Zinits Cloud
Shared infrastructure operated by Zinits. We schedule and apply platform updates. This is how PersonalOS and BusinessOS are offered today.
Enterprise integrations
Your operations. Connected. Your AI. On your terms.
Bring business records, documents, communications and AI assistance into connected workflows. Explore enterprise options that give your team a choice of where Zinits runs, where records live and how AI supports the work.
Start with PersonalOS and BusinessOS in controlled beta, or discuss a tailored enterprise pilot. Features marked In development or Planned describe upcoming capabilities.
Deployment
A deployment that fits your business.
Enterprise is a level of service, not a single hosting model. Each enterprise installation keeps its own database, files, configuration and AI runtime state.
Enterprise Cloud (dedicated)
A dedicated deployment on infrastructure isolated for your organization, managed by Zinits under an enterprise agreement.
Enterprise self-hosted
Runs on servers, virtual machines or private infrastructure you control. Your administrator schedules upgrades, with assistance from Zinits. A supported self-hosted offering is in development, and we explore it through consultation and pilots.
Compare the options
Choose the right home for your data and AI.
Match your deployment to the way your organization works. Explore dedicated cloud, infrastructure you control, and local or cloud AI through a tailored enterprise engagement or pilot.
Enterprise Cloud (dedicated)
- Platform runs
- A dedicated installation, managed by Zinits
- Records stored
- Its own database and files, separate from the shared cloud
- AI processing
- Approved AI providers configured for your organization
Focus on your operations while Zinits manages hosting and updates for your dedicated environment.
Enterprise self-hosted
- Platform runs
- Servers, virtual machines or private cloud you control
- Records stored
- On your infrastructure
- AI processing
- Your choice: a cloud provider, a local model, or both
Keep infrastructure and upgrade scheduling in your hands, with deployment assistance from Zinits.
Local or private inference
- Platform runs
- Self-hosted, alongside a model you run
- Records stored
- On your infrastructure
- AI processing
- An approved model endpoint on infrastructure you control
Bring AI processing onto infrastructure you control, with a pilot matched to your model, workloads and capacity.
Hybrid inference
- Platform runs
- Self-hosted
- Records stored
- On your infrastructure
- AI processing
- Some workflows use a local model, others use an approved cloud provider
Combine local and cloud models for different workflows. Model assignments are configured explicitly for your teams and connections, rather than selected automatically from content.
Local storage with cloud inference
- Platform runs
- Self-hosted
- Records stored
- On your infrastructure
- AI processing
- An approved cloud AI provider
Keep records on your infrastructure while using a cloud AI provider. For each AI request, the prompt and selected context, documents or excerpts are sent to that provider for processing.
Looking for a managed starting point? Zinits Cloud offers PersonalOS and BusinessOS in controlled beta, with Zinits-operated storage and workspace-configured AI sources.
Inference location
Control the data path. Choose the processing.
Match AI to the work
Explore local models for workloads you want to process on your infrastructure, cloud providers for hosted AI, or a configured mix of both. Hosting, record storage and inference are separate decisions.
Know where information goes
Your deployment plan maps model requests, document processing, logs, backups and connected services. Self-hosted deployments can still use external sign-in, email and AI services; their data flows are documented with you.
- Choose the model endpoints that fit your workflows.
- Define which teams and connections use each endpoint.
- Review external data flows before rollout.
- Evaluate quality, capacity and support in a focused pilot.
Governed organizational AI
AI access that follows your organization’s structure.
For teams, the question is not just which model to use. It is who may use AI, through which connection, on which records, and how much. We are building these organization-level controls into the next version of the platform.
Roles, teams and staff access
Organize people into teams and give them access that fits their responsibilities, with centrally managed invitations and membership controls.
AI profiles and named connections
Match people and teams with the AI connections they need, and manage individual allowances through dedicated AI access profiles.
Usage visibility
Understand how AI is used across people, teams, models and connections, with centrally enforced allowances.
Agent actions within permissions
Today, the Agent works through tools the application permits for the workspace and role. Actions that require approval stay behind their confirmation step.
Organization roles, AI profiles and named connections are in development for the next release.
What connects
Integrations, organized around the work.
Communications
Supported email and calendar accounts connect through an authorization flow. Reading stays within your permissions, and sending requires confirmation.
Connections guideDocuments and data intake
Files, folders and versions live alongside the records they support. Document and image extraction for workflows such as quote comparison or receipt reporting is still being validated, and each pilot confirms what it covers.
Business modules and custom workflows
Start with existing contacts, projects, jobs, estimates and billing. Connections to your other business systems are assessed for each pilot, and specialized modules are scoped only where a real gap remains.
AI providers and private inference
Workspaces can already use included access or their own provider connection, depending on configuration. For enterprise deployments, connecting an approved model endpoint you operate is scoped and tested with you.
Beyond supported email and calendar connections, we design and build integrations around your systems through a tailored engagement.
Planned: data onboarding
Bring existing data in, without losing the original.
Planned: turn existing files into organized business records with AI-assisted mapping, a reviewable preview and human approval. Keep the original source alongside the prepared data.
Originals are preserved
Uploaded files are kept unchanged, with a record of who uploaded them and when. Processing creates a separate prepared copy, and reprocessing creates a new revision rather than overwriting.
AI proposes, people approve
The Agent suggests how values map to destination modules and fields, and flags duplicates and uncertain values. Permission to upload is separate from permission to write business records. Nothing is imported until an authorized person approves the changes.
Export your data
Planned exports produce portable formats such as CSV and JSON, preserve useful relationships and respect record permissions.
- 1 · Upload: originals stored and checksummed
- 2 · Analyze: content extracted into a prepared copy
- 3 · Map: AI proposes destination fields
- 4 · Validate: deterministic checks and duplicate detection
- 5 · Preview: creates and updates shown against their sources
- 6 · Approve: an authorized person accepts the batch
- 7 · Import: approved operations write the records
- 8 · Reconcile: results traced back to the original files
Planned formats include scans, documents, CSV and spreadsheets, with supported formats and validation defined for each workflow.
Your next step
Start with a workflow. Build toward a connected business.
- 01
Map the workflow and systems
Identify the recurring work, the systems holding the data, who owns the review, and which data the pilot may use.
- 02
Agree the boundaries
Choose the deployment and inference options, the connections needed, the Agent actions allowed and the approval steps, with external data flows written down.
- 03
Pilot, measure, then expand
Run a scoped pilot against agreed acceptance criteria. Measure accuracy, review effort and support needs before deciding what to integrate next.
Start the conversation
Let’s connect the work that matters.
Describe the workflow, the systems involved, and any requirements for where data and AI processing must run. Keep credentials and confidential documents out of the first email.