9 primitives
The building blocks that power every domain. Same engine for ICM, franchises, rebates, restaurant ops. No custom code per domain.
AI interprets.The engine calculates.Every result, traced.
For the technical evaluator — IT, RevOps, CFO. A deterministic engine, an AI layer with hard boundaries, and a 5-layer drill-down from the final number to the raw transaction.
Swipe →
Anyone can bolt an agentonto old code architecture.The difference is what the AI is allowed to do.At Vialuce, it lives at the core of the architecture, not in the last layer.
Nine structural primitives. One engine. Every domain.
The same 9 primitives that calculate a sales commission calculate a franchise royalty. Domain-agnostic by architecture, not by configuration.
Composable by design
Every new plan, every new rule — assembled from the same nine primitives. Not patched. Not forked.
100% deterministic
Same input always produces the same output. Auditable, reproducible, defensible in front of any CFO.
Verified accuracy
Reconciled line by line against your current system during the Guided Proof of Value. Reproducible across runs.
AI reads the plan; the engine does the math.
Plan interpretation
Reads compensation plans as written. Extracts components, tiers, rates — in any language.
Field mapping
Maps source data to plan fields automatically. Closed-loop learning from corrections.
Anomaly detection
Flags outliers and structural inconsistencies before they reach a payout.
Coaching intelligence
Persona-adapted suggestions — communication style, risk signals, conversation context.
What AI doesn't do
- Calculate anything. The engine does.
- Modify rules at runtime.
- Train on customer data across tenants.
- Operate as a black box — every step is logged.
Any source. No IT team required.
Any source
Excel, ERP, CRM, flat files, APIs. Bring what you have.
Ingest & normalize
Field mapping handled automatically by the AI layer.
T-1 latency
Refreshes near-real-time with prior-business-day information. Not streaming — but not last week's either.
Tenant isolation. Multi-tenant by design.
Tenant isolation
Row-Level Security per tenant. Complete data isolation at the database level.
No data sharing
No raw customer data is shared between tenants, and customer data never trains shared models. Cross-tenant learning uses only structural classification signals (e.g., which headers are dates).
Multi-tenant, live
The architecture is in production today, validated across multiple tenants.
5-layer drill-down on every result.
L1
Final result
What the recipient sees.
L2
Plan/rule component
Which line of the model produced it.
L3
Applied logic
The tiers, thresholds or conditions that fired.
L4
Source data
The normalized records used.
L5
Raw record
The original line, untouched.
“If anyone asks how a number was calculated — a commission, a royalty, a rebate — you can show them. Every step. In minutes.”
Bring your data. See it run.
Same engine. 90–97% cheaper than legacy ICM.
Guided Proof of Value →