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西南智能科技 Xinan Intelligence Technology
ALA / SYSTEM DESIGN

ALA system designConnected for real delivery

Personal memory provides context, permission defines boundaries, and devices connect the experience. This public view explains system roles without exposing internal algorithm parameters, training methods or security implementation.

Design and prototype validation · Delivery in agreed phases

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ALA system design Connected for real delivery
AI-generated concept imagery. Product design, features and performance are subject to project validation and agreed delivery scope.
01 / WORKFLOW

From a user request to an approved result

Every stage has an input, a role and an output, constrained by permission. Public architecture illustration.

  1. 01

    Interaction

    Smart pets, home devices, web or mobile

    Receive voice, text, photos and user actions

  2. 02

    Permission and memory

    Identity, authorized sources and context

    Supply only relevant information the task may access

  3. 03

    Task orchestration

    Understand intent, plan steps and route processing

    Confirm high-impact actions and explain failures

  4. 04

    Results and feedback

    Answers, sources, task receipts and editing

    Feed corrections into the next controlled update

02 / DEPLOYMENT

Devices, local nodes and cloud services

Devices

Devices

Present dialogue and controls with clear microphone, permission and status indicators.

Local nodes

Local nodes

Run retrieval, caching and supported inference to reduce unnecessary data transfer.

Cloud services

Cloud services

Distribute models, extend compute and manage authorized sync and operations, with explicit offline limits.

03 / ARCHITECTURE

Seven-layer architecture

A public design view, not a claim that every feature is live. Models, processing location, targets, retention and supported devices are defined per project.

L1

Infrastructure

GPU, edge compute, vector stores, graph stores, object storage and local nodes.

L2

Foundation

Open-weight models, embedding models, inference services and model routing.

L3

Memory

Semantic memory, temporal knowledge graphs and life narrative structures.

L4

Personality

Explainable preference, value and communication-style modeling.

L5

Cognition

Agent loop, retrieval augmentation, planning and tool orchestration.

L6

Capabilities

Collaboration, future simulation, decision support and digital legacy.

L7

Applications

Terminals, apps, APIs and webhooks with consistent permission controls.

04 / ENGINEERING

Open foundation and proprietary skeleton

01

Open foundation: models, vector databases, graph databases and inference frameworks.

02

Proprietary skeleton: life narrative, personality modeling, context assembly, simulation and sovereignty controls.

03

Engineering rule: explainability, control and portability come before raw intelligence.

How engineering value is validated

Memory quality

Test relevance, source display and corrections with authorized samples

Delivery evidence:Evaluation set and issue log

User control

Test access violations, revocation, mistakes, failure and cancellation

Delivery evidence:Permission matrix and workflow tests

Device continuity

Measure response, sync and fallback on target hardware and networks

Delivery evidence:Device list and integration report

Maintainability

Exercise releases, rollback, logs and incident handling

Delivery evidence:Operations guide and update plan

05 / TRUST

Governance boundaries

Synthetic identity and avatar interactions must be clearly labeled.

The system must not manipulate users into major decisions.

Family data requires separate consent boundaries and minimum collection.

Digital legacy must rely on prior consent and revocable inheritance rules.

Capabilities

Start with one clear use case

Define the people, devices and data boundaries. Together, we scope the first release, acceptance tests and delivery plan.

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