Engineered for Scale

Memory & Feedback Engine

Provides a persistent enterprise memory. It captures context across sessions and projects, storing it in an auditable, editable knowledge layer. Every approval, correction, or supervisory input feeds back into this memory, creating a system that not only recalls information but learns from its use.

	{
		"memory": { 
			"type": "vector+kv", 
			"retention_days": 90, 
			"pii_redaction": true 
		},
		"feedback_loop": { 
			"learn_from": ["approvals","corrections","ratings"], 
			"update_schedule": "hourly" 
		}
	}

Orchestration Layer

Functions as a multi-agent backbone that breaks down complex tasks into smaller objectives, validates outputs, and adapts routing decisions based on past performance and live conditions. Its modular design enables seamless integration with APIs, databases, RPA tools, and legacy systems, while maintaining human-in-the-loop oversight where essential.

	pipeline:
		entrypoint: generate_summary
		agents:
			- id: reader     # pdf.parse, html.scrape
			- id: drafter    # llm.write, kb.search
			- id: checker    # citation.verify, policy.guard
		edges:
			- { from: reader,  to: drafter }
			- { from: drafter, to: checker }

Knowledge Integration

Transforms retrieval into a living process. Instead of relying on static embeddings, it uses anchored chunking for traceability, re-ranks results based on actual business outcomes, and continuously retrains itself on accepted outputs. This means the longer a NeuralHue framework runs, the more accurate and contextually aligned its knowledge base becomes.

	connectors:
		- { name: openai,    type: llm,       auth: env:OPENAI_API_KEY }
		- { name: atlas,     type: vector_db, uri: mongodb+srv://... }
		- { name: perplexity,type: web_search, rate_limit_per_min: 30 }

Governance & Alignment

Completes the architecture. Compliance and trust are embedded into the system itself through maker-checker approvals, automated bias detection, and role-based policy enforcement. With real-time dashboards and regulator-ready audit trails, governance is not a checkbox — it is a productised capability.

	governance:
		checks:
			- { name: citation_coverage, min_ratio: 0.8 }
			- { name: pii_redaction, required: true }
			- { name: hallucination_guard, model: factcheck-v1 }
		approvals:
			roles: [maker, checker]
			thresholds: { maker: 0.7, checker: 0.9 }