported, and what happens operationally when an upgrade fails midway through a maintenance window.

The second thing to watch is the quality distance between self-hosted open-weight models and the strongest hosted frontier models. That gap has narrowed for retrieval-grounded tasks, where the answer is largely determined by what the index retrieves, but it has not closed for complex multi-step reasoning. Organizations should decide per use case whether local inference is good enough, and design the workspace so a model endpoint can be replaced without rebuilding the retrieval layer underneath it. The value of on-premise deployment is not that it makes enterprise AI cheaper or smarter by itself. It removes a blocking objection, and the teams that execute well are the ones that treat it as an infrastructure program rather than a single purchase.

FAQ

Does on-premise deployment include the same features as the hosted Pilardin workspace?

The intent is feature parity across the core workspace: chat over documents, retrieval with citations, connectors to live operational systems, and the Matrix AI modules. The difference is where components run, not which capabilities are supposed to exist. Because some hosted conveniences depend on external services, administrators should confirm during scoping which optional integrations work in an air-gapped configuration and which require limited outbound access.

Can a Pilardin installation run completely offline?

Yes. Deployments can be configured for air-gapped operation, where the workspace does not reach the public internet and updates are delivered as offline packages imported by the infrastructure team. This is usually the right configuration for classified or highly sensitive document sets. The trade-off is operational: patching, model updates, and support diagnostics take longer because they cannot be pushed automatically, so an air-gapped site needs a named owner and a maintained change calendar.

Who maintains the hardware and the models?

The customer does. Pilardin provides the software, deployment guidance, and support for the application layer, while compute, storage, networking, backup, and the underlying model serving stack remain the responsibility of the customer's platform or infrastructure team. Some organizations run inference on their own GPU capacity; others route requests to an internal model gateway managed by a central AI platform group. Which model serves which use case is a governance decision that should be agreed before the first pilot, not after it.