AI Business Intelligence Dashboard

Business monitoring tool that aggregates traffic, channel, and revenue data (in internal validation)

How to quickly see whether this service fits

We surface the key points you need for a first decision.

2 weeks

Best fit for

SMEs 10-50 people

First thing we tackle

2 weeks

Check this first

No data sources at all

Service Overview

A business monitoring tool that aggregates traffic, channel, and revenue data on a single view. Currently in internal validation within STAR-T; external launch is planned. It does not work in all cases — particularly when data sources are unavailable or business goals are unclear.

A business monitoring tool that aggregates traffic, channel, and revenue data on a single view. Currently in internal validation within STAR-T; external launch is planned. It does not work in all cases — particularly when data sources are unavailable or business goals are unclear.

Key Benefits

Data integration support in limited judgment area
AI insight auto-extraction area provision (limited conditions)
Predictive analysis area provision (limited conditions)
Real-time monitoring area provision
Auto report generation area provision (limited conditions)

Process

1

Check structure of data judgment system in operation

2

Explain dashboard design area (limited judgment area)

3

Guide AI insight extraction area

4

Explain predictive analysis model area

5

Guide real-time monitoring and report automation area

Deliverables

Check integrated dashboard structure in operation
Check AI insight extraction system area in operation
Check predictive analysis model area in operation
Check real-time monitoring system area in operation
Check auto report generation tool area in operation

Service Information

⏱️ Implementation Period
2 weeks
👤 Human Resources
4 hours/week
🎯 Suitable Organization
SMEs 10-50 people
📋 Prerequisites
Data sources are available

Self-Diagnosis Checklist

📋 Suitable Cases

  • Have lots of data but can't find insights
  • Don't know key metrics
  • Need predictive model

⚠️ Unsuitable Cases

  • No data sources at all
  • Business goals are unclear
  • Data quality is very low

Design Approach

AI:

Data source integration priority, key metric selection, predictive model selection, insight extraction criteria

⚠️ Human:

Business goal setting, data interpretation and decision-making, data culture building within organization, strategic direction establishment are performed by humans

Not Working:

Does not work when data sources are unavailable or business goals are unclear

Real Implementation Case

Before

15 hours/week to write data analysis reports → Cannot take action

After

Auto report generation → Focus on strategy and execution

⚠️ However, business goal setting and strategy establishment are performed by humans

Verification Results

10
Verified Companies
0
Incidents
Verification Period: 1 year

Recommended Path

Data / Performance Analytics

When data is accumulating but it is unclear what to watch or how to read performance structurally

AI Business Intelligence DashboardDecision Dataset Foundry
Shared service rules

We answer the highest-risk questions before procurement does.

For B2B customers, trust is not a supporting detail. These five rules are the baseline across our service surfaces.

Data scope

We use the minimum information needed for the workflow and explain what enters the system and what is stored.

AI usage boundary

We separate AI-supported steps such as summarization, recommendation, and draft generation from final human judgment.

Human approval points

External delivery, customer response, final submission, and spending-related steps default to human review.

Logs and auditability

Operators should be able to trace what entered, what was suggested, and where the process stopped when something fails.

Access control

We separate operator, reviewer, and admin responsibilities and avoid broad access to internal-only data.

What we lock before launch

  • What data can enter the workflow
  • Which outputs must never go out without review
  • Where the flow stops and who confirms issues
  • What logs operators need to resolve incidents quickly

What you can confirm before talking to us

Data scope

Human approval points

Logs and auditability

Service Information

Project Duration

6-12 weeks

Price

To be discussed

Implementation Period

2 weeks

Human Resources

4 hours/week

Suitable Organization

SMEs 10-50 people

Verification Results

Verified Companies10
Incidents0
Verification Period: 1 year

Main Services

  • Integrated dashboard
  • AI insight auto-extraction
  • Predictive analysis
  • Real-time data monitoring
  • Auto report generation