A New Standard in AI Infrastructure
Seamless context continuity across multiple LLMs with ContextBridge, and MoE-based computation optimization to accelerate enterprise AI adoption.
Get in TouchWhat We Do
ContextBridge
Multi-LLM Orchestration Platform
A B2B API/SaaS platform that manages, converts, and synchronizes shared conversation history and per-model history so that context is never lost when switching between GPT, Gemini, Claude, and other LLMs.
- Shared + per-model conversation history
- Canonical IR-based format conversion
- Session management with collision prevention
- Token usage tracking and billing integration
AI Companion Service
MoE-Optimized AI Agent
Fine-tuned on open-source LLMs (LLaMA, Mistral, etc.) for emotionally intelligent conversations. Uses MoE-based dynamic computation optimization to reduce inference costs while maintaining real-time response performance.
- Proactive conversation system
- Emotion recognition and personality analysis
- Dynamic computation optimization (patent pending)
- Crisis detection system
Our Technology
Cross-Model Conversation Context Management System
Dec 18, 2025
Protects the core ContextBridge architecture — shared history storage, format conversion, and session management — providing a proprietary and validated solution for cross-model interoperability.
Dynamic Computation Optimization for Language Models
Jan 15, 2026
MoE-based dynamic computation optimization technology that will directly reduce inference costs and improve response speed for AI services.
Inference-Efficient MoE via Non-Computational Experts
Mar 20, 2026
A modular optimization technique that extends the gating network of existing MoE models with non-computational experts — preserving the original model while substantially reducing inference cost.
Parameter Updater Expert in Mixture-of-Experts Layer
Mar 30, 2026
A neural architecture embedding a dedicated "updater expert" slot inside the MoE layer that dynamically modifies sibling experts' weights at inference. Enables the model to learn new rules at inference time without a separate training stage.
Publications
Parameter Updater Experts: Inference-Time Learning in MoE Models via DeltaNet-LoRA
Han, Jongyun · Apr 10, 2026
Proposes dedicated expert slots within MoE layers that generate weight modifications during forward passes. DeltaNet-LoRA achieves 100% in-context fact retrieval on OLMoE-1B-7B, and 80.1% persistent retrieval under sliding-window attention.
View on Zenodo → opens in new tab
Open Models
Nemotron-3-Super-64B-A12B-Math-REAP
Apr 22, 2026
REAP-pruned (512 → 256 experts, MTP layer removed) NVIDIA Nemotron-3-Super-120B-A12B, briefly LoRA-RL fine-tuned on AIMO3 + AstralMath-v1, then post-training quantized. Released in three variants for different deployment trade-offs.
AIME 2026 avg@4 — FP8: 0.9167 · AWQ: 0.9083 vs. 0.9000 base (120B).
Variants: BF16 opens in new tab · FP8 opens in new tab · AWQ opens in new tab
View benchmarks on GitHub → opens in new tab
About Us
Max & Omnis is a technology startup developing AI infrastructure software (LLM integration platform) and AI application services. Built on patent-protected core technology, we create AI solutions that deliver value to both enterprises and end users.
Contact
We welcome inquiries about service adoption, technical partnerships, and investment.
madmax0404@maxandomnis.com© 2026 Max & Omnis Inc. All rights reserved.