Self-driving AI doesn’t belong in finance. Numen is built so agents execute fast and humans verify — Vertical Finance Agentic AI.
While other AI tools ask “what more can AI do?”, we asked “what can humans still see?”
A DB schema stores data. The Numen Ontology models your finance organization as a digital twin — accounts, BUs, entities, KPIs, and rulebooks living as objects with relations in one operational knowledge graph. On this semantic layer, the LLM stops describing your data and starts driving decisions.
Understands close cycles, fluent in IFRS, US GAAP and local GAAPs, knows the artifacts finance teams actually produce. Domain-grounded from the first line of code.
Ask Numen “why?” — it doesn’t generate an explanation, it shows the steps. Every answer carries data sources, formulas, and assumptions.
Every output lands on the canvas — where finance teams review, edit, and approve. Nothing moves without sign-off.
Upload one GL file, AI standardizes it in 30 seconds. No IT team, no consultants. (Enterprise scale uses a separate AX track.)
Existing tools keep data, analysis, and execution in separate silos. Numen is designed so the three are auto-linked through AI.
| Legacy ERPSAP, Oracle | Consulting SaaSAnaplan, Pigment | LLM toolsChatGPT, Copilot | NumenVertical Finance Agentic AI | |
|---|---|---|---|---|
| Time to value | 6–12 months | 2–3 months | Instant | Instant |
| Consulting cost | $1M+ | $100K+ | 0 | 0 |
| Finance domain depth | △ | ✓ | ✗ | ✓Vertical |
| Auto data standardization | ✗ | △ | ✗ | ✓ |
| AI Ontology (Semantic Layer) | DB schema only | Hand-modeled | None | ✓ |
| Learns your company | ✗ | ok (slow) | ✗ | ok (auto) |
| Auditable | ✓ | ✓ | ✗ | ✓ |
| Human-in-the-loop | — | — | Self-driving | Verify-first |
| Self-serve | ✗ | ✗ | ✓ | ✓ |
| Starting cost | $1M+ | $100K+ | Free | From $499 / mo |
The Manifesto, organized by role. Read just the card that applies to you.