On-premise AI agents
Run governed AI workflows inside your company network.
Dialogo supports fully on-premise execution for organizations that cannot send sensitive operational data to external AI services. The orchestration layer, models, workflow memory, integrations, guardrails, and run history can operate on infrastructure controlled by the customer.
The operating problem
Sensitive operations cannot always cross a cloud boundary.
Manufacturing, finance, healthcare, government-adjacent, and regulated organizations may need AI execution without exposing internal records, documents, credentials, or prompts to external model providers. Dialogo can run with local models and local infrastructure so workflow data remains inside the company network.
A strong fit for
- Organizations with strict data-residency or confidentiality requirements
- Companies deploying local LLMs on private infrastructure
- Regulated teams that need auditable system access
- Hybrid environments that selectively permit frontier models for approved tasks
From event to evidence
One controlled execution chain.
Choose the deployment boundary
Define the network, infrastructure, data stores, identity systems, and approved external connections.
Deploy the execution layer
Run orchestration, workflow memory, guardrails, and records in the customer-controlled environment.
Connect local systems
Integrate internal APIs, ERP, CRM, databases, documents, email, and other permitted services.
Route to approved models
Use local LLMs by default or allow selected frontier models only for explicitly approved data and tasks.
Govern every action
Apply scoped permissions, validation, approval gates, and the Dialogo guardrail engine.
Retain evidence locally
Keep workflow memory and the complete Run Ledger inside the company-controlled environment.
Platform capabilities
Control the work, not only the model.
Nothing leaves the network
In a fully local configuration, prompts, records, documents, model inputs, outputs, and run evidence remain inside the company boundary.
Local LLM support
Use privately deployed open or proprietary models selected by the customer.
Local orchestration
Keep workflow execution, memory, evaluation, and tool coordination on customer infrastructure.
Frontier-model guardrails
For approved hybrid use, the guardrail engine controls which data and actions may reach external models and tools.
Private integrations
Connect internal systems without exposing them as public internet services.
Customer-owned evidence
Store logs, approvals, errors, changes, and outcomes under the customer’s retention and access policies.
Example workflow
A real event, handled inside a defined boundary.
A production-quality exception contains confidential customer and machine data.
- 1Read the internal QMS and production records locally
- 2Use a local LLM to classify the exception
- 3Prepare the permitted corrective workflow
- 4Route the release decision to an authorized employee
- 5Store the complete evidence record on-premise
Outcome
The workflow gains AI-assisted execution without sending sensitive operational content outside the company network.
How success is measured: Deployment design is validated against the customer’s network boundary, model policy, identity system, retention rules, permitted integrations, and approval requirements.
Questions teams ask before they deploy
Does anything leave the company network?+
In a fully on-premise deployment, workflow data, model inputs and outputs, memory, credentials, and execution evidence remain inside the customer-controlled network.
Can Dialogo use local LLMs?+
Yes. Dialogo is model-independent and can route workflow steps to models deployed on the customer’s own infrastructure.
Can we combine local and frontier models?+
Yes, when policy allows it. A hybrid deployment can keep sensitive work local and route explicitly approved tasks to frontier models through the Dialogo guardrail and permission layer.
Who controls the infrastructure and logs?+
The customer controls the deployment environment, access policy, model endpoints, connected systems, retention rules, and locally stored execution history.
Start with one workflow
Define the outcome, controls, and proof before you scale.
20 minutes · one operational process · no obligation