Enterprise Knowledge AI
Private language infrastructure for internal knowledge workflows
Deploy the model serving and retrieval behind internal search, summarization, extraction, and assistants without sending company knowledge to a public LLM API by default.
Typical workflow
01private LLM inference
02embeddings and reranking where needed
03vector-search integration
04document-ingestion interfaces
05identity-aware retrieval architecture
06API gateway and application integration
07evaluation, benchmarks, observability, and operations
Workflow
Where Izwi fits
Izwi provides the voice and language infrastructure beneath the workflow, deployed where your application and data need it.
- private LLM inference
- embeddings and reranking where needed
- vector-search integration
- document-ingestion interfaces
- identity-aware retrieval architecture
- API gateway and application integration
- evaluation, benchmarks, observability, and operations
Why run it privately
Keep processing close to your data and application
A private deployment gives your team more control when sensitive data, latency, limited connectivity, customer infrastructure, or internal policy rules out a permanent dependency on hosted AI APIs.
View LLM Deployment →What Izwi provides
Clear ownership across the complete workflow
Izwi builds and integrates the private model-serving and retrieval layer. Your team retains control of source permissions, document governance, information architecture, user experience, and business processes, or can ask us to include specific integration work in the engagement.
Bring the workflow and target environment
We’ll help you choose the most useful first step: local evaluation, an assessment, a paid pilot, or production deployment.