Product
Not AI-Enhanced.
AI-Native.
Traditional ERPs bolt AI onto existing systems. Uno360 was built from the ground up with LLM intelligence as the execution substrate. If the AI doesn't work, the architecture must be reconsidered. That's the bet.
Business rules stored as English.
Evaluated by AI at runtime.
Traditional ERP
{
"field": "amount",
"operator": "gt",
"value": 10000,
"approver_role": "VP"
}Lossy translation. Can't express "unusual" or "significant."
Uno360
VP approves marketing spend over $25K, except recurring vendors with 12+ months history
Stored verbatim. Full nuance preserved.
Four paths. One engine.
Always the fastest route.
| Path | Example | Speed |
|---|---|---|
| Structured fast path | amount > $10,000, department = Marketing | Milliseconds |
| LLM complex path | "Unusual for this vendor", "significant deviation" | Sub-second |
| Cached LLM decisions | Same context seen before | Milliseconds |
| Pattern graduation | Frequent LLM rules converted to structured | Milliseconds |
The system learns which rules can be compiled down to structured code over time — a self-optimizing loop.
The AI knows when it doesn't know.
High
>85%
Auto-execute
Decision proceeds without human intervention. Full explanation logged.
Medium
60–85%
Execute + flag for review
Action taken but queued for human review with reasoning attached.
Low
<60%
Route to human
System presents analysis and options but defers the final call entirely.
Every decision produces three outputs: decision + explanation + confidence score. In financial software, unexplainable decisions are unacceptable.
Gets smarter every month.
Literally.
Every user correction captured with full context → accuracy improves
LLM decisions cached, patterns extracted → repeat decisions served instantly
Frequent LLM rules graduated to structured rules → system gets faster
ML models retrain on growing transaction history → predictions sharpen
Embedding quality improves as more financial data indexed → search gets precise
Target: >2% accuracy improvement per month.
A 2-year-old deployment is fundamentally better than a fresh install.
5 layers of safeguards.
LLM-native doesn't mean LLM-reckless.
Templates
Structured prompts prevent hallucination
Validation
Output checked against business rules
Simulation
Test before going live
Runtime Monitoring
Continuous confidence tracking
Audit
Every decision logged with full context