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Input
Raw requests enter with context, identifiers, documents or structured records.
Teknikal Seni Bina Paper
NeuralOps Seni Bina
This paper explains how AINNA routes work through parsing, AI, deterministic validation and action layers. The goal is not to send everything to the largest model, but to keep each step visible, auditable and fit for purpose.
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Penghalaan
Smart routing selects the cheapest safe path before the LLM is used.
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Specialised Parser
Parsers turn messy inputs into versioned structures that can be validated.
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AI Decision Point
The model handles language-heavy or reasoning-heavy work when rules are not enough.
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Sistem Berasingan
Deterministic business logik executes outside the model to keep repeatable work auditable.
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Validation
Rule checks, schema checks and business constraints stop unsafe outputs.
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Tindakan
Luluskand actions move to downstream systems, workflows or human review.
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Logging
Every step leaves an audit trail for review, debugging and governance.
Visible explanation
| Layer | Peranan | Why it matters |
|---|---|---|
| Input | Capture user request and operational context. | Reduces ambiguity before the model is involved. |
| Penghalaan | Choose parser, rules or model path. | Avoids unnecessary model calls. |
| Validation | Check schema, logik and business rules. | Stops invalid outputs from moving downstream. |
| Tindakan | Trigger workflow, report or human review. | Keeps human authority where required. |
| Logging | Store traceable evidence of each step. | Supports auditability and debugging. |
AINNA's implementation is intentionally practical: input first, routing second, model only when useful, and deterministic checks before action.
Interpretation
NeuralOps is AINNA's operating pattern for smart routing, detached execution and accountable AI-assisted work. It is not a claim that every problem should be solved without a model; it is a claim that the model should not be the only control point.
Limitations
- Kompleks reasoning still needs a model.
- Rules must be maintained as operations change.
- Log prove process sejarah, not perfect correctness.
- Manusia review remains necessary for high-risk actions.
Related technology
NeuralOps
Main product architecture and service overview.
Sistem Berasingan
Why deterministic logik is separated from the LLM.
Citation information
Suggested citation: AINNA. "NeuralOps Seni Bina." AINNA Penyelidikan, 2026. Canonical URL: https://ainna.bond/research/neuralops-architecture/
