Deployment confidence
A documented architecture and deployment process designed for the customer's infrastructure and security boundary.
Enterprise Private AI
Private AI with packaging, controls, and an accountable operating model
Izwi helps enterprises and software vendors move private voice and language AI from evaluation into supported production. We package the runtime, integrations, controls, documentation, and operating model around your environment.
Enterprise delivery
Move private AI into production.
Architecture, versioned releases, operational visibility, documentation, rollout guidance, and support.
What changes in production
More than access to a model
Approved versions
Pinned runtime, container, dependency, and model versions with controlled release and rollback guidance.
Operational visibility
Health, latency, throughput, error, and resource telemetry integrated into the agreed operating model.
Accountable support
Defined ownership, escalation, maintenance, and incident processes rather than an unsupported open-source stack.
Enterprise capabilities
The controls and support your deployment needs
We select the capabilities that fit your product, infrastructure, security review, and operating model.
- supported runtime builds or container images
- approved model bundles or model-artifact process
- version pinning and release notes
- deployment and rollback guidance
- infrastructure automation or manifests
- health checks and observability hooks
- network, identity, and secrets integration guidance
- security and data-flow documentation
- runbooks and knowledge transfer
- support terms according to contract
- managed operations as an optional recurring service
Two enterprise tracks
Voice product depth and practical LLM infrastructure
Product track
Enterprise Voice AI Runtime
Production packaging and support for private speech recognition, text-to-speech, diarization, and voice workflows.
Explore Voice AI Runtime →Service track
Private LLM Deployment
Model evaluation, infrastructure design, serving, integration, observability, optimization, and handover.
Explore LLM Deployment →Security and compliance review
Give reviewers the evidence they need
We can document architecture, data flows, configuration, deployment, model versions, and operations for your review process.
Keeping processing in an environment you govern can support security, privacy, and data-residency requirements. Final compliance depends on the complete system, your operating controls, and a customer-specific review.
Paid production pilot
One workload. One environment. Written success criteria.
Use realistic infrastructure, permitted data, measurable quality and performance targets, and a written recommendation for production.
01
validated workload fit
02
benchmark and capacity findings
03
architecture and risk findings
04
written production recommendation
Ready to move a private AI workload into production?
Tell us what needs to run, where it will run, and what is blocking deployment today.