Intent Classification & NLP Automation
In technical workflows, users don’t always speak the system’s language. That’s where intent classification comes in. I build classifiers that bridge natural language queries and structured commands, enabling conversational automation while maintaining precision and safety.
My Approach
Semantic Embeddings for Classification – Inputs are vectorized and matched against labeled intent sets, providing robust understanding even with phrasing variations.
Thresholding & Abstain Logic – Confidence scoring ensures low-certainty queries are rejected or flagged for review instead of mis-executed.
Command Mappings – Each intent ties directly to predefined actions (e.g., CLI commands), making outputs predictable and auditable.
Safety Controls – Config changes require explicit verification; read-only queries run automatically.
Advancing Further
I continue to evolve methodology toward:
Hierarchical Intent Models – Multi-layer classification that narrows broad categories down to precise actions.
Online Learning – Systems that adapt by incorporating user feedback into future intent mapping.
Cross-Vendor Abstraction – Mapping single intents (e.g., “show neighbors”) into device-specific syntax across Cisco, Juniper, Arista, and others.
Why It Matters
Intent classification turns conversational interfaces into reliable automation layers. By combining semantic embeddings with strict safety gates, I enable natural-language interaction with infrastructure while keeping execution trustworthy, repeatable, and domain-aware.
