Vertiva :: Enterprise Knowledge Platform
Vertiva lets enterprises upload, explore, and query large document collections through a natural-language chat interface — and get citation-backed, traceable answers. The platform runs inside your own cloud environment, giving you self-hosted, governed infrastructure built for regulated industries.
Vertiva :: Overview
The team at Velastegui Ventures architected Vertiva as a product running on a managed cloud platform that owns its data plane, its model selection, and its retrieval — with the governance and compliance a regulated buyer requires. Five tenets organize the platform direction.
Vertiva provides high-throughput ingestion from any source with an enterprise-grade vector store and a highly scalable orchestration layer.
Trace, evaluate, and control cost for every model and retrieval call running in production.
Guardrails, lineage, and audit evidence sufficient to satisfy a regulated enterprise buyer.
VV engineers automate, support, and maintain a fully deployed installation with continued quarterly improvements.
Combines keyword, semantic RAG, and NLQ over unstructured content and structured databases. New feature for 2027.
Vertiva :: Architecture
Every layer sits behind a stable internal interface today, so any future technology improvement can swap the component behind it — a broker, a vector store, an inference engine — without downtime, and with quick adoption by our enterprise customers.
Per-tenant landing zones separate raw connector writes from the platform-managed lake. Prefixes encode org → team → workspace, trust level, and classification — so storage governance and retrieval governance read from the same model.
Embedding model and version are part of index identity — switching embedding models triggers a governed re-embed into a fresh collection, with no downtime. SLM/LLM models are easily changed.
Every question fans across all three methods; results fuse behind one set of citations, so an answer is always traceable back to the documents that produced it.
Tracing, evaluation, and cost control wrap every call out of the box. Token metrics can be provided on workflows, organization, or user queries. Intelligent query caching reduces usage of costly LLM tokens.
Model cards, approval workflows, and per-tenant access clearance govern which models and tools a principal may use, with human-approval gates on high-risk actions.
Vertiva :: Deployment
The team works with your enterprise to carefully analyze your compute environment, data stores, business work models, and critical needs to deploy Vertiva within your managed cloud environment.
Stand up the customer environment under the agreed deployment model — shared multi-tenant SaaS, dedicated single-tenant, or BYOC.
Platform ready to accept enterprise knowledge.
Wire the customer's identity provider via enterprise SSO connections.
Map enterprise teams & users with proper clearance and access to data.
Connect the customer's document sources, map their data-classification taxonomy, and run the initial bulk ingestion.
Validate access controls against real data and prove the data pipeline with quality checks.
Security review and penetration test, data-residency and key-management review, execution of required agreements.
Proves security and audit measures for regulated environments. Runs in parallel with provisioning.
A limited pilot cohort with explicit success criteria, followed by phased expansion to general availability.
Platform moves to production for your enterprise.
Customer-scoped dashboards and service-level objectives, alerting, on-call and escalation runbooks.
Define enterprise admin monitoring and acknowledgements — engagement starts during provisioning.
Runbooks, dashboards, metrics guidance, and customization of Vertiva analytics.
Observability & cost efficiency to make the most out of your enterprise business data on Vertiva.
Vertiva :: Compliance
From day one, the Vertiva platform baseline provides certification evidence on the first chat query rather than being bolted on at project end.