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Solutions

Dynamic AI Routing

Intelligent Multi-LLM routing across 41 algorithms and 7 specialized AI models — from intent parsing to self-evolving orchestration. Sub-50ms with 60% cost savings.

Why Dynamic Routing Matters

Static model assignment wastes resources and limits quality. Dynamic routing ensures every task gets the optimal AI model.

Sub-50ms Routing

Intent classification and model selection in under 50 milliseconds — faster than traditional API gateway routing.

Cost-Optimized

Automatically routes simple queries to cheaper models and complex tasks to premium models — reducing API costs by up to 60%.

Multi-LLM Orchestration

Coordinate multiple LLMs working in parallel or sequence — each handling the subtask it's best suited for.

Self-Evolving

The routing system learns from outcomes and continuously improves model selection accuracy through meta-learning.

9 Tiers of Algorithm Intelligence

41 algorithms organized into 9 tiers — from foundational operations to self-evolving meta-intelligence.

T-S

Supervisor

System-level orchestration and resource management

3
Algo
T-1

Foundation

Core intent parsing and basic NLP operations

4
Algo
T-2

Analysis

Deep semantic analysis and context extraction

5
Algo
T-3

Reasoning

Logical inference and chain-of-thought processing

5
Algo
T-4

Synthesis

Multi-source data integration and synthesis

4
Algo
T-5

Verification

Cross-validation and fact-checking mechanisms

5
Algo
T-6

Optimization

Performance tuning and cost optimization

4
Algo
T-7

Adaptation

Dynamic model selection and routing adaptation

5
Algo
T-8

Evolution

Self-evolving orchestration and meta-learning

6
Algo
41Total Algorithms across 9 Tiers

HexaCore — The 7-Model Roster

3 Western : 3 Eastern : 1 Regional — balanced global intelligence, including Typhoon G38 powered by SCB10X for Thai-language sovereignty.

G1US
Supreme Architect
Claude Opus 4.6
G2CN
Lead Builder
Kimi K2.5
G3CN
Junior Builder
MiniMax M2.1
G4US
Specialist
Gemini 3 Flash
G5US
Librarian
Grok 4.1 Fast
G6CN
Humanizer
DeepSeek V3.2
G38TH
Regional Thai
Typhoon v2 70B

4 Consensus Methods

JITNA selects the voting method based on task criticality — from fast majority polling to SignedAI's 75% unanimous consensus.

MAJORITY

Simple majority — fastest consensus for standard tasks.

WEIGHTED

Models weighted by domain proficiency — specialist gets higher vote share.

RANKED

Models rank each other's outputs — best answer by peer evaluation wins.

UNANIMOUS

75% consensus required — highest trust, slowest, used for critical SignedAI verification.