UniVerse by Trinamix
The universal business object layer for enterprise AI.
UniVerse connects enterprise systems, translates their data into a shared business object model, and provides a semantic foundation for AI, analytics, and workflows in weeks, not years.Â
How the UniVerse Works
Connect. Unify. Act.
Three capabilities, one continuous loop, turning fragmented enterprise systems into a universal business layer for analytics, AI, and action.
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Connect every system, document, and signal, without rip and replace.
Bring every enterprise source into one foundation
UniVerse connects ERP, CRM, supply chain, documents, external data, and operational signals without requiring organizations to replace the systems they already run. It creates a unified data foundation while preserving the source systems that power the business.
A universal business object layer across every source system
UniVerse translates source-specific data, transactions, and business context into shared business objects and relationships. That universal layer becomes the semantic foundation for analytics, decision intelligence, AI agents, and automation.
Example business objects
- Customer
- Supplier
- Item
- Order
- Shipment
- Invoice
- Asset
- Location
Turn business context into outcomes
With a shared semantic layer in place, UniVerse powers self-service analytics, decision intelligence, AI copilots, agents, and workflow automation so teams can move from insight to action across systems with trusted business context.
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Key Capabilities
Built for the modern enterprise. Ready for AI.
The architecture beneath the fabric. Built for the teams who will run it.
Connect to the systems you already run, ERP, SCM, CRM, the data warehouse, documents.
Webhook, CDC and batch ingestion; connectors run inside your network; existing sources stay intact.
Stand up on the stack you already own, no rip and replace, live in weeks.
A canonical model plus a governed knowledge graph of entities, relationships and meaning.
Business glossary, governed metrics and relationship mapping that ground every query.
AI reasons with real context, trustworthy answers, not hallucinations
Tool calling agents that answer, recommend and execute, composed into multi step workflows that span systems and humans.
No code agent builder, orchestrated tool calling, stateful orchestration, retries, human in the loop, branching logic, scheduling and approvals.
Automate the work between insight and action, without an army of engineers; close the loop from detection to execution.
Write decisions, updates and actions back into your source systems automatically.
Closed loop write back into ERP, CRM, SCM and warehouse systems; bidirectional sync with conflict resolution.
Decisions that execute, not dashboards that sit; less time between insight and action.
Runs on OCI, AWS or Azure with the LLM you choose.
Cloud agnostic, AI native deployment; your data never leaves your environment.
No migration, no lock in; faster security and procurement approval.
How UniVerse Differentiates
Same enterprise. Three approaches to AI.
Most enterprise AI starts with the same systems, data, and business objects. The difference is what each approach preserves: isolated workflows, disconnected data, or shared business context that every model, workflow, and agent can use.
Workflow automation in silos
Each agent can automate a task or workflow, but the business context stays trapped inside that agent. Connections are built point to point, reused inconsistently, and break at system boundaries.
Unified data, limited business meaning
Warehouses centralize data at scale, but relationships between business objects are usually structural, not semantic. Teams still have to rebuild context, logic, and joins across every use case.
A shared enterprise intelligence layer
UniVerse connects customers, orders, suppliers, contracts, inventory, products, invoices, and risk into a reusable semantic layer with named relationships and business context. Every AI workflow, decision flow, and application inherits the same enterprise understanding.
Outcomes by Function
Same fabric.Different leverage for every leader.
The question
Can you scale trustworthy AI across the enterprise, without a multi year platform build?
- Platform consolidation
- Cross ERP unification
- Enterprise AI foundation
- AI governance
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Challenge today
A stalled AI or platform program; pressure to consolidate; rising cost of fragmentation.
Business outcome
One intelligence layer over your enterprise systems that runs on infrastructure you already own and enables trustworthy AI in weeks, not a multi year platform build.
The question
Can you see supply chain risk before it costs a shipment?
- Supplier risk detection
- Inventory rebalancing
- Inventory rebalancing
- Forecast write back
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Challenge today
Risk and imbalances surface too late; OTIF decisions mean stitching data manually and every analysis is an IT ticket.
Business outcome
Capture supplier and inventory risk early and act on it, for higher OTIF and planner led decisions, without relying on IT.
The question
How do you deliver trustworthy AI without rebuilding integrations every time?
- Cross ERP unification
- Golden records
- Self serve analytics
- No code agents
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Challenge today
Every AI initiative is a custom integration project; agents built one at a time; AI hallucinates; the report backlog never clears.
Business outcome
One governed foundation for every AI use case, trustworthy, traceable, and live in weeks on the infrastructure you already own.
The question
Do you know which accounts are at risk before they churn?
- Customer 360
- Account risk analysis
- QBR prep
- Collections outreach
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Challenge today
Customer data is scattered across ERP, CRM, support, and billing; a 360 view takes days; risk signals surface too late.
Business outcome
A complete customer 360 in seconds that flags at risk and concentrated accounts early, so you retain and expand proactively.
Trust & Governance
Trustworthy AI starts with Trustworthy Context.
Every answer is grounded, governed, and traceable to its source. Trust is the foundation , not an afterthought, and UniVerse runs on the infrastructure you already own.
Governance | Governed metrics and definitions keep answers consistent. |
Lineage | Full field level provenance, every answer traces to its source. |
Security | Runs in your own network/cloud; your data stays yours. |
Access controls | Role based access across the fabric. |
Explainability | Answers show the ‘why’ and the sources behind them. |
Auditability | Agent run history and ingestion ledger record what happened and when. |
