A Hybrid Memory & Routing Layer for Privacy‑First Corporate AI

Membria Enterprise Edition (EE) delivers GPT‑4‑class intelligence on‑device while keeping corporate data private and GPU costs under control. Edge devices run an INT8 Qwen‑2 7B model accelerated by an on-device NPU (e.g., from Intel, AMD, NVIDIA). Instead of slow, per-task fine-tuning, Membria EE uses a proprietary Skill Forge that creates task-specific LoRA patches in seconds. This process is driven by an autonomous Knowledge Curator agent that selects the best examples from the local database. A role‑aware Gateway routes only the most complex queries to the cloud. Result: field staff get realtime, offline, and instantly personalized AI through an Autonomous Personalization Loop; headquarters retain full audit, IP protection, and license control.

Real‑world deployments expose four overlapping bottlenecks that classic cloud‑only LLM solutions cannot simultaneously solve:

A symbolic view of data & compute flow

flowchart TD
    subgraph Edge["Field Device – Offline‑First"]
        Tiny["Tiny Qwen‑2 7B
INT8 on Edge NPU"]
        Graph["Knowledge Core
(SQLite)"]
        Curator["Knowledge Curator
(Example Selector)"]
        SkillForge["Skill Forge
(Parameter Generator)"]

        Tiny -->|read / write| Graph
        Tiny -- "knowledge gap" --> Curator
        Curator -- "finds best examples" --> Graph
        Graph -- "provides examples" --> Curator
        Curator -- "sends curated examples" --> SkillForge
        SkillForge -- "generates" --> LoRA["Δ-LoRA Patch"]
        LoRA --> Tiny
    end

    subgraph Cloud["Private Cluster"]
        GW["Membria Gateway
Auth • RBAC • Licensing"]
        Big["Qwen‑3 235B 4‑bit
4 × RTX 5090 32 GB"]
        Vec["Milvus 2.3 (vector DB)"]
        Ops["ClickHouse
Audit & KPI"]
        GW --> Big
        Big -- "vector‑ids" --> Vec
        Vec --> Big
        GW --> Ops
    end

    Edge -- "mTLS (Query / RAG-Push / License Check)" --> GW

Edge answers ~90 % locally. Personalization is instantaneous and autonomous. Only the most complex queries travel the red mTLS link.

• CPU: AMD Threadripper PRO 7995WX — 96 cores / 192 threads
• RAM: 256–512 GB DDR5 ECC (fits Qwen3 weights + KV)
• GPU: 4 × GeForce RTX 5090 32 GB, NVLink/SLI bridged
• Storage: 2 TB NVMe RAID‑0 (OS + LMCache shards)
• Network: dual 10 GbE + 200 Gbps InfiniBand HDR
• Parallelism: Tensor‑Parallel × Expert‑Parallel (vLLM TP=4, EP=4)
• Capacity: one node ⇒ ≤ 100 concurrent users (@30 tok/s budget)
• Scale‑out: add 1 node, link over 200 Gbps IB; Gateway load‑balances & LMCache sharding auto‑sync.

• Local-first autonomy keeps 90 % of answers offline.
• All new data and logs queue in an encrypted outbox in the local SQLite DB.
• UI shows “Cloud unavailable — best‑effort answer” during connectivity loss.
• The system automatically syncs when connectivity is restored.

This section details the bidirectional data flow that keeps the edge fleet aligned with the central knowledge base while allowing new information to be securely uploaded from the field.

1. Vector clock in device header → Gateway reply tells latest rev per collection.
2. Device computes missing range. If < 20 MB, pulls incremental diff; else requests snapshot.
3. Gateway streams blocks (PUT /sync?id=&rev=) in order; device acks.
4. After full catch‑up, device sends its own queued chunks (POST /push).

This workflow securely uploads new documents and facts created by field workers to the central Milvus database, with full role-based access control enforced by the Gateway. The sync_status flag in the local SQLite chunks table tracks the state of each chunk (local, queued, synced) to ensure reliable, transactional updates.

• Chunk – last‑writer‑wins (higher rev) with audit kept in ClickHouse.
• CoT – immutable; duplicates deduped via hash in Milvus tag.
• Δ‑LoRA – private per device; no merge.

To eliminate the time and resource costs of on-device fine-tuning, Membria EE uses an Autonomous Personalization Loop. This replaces the slow, traditional "distillation-via-training" pipeline with an instantaneous "personalization-via-generation" process. The loop consists of two key proprietary components.

1. The Knowledge Curator: This is an intelligent agent within the Edge SDK. When a knowledge gap is identified (e.g., a failed local query), its job is to automatically search the entire SQLite Knowledge Core. It selects a small, diverse, and representative set of examples that best define the new task or skill the local LLM needs to learn.

2. The Skill Forge (Parameter Generator): This is a pre-trained "hyper-network" model. It takes the curated examples from the Knowledge Curator as input. In a single forward pass that takes seconds, the Skill Forge generates a new, highly specialized Δ-LoRA patch tailored to the new task.

• Massive Time & Energy Savings: Replaces a multi-minute, energy-intensive fine-tuning process with a near-instantaneous one.
• Fully Autonomous: The Knowledge Curator automates the example selection process, removing the need for human intervention to personalize the model.
• Zero-Shot Personalization: The system can adapt to completely new tasks on the fly.

A key challenge in continuous learning is "catastrophic forgetting". The Membria architecture has a multi-layered defense against this:

1. Inherent LoRA Stability: LoRA only modifies a small fraction of the model's weights, leaving the vast knowledge of the base LLM untouched.
2. Dynamic Patch Management: The system maintains a library of task-specific LoRA patches. A lightweight intent classifier in the Edge SDK analyzes each user query and dynamically loads only the relevant LoRA patch for that specific task.
3. External Knowledge Core as a Safety Net: The system's true "memory" is externalized in the SQLite database. Even if a LoRA patch were to degrade the model's ability to recall a fact, the RAG system will still retrieve the correct information from the database and provide it to the model as context.

• Physical Security: Full-disk encryption, TPM-bound keys.
• Data Protection: On-device DB encrypted; model weights protected by NPU secure enclave.
• Remote Revocation: Stolen or non-compliant devices can be remotely wiped or locked out by the Gateway.
• Network Security: End-to-end mTLS 1.3 with certificate pinning.

• Primary ops owner: Membria NOC (24×7) operates Gateway, Milvus, LMCache and ClickHouse in the customer’s private VPC or on‑prem K8s.
• Edge fleet support: Certified SI / device‑OEM partner (e.g. Accenture, Jabil) images NPU-enabled laptops and handles RMA.
• Update pipeline:
  1. Monthly security patch (Gateway container).
  2. Quarterly Qwen & LoRA refresh signed via OTA.
  3. Emergency CVE ≤ 24 h.
• Customer IT is responsible only for network, VPN and account provisioning (LDAP/OIDC).

The Total Cost of Ownership (TCO) is calculated for a deployment scenario of 500 field workers over a 3-year period to provide a transparent view of all capital (CapEx) and operational (OpEx) expenditures.

Membria Enterprise Edition utilizes a per-seat, annual subscription model, enforced by the Gateway acting as a central License Server.

The license covers the proprietary Membria Gateway, the Edge SDK (which includes the Knowledge Curator and Skill Forge), and the Support & Maintenance Contract.

Each licensed device receives a unique cryptographic Device License Certificate from the Gateway upon first activation. The LoRA patches generated by the Skill Forge are automatically watermarked using this certificate. The Edge SDK will refuse to load any LoRA patch with a missing or invalid watermark, thus tying the use of personalized skills to a valid license.

• Milvus: HA via Raft, WAL backups, hourly snapshots (RPO ≤ 60s).
• ClickHouse: 3-replica cluster, incremental backups (RPO ≤ 5 min).
• Gateway: Redundant instances behind a load balancer.

Membria Enterprise Edition empowers organisations with a self-improving, privacy-first AI fabric that scales from cloud to edge. By leveraging a cutting-edge Autonomous Personalization Loop, it delivers instantaneous on-device personalization, ensuring offline autonomy, data sovereignty, and significant cost savings while remaining highly resilient to common continual learning challenges.

The primary value for corporate clients lies in solving four critical, previously irreconcilable challenges of modern AI deployment:

1. True Offline Autonomy: It provides continuous AI support for field workers in environments with unstable or non-existent connectivity.
2. Unyielding Data Sovereignty: By processing sensitive information and personalizing models directly on the device, it guarantees compliance with strict data privacy laws like GDPR, ITAR, and HIPAA.
3. Enhanced Reliability: The hybrid architecture drastically reduces context deficits and knowledge drift, minimizing model hallucinations.
4. Fundamental Economic Shift: Most importantly, Membria EE fundamentally changes the economic model of using frontier AI. It moves enterprises away from the prohibitively high and unpredictable operational expenditure (OpEx) of per-token API calls to a predictable, controlled cost structure. The Total Cost of Ownership analysis shows that for a fleet of 500 workers, the 3-year cost is approximately $1.46 million, representing a cost reduction of up to 85% compared to the estimated ~$10 million for a pure-cloud solution.