Orazaka Cognitive Infrastructure

What is cognitive engineering?

Applied cognitive engineering in AI means structuring and organizing the flow of thought of artificial intelligence. Instead of treating a large language model as a black box where you send queries and hope for a good answer, Orazaka divides each interaction into clear, specialized stages. This guarantees data security, factual grounding, and AI predictability.

⚡Moving Beyond Basic Chatbots

Traditional enterprise chatbots send your queries directly to cloud servers without intermediate checks, leading to severe data leakage and hallucination risks. Krizaka designed Orazaka to change this. Orazaka acts as a cognitive engineering copilot: it cleans the query, fetches local memory and knowledge, applies safety checks, and validates the output locally on your machine before delivery. Total sovereignty, fully compliant with Law 25 and GDPR.

FULL COGNITION

The 3 Pillars of Orazaka's Cognition

Inspired by the human brain, Orazaka's engine coordinates 3 major complementary cognitive functions.

Prefrontal Cortex

Perception & Attention

⚙️ Semantic Routing

Orazaka analyzes the underlying intent of your request to filter noise and route it to the optimal local or cloud model.

Hippocampus

Memory & Knowledge

⚙️ RAG & MCP

Orazaka securely connects the AI to your local files and workspace tools in real-time, without ever exposing personal data.

Anterior Cingulate

Metacognition & Reflection

⚙️ Closed-Loop Validation

The AI inspects its own response, self-corrects logic errors, and verifies security policies before displaying the result.

CONTROL ARCHITECTURE

The Cognitive Pipeline: 3 Key Stages

Every user interaction flows through a secure three-step sequence to guarantee a flawless response.

Perception & Ingestion

The query is cleaned, system context variables are injected, and user identity (RBAC) is verified locally.

Reasoning & Context

Orazaka queries long-term context (RAG) and short-term sliding memory, then routes the prompt to the optimal local or cloud model.

Validation & Metacognition

The final output is audited in a closed loop (JSON schema validation, safety rules, execution sandbox) before delivery.

INFERENCE PROCESSOR

Divided Analysis & Action

Orazaka cleanly separates the information accumulation phase from the decision phase, mimicking human thought.

PHASE 01

Stage 1: Ingestion & Enrichment

The engine gathers chat history, vector knowledge (RAG), and system rules to construct a rich context.

Memory

FIFO Chat

Knowledge

RAG / Vector

Rules

RBAC / Env

Enriched Context

TRANSFER

PHASE 02

Stage 2: Reasoning & Safety

The AI reasons in a secure sandbox and validates output compliance before returning the final answer.

Secure Sandbox

Isolated reasoning

Compliance Checks

JSON schema validation

Secure Delivery

Hermetic and certified

OPERATING MODES

Default Operating Modes

Orazaka includes two built-in standard execution modes while allowing full pipeline extensibility.

DEFAULT MODE

Sovereign Mode

Hermetic local execution (via Ollama) ensuring absolute data privacy and direct compliance (Law 25 / GDPR).

DEFAULT MODE

Deterministic Mode

Strict 0.0 temperature, constrained output schemas (JSON), and closed-loop validation to eradicate hallucinations.

ADAPTIVE PARAMETER

Dynamic Effort Adjustment

Effort is not a mode, but an adjustment parameter. The engine analyzes user intent complexity to calibrate reflection tokens and active interceptor depth.

⚡

Low

Direct response

3–5 interceptors

Minimal tokens

⚙️

Medium

Standard analysis

7–10 interceptors

Balanced budget

🧠

High

Deep deliberation

12–15 interceptors

Maximum budget

ACTIVE EXTENSIBILITY

Extensibility & Custom Modes

Assemble Orazaka's 15 interceptors to design tailored modes. Configure custom business rules, auditing, and security topologies.

Choose your interceptors

RAG

Audit

JSON Schema

Memory

Security

Metrics

Routing

Cache

Your custom mode

AuditSentinelle

RAGAuditJSON Schema

Active

Cloud Stateless API vs. Orazaka Reasoning Engine

Simple text generator (Stateless)

Classic Cloud API & Chatbot

RequestRaw prompt

Public CloudExternal API

Raw OutputUnchecked text

Direct linear pipeline

Queries are relayed directly without prior intent analysis or local context gathering.

No secure state or memory

Session history and context are volatile, without local isolation or RBAC controls.

Third-party dependency

Mandatory transmission of raw prompts to external servers (Law 25 & GDPR risks).

Unchecked raw generation

No output verification or format enforcement, causing frequent hallucinations.

Sovereign cognitive infrastructure

Orazaka Reasoning Engine

ContextMemory & RAG

ReasoningLocal LLM

ComplianceJSON Audit

Cognitive interceptor mesh

Queries traverse 9 to 15 modular interceptors to validate intent and refine the prompt.

Local memory & RAG

Immediate access to local vector stores and sliding history windows, keeping metadata secure.

Hermetic on-premise execution

Data remains encrypted inside your private corporate network (native Law 25 & GDPR).

Closed-loop metacognition

Self-correcting audits verify formats (JSON schema) and security policies before delivery.

Ready to get started with cognitive engineering?

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