Now Live: Kutup NQ
Neural Query is the first production app, focused on industrial automation, IT/OT, ML, protocols, and data-system fundamentals with evidence-aware output.
One mission: purpose-built AI for industrial reality.
Kutup is developed by ASP Dijital as a structured ecosystem of specialized tools. The current production engine, Kutup NQ, is a domain-constrained industrial assistant that combines curated datasets, retrieval pipelines, and guided model synthesis to answer technical questions with visible evidence.
6 focused tools planned
Kutup NQ v1.4 (Beta)
Curated knowledge + live sources
Core Ideology
Building principles that guide every tool in the ecosystem.
One domain, one product
Each tool is purpose-scoped so depth is never sacrificed for breadth.
Curated over generated
Answers are grounded in maintained datasets first, then expanded through controlled Wikipedia and RSS evidence.
Bilingual by design
Turkish and English are both first-class from architecture to interface, including cross-language search expansion.
Inspectable by default
NQ exposes source context, tool usage, and evidence panels so engineers can see how an answer was assembled.
Ecosystem Narrative
Kutup is built incrementally: launch one reliable tool, validate it in production, then expand.
How NQ Works
NQ routes a query through custom datasets, browser-based ML signals, Qwen3.5-9B synthesis, and selective Wikipedia/RSS enrichment when additional context is needed.
Operational Direction
Modbus LLM, OPC LLM, PLC LLM, Flow Maker, and Kutup Forge extend the platform from technical Q&A into deterministic execution workflows.
Build Principles
The same architecture and UX principles apply across every Kutup application, with NQ as the reference implementation.
Scope boundaries first
NQ rejects irrelevant prompts and only accepts supported English and Turkish industrial-domain requests.
Data curation pipeline
Custom JSON datasets are curated and versioned, then expanded with bilingual retrieval paths to improve technical recall depth.
Production UX for engineers
Session history, thinking states, used-tool chips, and inspectable evidence panels keep the answer flow visible instead of opaque.
Hybrid synthesis layer
Qwen3.5-9B is used as the primary synthesis model, while browser-side ML helps ranking and interaction quality on the frontend.