Unovie helps you own your enterprise context foundation — on your terms, with your data — to power the AI-native initiatives ahead. A domain ontology, a knowledge graph and vector memory become one living model of your world: you can buy the models and the agents, but the context — the boundary that makes you a coherent system — is yours to build, woven from open standards, on-prem. Custom models read your documents, telemetry and records into a typed graph; agents reason over it; your teams query it in plain language. And leaked context — vectors handed to models you do not own — is a business liability and a governance violation.
Your world modelled as typed entities and relationships — the who, what, when, where and why — so context is structured, not guessed.
A graph store and vector memory side by side: subgraph traversal for structure, dense and sparse embeddings for meaning, fused into one answer.
Teams author and query in natural language; domain fine-tuned agents plan, retrieve and act — no SQL, no data team in the loop.
Parse docs, tables and signals.
Build the typed graph.
Embed for graph + vector.
Grounded answers & agents.
Your context is not a feature you bolt on — it is your model of your own world, the boundary that lets your organisation perceive, predict and act as one coherent system. An ontology core, a living knowledge graph, and a membrane of identifiers, rules, meaning and processes that lets the world in without losing what makes you you.
A context you can buy is a context your competitors can buy too.
Leaked context — or vectors — to models you do not own is a business liability. A governance violation.
You can buy the models. You can buy the agents. The context — the boundary that defines you — you build and own, woven from open standards, on your terms. Outsource it, and you become a component in a system someone else defines.
Document- and table-structure models parse PDFs, images and text; an LLM emits structured records that become typed nodes and edges.
Records are de-duplicated, transformed and persisted to a fast embeddable graph store, with embedding models writing vectors beside every node.
Traditional indices, text and sparse embeddings and subgraph traversal feed a tensor-based fused ranker and query-rewrite models — grounded Top-K, not guesses.
Graph OLTP keeps every node and edge temporally versioned with an immutable audit trail; columnar OLAP serves analytics — multi-tenant, OAuth2 / RBAC, zero-trust.
Document-parsing, embedding, NER and query-rewrite models, plus domain fine-tuned SLMs — trained on your domain, swappable and multi-model routed.
Pre-built agents — diagnostics, differential, referral, next-best-action and triage — reason over the graph and call tools to act.
Self-service portals let users ask, author and govern in plain language across chat, voice and app — every answer traceable to source.
Turnkey Edge-AI — fixed time, fixed cost, full responsibility.
