Local AI & on-premise deployment
Assess data boundaries, access, hardware and support needs to plan a model and data environment the business can manage.
01 / LOCAL AI & MULTI-MODEL INTEGRATION
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 INTEGRATION01 CAPABILITIES
Assess data boundaries, access, hardware and support needs to plan a model and data environment the business can manage.
Connect language, vision / OCR, speech and retrieval models from different sources by task, with routing, orchestration and output validation.
Connect documents, knowledge bases, forms and existing systems, with sources, permissions and human review built in.
02 MODEL INTEGRATION
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.
Set sensitivity, role permissions, inputs and intended outputs.
Use document / vision extraction, speech-to-text, retrieval and language understanding where each fits.
Bring model outputs into existing forms, knowledge bases and business systems.
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
Illustrative workflows for potential co-development, assessed using your actual data and working practices.
Electrical, equipment or renovation teams · Site records and estimating inputs
Organize voice notes, photos and survey records.
Retrieve similar jobs and reference pricing.
Prepare report and estimate drafts, flagging open questions.
A team member checks the draft before updating existing tools.
Internal documents and operating knowledge · Find and reuse information
Identify manuals, documents and recurring questions.
Define access by data type and user role.
Provide references alongside proposed answers.
Let the team validate answers and update knowledge.
Reports, forms and daily paperwork · Connect repetitive tasks across tools
Confirm document types, tables and required fields.
Convert content to a consistent structure.
Identify gaps, conflicts and items needing judgment.
Produce a draft or update the agreed system after review.
These are illustrative concepts. Scope, functionality and outcomes depend on requirements and data assessment.
04 DEPLOYMENT
Choose around data boundaries, hardware, latency and maintenance. Any cloud use in a hybrid setup must follow the company’s data policy.
01 / LOCAL
Run suitable models and knowledge retrieval in the company environment, keeping designated data on premises.
02 / HYBRID
Keep sensitive data and core workflows local; use cloud models only for suitable tasks permitted by data policy.
03 / VALIDATE
Compare quality, latency, resource use and maintenance with representative data, then expand from one workflow.
LET’S BUILD SOMETHING LASTING
Explore Local AI, multi-model integration, or M&A and business succession in Taiwan and Japan.