01 / LOCAL AI & MULTI-MODEL INTEGRATION

Local AI.
Models working together.

We build around Local AI, connecting different AI models with enterprise systems. Language, vision, speech and retrieval models take complementary roles in workflows designed for evaluation and ongoing operation.

LOCAL AI · MULTI-MODEL · SYSTEM INTEGRATION

01 CAPABILITIES

Connect deployment, models
and enterprise workflows.

01

Local AI & on-premise deployment

Assess data boundaries, access, hardware and support needs to plan a model and data environment the business can manage.

LOCAL AI / ON-PREMISE
02

Multi-model integration

Connect language, vision / OCR, speech and retrieval models from different sources by task, with routing, orchestration and output validation.

MULTI-MODEL / ORCHESTRATION
03

Enterprise knowledge & system connections

Connect documents, knowledge bases, forms and existing systems, with sources, permissions and human review built in.

DATA / WORKFLOW / SI

02 MODEL INTEGRATION

Different models.
One connected workflow.

Different models can serve different roles in one workflow. Define task and data boundaries first, then decide what runs locally, how it connects and who reviews it.

  1. 01

    Define tasks & data boundaries

    Set sensitivity, role permissions, inputs and intended outputs.

  2. 02

    Give models complementary roles

    Use document / vision extraction, speech-to-text, retrieval and language understanding where each fits.

  3. 03

    Connect tools & enterprise systems

    Bring model outputs into existing forms, knowledge bases and business systems.

  4. 04

    Evaluate, review & improve

    Measure quality and cost on representative tasks, with records and human approval.

Local models and approved cloud-model APIs can be assessed as needed. Model choice and compatibility require practical testing.

03 WORKFLOW EXAMPLES

Start with one workflow.
Make improvement tangible.

Illustrative workflows for potential co-development, assessed using your actual data and working practices.

Electrical, equipment or renovation teams · Site records and estimating inputs

  1. 01Capture the site

    Organize voice notes, photos and survey records.

  2. 02Find relevant experience

    Retrieve similar jobs and reference pricing.

  3. 03Draft the documents

    Prepare report and estimate drafts, flagging open questions.

  4. 04Review and transfer

    A team member checks the draft before updating existing tools.

INTENDED OUTCOMELess repeated searching and formatting, with human judgment retained.

These are illustrative concepts. Scope, functionality and outcomes depend on requirements and data assessment.

04 DEPLOYMENT

Local at the core.
Deployment shaped by the task.

Choose around data boundaries, hardware, latency and maintenance. Any cloud use in a hybrid setup must follow the company’s data policy.

01 / LOCAL

Local first

Run suitable models and knowledge retrieval in the company environment, keeping designated data on premises.

02 / HYBRID

Hybrid collaboration

Keep sensitive data and core workflows local; use cloud models only for suitable tasks permitted by data policy.

03 / VALIDATE

Validate, then expand

Compare quality, latency, resource use and maintenance with representative data, then expand from one workflow.

LET’S BUILD SOMETHING LASTING

One conversation.
A longer perspective.

Explore Local AI, multi-model integration, or M&A and business succession in Taiwan and Japan.

Get in touch