Korea Platform
SystemContact

AI that understands data
and keeps work moving.

Starting from our experience in video and data analytics,
we design ways to use AI that fit your organization’s context.

Video & data: delivered work Language AI: proposed

Discuss AI adoptionView use scenarios

We bring our analytics experienceto the next stage of AI.

Building on our experience developing video analytics, data analytics, and prediction AI, we propose the next step: work environments that use language models.

Delivered work
Example video analysis resultPaths · Dwell time · Zones of interest

AI video processing & analysis

We recognize objects and situations in video and turn the information you need into a basis for service decisions.

Delivered work
VideoSensorsBusiness DB
Processed outputService datasetCleanup · Verification · Connection

Data analysis & processing

We collect scattered data and process it to fit the purpose, turning it into readable information and structures that can be connected.

  • AI data platformWe developed an AI data platform in 2018.
  • Data Voucher supplierDesignated as a supplier in 2021 (as of the date designated).
Delivered work

Prediction & IoT AI

We developed AI that uses facility, sensor, and vital-sign data to predict facility output and hazards.

  • Fire prevention and hazard prediction AI for industrial facilitiesDeveloping an AI facility management system for fire prevention and hazard prediction, and an equipment control (PLC) system
  • Facility output forecasting AIDeveloping an AI algorithm for output forecasting
  • Wearable for predicting infant safety risks (IoT)Developing a wearable device that collects vital-sign data and predicts risky situations, and a vital-sign monitoring application

Toward AI that understands language and context

LLMs, RAG, and AI agents are proposed use cases. We first check your organization’s data and security environment, then validate what’s possible on a small scale.

View the three approaches

Our role and the key technologies in each video, data, and prediction AI project are listed on our project experience page.View projects by industry

We shape AI into three approachesto fit your business challenges.

Enterprise knowledge search (RAG) and document & workflow automation are proposed use cases. Video AI & data analytics builds on projects KPS has delivered itself.

Three use scenariosin everyday work

These proposed scenarios place enterprise knowledge search (RAG) and document & workflow automation in real work situations. We check your current work and data environment and start with a scope that fits.

Proposed scenarioEnterprise knowledge search (RAG)

AI that finds and answers
from internal knowledge

We propose a knowledge search environment that connects work documents and rules, understands the context of a question, and answers with sources.

  • Unified search across documents and rules
  • Answers built on sources
  • Access based on permissions
View the build scope for this scenario
Example screen: Internal knowledge Q&A Sample data
Work request

Find the security check items in last quarter’s operating guidelines.

Organization data checkAI analysisHuman review
Answer with sources

Security checks follow this order: confirm access permissions, review the change history, and record anomalies.

  • Operating guidelines · Security checks
  • Access permission management standards
  • Change history review procedure
Discuss AI adoption

Tell us about your current work and data environment, and we’ll work out a feasible starting point with you.

Start a consultation

We connect AI securelyto existing systems.

We design everything from service screens to data, AI models, security, and operations as one structure. The setup can vary with your organization’s environment.

Example architecture
Example architecture for an enterprise AI serviceA structure where the services people use for work connect to enterprise data and AI models through an AI orchestration layer, with security and operations management applied throughoutUSERBusiness usersSERVICEWeb & systemsMODELLLMs · AI modelsDATADocs · DB · APIAI ORCHESTRATIONLinks questions, tools, and workflowsKNOWLEDGE & INTEGRATIONLinks knowledge and business systemsSECURITY · ACCESS CONTROL · MONITORING · HUMAN REVIEW
Business users · Services
AI orchestrationLinks questions, tools, and workflows

Enterprise data

AI models

Security · Access control · Monitoring · Human review

We validate small,then let the results set the next step.

LLM and RAG adoption starts with a pilot (proof of concept, PoC) of limited scope. We set the evaluation criteria first and use the same criteria to decide whether to scale up.

  1. Question selection

    From recurring questions or document tasks, we choose a scope whose results are easy to check.

  2. Evaluation set

    We first build the criteria by pairing representative questions with expected results and source documents.

  3. Limited rollout

    We connect it for a defined set of users and data, including permissions and review procedures.

  4. Scale-up decision

    We look at the evaluation results together with the operating workload to decide the next step.

    • Scale up
    • Refine
    • Stop

Our guide explains how to set the scope and evaluation criteria for a PoC.

First AI project: start with a small pilot

Let’s start by exploring what AI can do
for your organization.

Tell us about your work and data environment, and we’ll design a realistic starting point with you.

Discuss AI adoption

or+82-2-830-7721kps@openkps.com