AINNA NeuralOps LLM Strategies

Smart Routing · Detached Systems · AI Builder for Phase 1 & 2

7-model orchestration 87% token savings Detached systems Phase 1 & 2 delivery

Investor takeaway

Multi-model routing is not a tech flex it is a margin lever. Local 7-model orchestration avoids per-token API fees on local workloads, reduces unnecessary GPU usage, and enables more predictable unit economics at SME scale.

87% token savings benchmark → Pitch unit economics →

CURRENT — Production Smart Routing, Detached Systems, and 7-model orchestration are live for suitable workloads. 87% token reduction is an internal benchmark.

ROADMAP — Ambition Task segmentation, advanced parser engines, and deeper autonomous optimization are planned for Phase 2.

See the Efficiency Flywheel and Model Distillation pillars for how these layers compound.

Phase 1 & 2

Smart Routing

Intelligent request distribution across multiple LLM providers routes queries to the optimal model based on task complexity, latency requirements, and cost efficiency. Ensures high availability and fallback resilience.

Phase 1

Detached Systems

Independent, isolated AI subsystems that operate autonomously without cross-contamination. Each system manages its own model lifecycle, data pipeline, and execution context enabling parallel processing and fault isolation.

Phase 1 & 2

AI Builder Generic Agent AI

Generic Agent AI serves as the core AI builder orchestrating both phases. Phase 1 establishes foundational detached AI agents and routing infrastructure. Phase 2 scales with advanced model orchestration, multi-provider load balancing, and autonomous system optimization.

Planned Integration

Task Segmentation

Planned layer for decomposing complex requests into discrete subtasks before routing. Latency and throughput effects will require validation during implementation.

Planned Integration

Parser Engine

Proposed adapter layer for supported bank-statement formats before model routing. Additional document families require dedicated parsers and validation fixtures.

7 Model Roles (Current production routing — suitable workloads)

Qwen

Qwen3-14B

DeepSeek R1

DeepSeek-R1-Distill-Qwen-14B

GLM

GLM-4.5-Air

Gemma

Gemma-3-12B-IT

Llama

Llama-4-Scout-17B-16E-Instruct

Mistral

Mistral-Small-3.2-24B-Instruct-2506

Kimi K2

Kimi-K2.6

Model Intelligence

Our NeuralOps Inhouse Asset

Auto-cycling evaluation of our sovereign LLM orchestra spider chart across 6 weighted dimensions.

Currently Evaluating Qwen auto-cycling...

NeuralOps Asset Score

0 / 100

Final Score = Accuracy × 0.25 + Task Fit × 0.20 + Speed × 0.15 + Cost × 0.15 + Safety × 0.15 + Low Edit × 0.10

Architecture Flow

Request

Neural Router

Selected Model

Detached System

Business Execution

AINNA Neural Router selects the right model based on task type. The selected model helps build or improve the system. The detached system then runs the business process independently.