Hybrid AI Engineering
Hybrid AI and software engineering
Build AI assistants, document processing, business applications, and automation that connect to your existing systems. Test the model, integrations, and access controls before release.
Prototype, test, integrate, and deploy.
How a local-only system works
- 01Use only the sources the employee is allowed to access.
- 02Run the model in the agreed local environment.
- 03Show supporting sources and require approval where needed.
Local-only model execution has no automatic cloud fallback. Storage, backups, logs, and integrations are specified separately.
Build the application your process needs
Custom applications
Business applications, portals, and integrations that replace disconnected manual steps.
Document processing and search
Extract information, search approved records, and answer questions with supporting sources.
Workflow automation
Connect tools and systems, automate routine steps, and add human review where actions require approval.
An answer with its evidence attached
Illustration
An answer with its evidence attached
Illustration
Approved documents
Policies, procedures, and contracts in scope.
Business systems
Records from the connected applications.
Microsoft 365
Mail and files, under the user’s permissions.
Your AI assistant
Drafts from permission-checked context. Runs where the data policy permits.
Answer with sources
Each statement links to the record it came from.
Proposed action
Waits for policy and an authorized approver.
Answers draw only on sources the user is already allowed to see. Consequential action moves through policy and approval.
Connect the systems around the work
Review how information moves, remove duplicate effort, and improve the process. Use AI where it adds value; use conventional software and rules where they are sufficient.
Choose where the system runs
Select local, private cloud, or approved hybrid deployment based on data requirements, accuracy, performance, cost, and resilience. Local-only workloads have no automatic cloud fallback.
Pilot, production, and ongoing development
Validate the assumptions
Private AI Pilot
Test one use case with representative data. Review output quality, cost, and readiness for production.
Explore the engagement →Find the blocker
Private AI Project Recovery
Review a stalled build, identify the causes, and recommend repair, redesign, or replacement.
Explore the engagement →Maintain delivery capacity
Ongoing Engineering
Work through an agreed application, integration, or AI development backlog.
Explore the engagement →Define what must pass before launch
Quality and controls
Test representative cases, failure handling, permitted actions, and approval requirements.
Integration and deployment
Verify integrations, system access, release procedures, and rollback.
Operation and handover
Agree support ownership, operating costs, documentation, and handover.
Questions IT teams ask about the build
Which models does Ixvara use?
The model fits the job. Some workloads use a frontier cloud model, some a smaller specialized model, some local inference, and some no model at all. In a hybrid system the Ixvara AI firewall chooses per request by data sensitivity, quality, and cost.
Is customer data used to train models?
Not by Ixvara. Systems answer from your content at question time, using retrieval against permission-checked sources, instead of training models on it. Each hosted provider’s training and retention terms are reviewed for the workloads routed to it.
Can AI run without sending data to a hosted model?
Yes, when deployed and validated for local operation. Under a local-only policy, prompts, retrieved context, and execution stay inside the defined boundary, with no automatic cloud fallback. Storage, backups, logs, and integrations are specified separately.
How is untrusted content handled?
Retrieved documents and user-supplied content are treated as data, not instructions. Consequential action still requires the policy and approval path regardless of what a document says.
How do we know it works before we commit?
Every engagement defines evaluation criteria up front: accuracy against representative data, cost per unit of work, and the consequence of being wrong. The pilot exists to produce that evidence.
Discuss private AI engineering
Tell us what you want to build, the systems involved, and the requirements.