Solution · Data you can trust + dashboards

Make the company's numbers trustworthy, before you decide anything with them

The reason most companies still have no dashboard is not the tools. It is that the data sits in pieces, spelled differently in each place, and every department defines “sales” its own way. This bundle fixes that first — everything pulled into one place, cleaned up, one agreed definition per number — and only then opens a screen anyone can use.

Data from every system in one place, updating itself Settle once and for all what each number means Clean duplicates and malformed records before they reach a report A screen anyone can open themselves, with nobody to wait for Keep the history your source systems throw away Know the moment data starts drifting, before the report is wrong

A free 30-minute call

Work in

Data you can trust + dashboards+ human approval

To the customer

A person approves before it ever reaches the customer· drag to rotate

A fit if you…

Companies where looking at a number means waiting for someone to pull files out of 3–4 systems into Excel first
Teams that keep arguing over whose number is right, because sales and accounting count revenue differently
Businesses that tried a dashboard once and nobody used it, because the numbers on it did not match what they knew

We usually start with

We usually start with Data Hub, because everything else has to wait for the data to land in one place, and it shows results fastest — once it is together, the hidden problems surface on their own within the first week. If you are not sure what state your data is in, a small 1–2 week audit to map where each number lives and how far it can be trusted is a good first step. Start with a free 30-minute call.

This bundle has 6 agents. You don’t have to switch them all on at once — start with one, see the result, then add the rest one at a time.

item 1 / 6 · Data from every system in one place, updating itself

In use in 3–4 weeks (depending on how many systems)

01 · Data Hub

Data from every system in one place, updating itself

In use in 3–4 weeks (depending on how many systems)

Work arrivesPOS / ERPCustomer / system

The AI agent does itData HubOne central store holding every system, on the refresh cycle you choose

A person decidesApprove in one clickWe never touch or change anything in the source systems, and you decide which sets of data get pulled.

Result0 hrsTime spent assembling data before you can look at it

Example: Data from every system in one place, updating itself

LIVE DEMO

1 · Work arrives

Sales sit in the POS, stock in another system, the books in the accounting software, customers in LINE and several Excel files. Pulling it together needs a person every single time.

2 · The AI agent does it

One central store holding every system, on the refresh cycle you choose
A data map showing which system and which table each number comes from
Alerts when a pull fails or brings back unusually little data
Read-only from the source systems, nothing written back

3 · You just approve

We never touch or change anything in the source systems, and you decide which sets of data get pulled.

Approve in one click

Result

0 hrs

Time spent assembling data before you can look at it

We connect to the systems you already use and copy the data into one place on a schedule you set — hourly, nightly, whatever fits — without touching the source. What comes in is shaped the same way and linked by product and customer codes, and if a pull fails you get an alert straight away, so numbers never sit there quietly out of date.

Connects to

POS / ERPAccounting softwareGoogle SheetsMarketplaces / LINE OA

0 hrs

Time spent assembling data before you can look at it · From the work we have done, teams were spending half a day to a full day a week just merging files.

02 · Metric Dictionary

Settle once and for all what each number means

In use in 2–3 weeks

Work arrivesEvery system connected in Data HubCustomer / system

The AI agent does itMetric DictionaryA metric dictionary in plain language, next to the formula the system really uses

A person decidesApprove in one clickThe shared definition is your decision and your department heads'. We put the facts on the table; we do not decide for you.

ResultgoneTime lost arguing over whose number is right

Example: Settle once and for all what each number means

LIVE DEMO

1 · Work arrives

Sales says five million this month, accounting says 4.2. Neither is wrong — one counts at the purchase order, the other when the money lands. So every meeting starts with an argument over whose number is right.

2 · The AI agent does it

A metric dictionary in plain language, next to the formula the system really uses
A comparison table showing where each department's old definition differed
Every number on screen clickable to see which definition it uses
A review cycle when the business changes, not a document written once and forgotten

3 · You just approve

The shared definition is your decision and your department heads'. We put the facts on the table; we do not decide for you.

Approve in one click

Result

gone

Time lost arguing over whose number is right

We collect the definitions each department actually uses, lay them side by side so the differences are visible, then help you settle one shared definition at a time. That definition is written as a formula in the system ⇒ every report and every dashboard calculates from the same one automatically, instead of each person doing their own maths.

Connects to

Every system connected in Data HubThe reports you already use

gone

Time lost arguing over whose number is right · From the work we have done, management meetings were losing the first 15–30 minutes to this every time.

03 · Data Cleanup

Clean duplicates and malformed records before they reach a report

In use in 2–4 weeks

Work arrivesData HubCustomer / system

The AI agent does itData CleanupLikely duplicates ranked by confidence, with the reason it thinks they match

A person decidesApprove in one clickMerging two records into one always needs a person to confirm. The system proposes; it does not decide.

Resultdown sharplyDuplicate records in the customer database

Example: Clean duplicates and malformed records before they reach a report

LIVE DEMO

1 · Work arrives

One customer is recorded under four names because people typed it differently, one product has two codes, and some rows carry Buddhist-era dates while others are Gregorian. Merge it all and the report is wrong without anyone noticing.

2 · The AI agent does it

Likely duplicates ranked by confidence, with the reason it thinks they match
Dates, phone numbers, tax IDs and product codes standardised to one format
A monthly data health report showing which channel the mess arrives through
Rules pushed back into the source systems so bad data stops coming in again

3 · You just approve

Merging two records into one always needs a person to confirm. The system proposes; it does not decide.

Approve in one click

Result

down sharply

Duplicate records in the customer database

The agent finds rows that are probably the same thing written differently, standardises dates, phone numbers and tax IDs, then proposes in batches which records should be merged. It does not merge on its own, because an incorrect merge is harder to undo than leaving it alone.

Connects to

Data HubCRM / customer databaseProduct register

down sharply

Duplicate records in the customer database · From the work we have done, a customer base that has never been cleaned usually runs 10–25% duplicates, which makes both the customer count and spend per head wrong.

04 · Self-serve Dashboard

A screen anyone can open themselves, with nobody to wait for

In use in 3–4 weeks (once Data Hub is in place)

Work arrivesData HubCustomer / system

The AI agent does itSelf-serve DashboardA main screen that works on phone and desktop, with filters you drive yourself

A person decidesApprove in one clickThe screen is a tool for looking at data. Deciding and acting is still a person's job.

Resultdown sharply“Can you pull that data for me” requests

Example: A screen anyone can open themselves, with nobody to wait for

LIVE DEMO

1 · Work arrives

You want this branch only, this product group, this period — so you message the team for a file and wait half a day, and once it arrives you want to see it from another angle anyway.

2 · The AI agent does it

A main screen that works on phone and desktop, with filters you drive yourself
Click any number through to the source records
Summaries sent to LINE or email on the cycle you set
Role-based access — who gets to see which layer of data

3 · You just approve

The screen is a tool for looking at data. Deciding and acting is still a person's job.

Approve in one click

Result

down sharply

“Can you pull that data for me” requests

We build a screen that works on a phone or a computer, filters by branch, period, product or channel, and lets you click any number down to the records behind it. It can send a summary into LINE on whatever cycle you want, and what each person can see is set by their role — not everyone sees everything.

Connects to

Data HubMetric DictionaryLINE / email

down sharply

“Can you pull that data for me” requests · From the work we have done, requests like these eat hours a week from the data or accounting team.

05 · History Keeper

Keep the history your source systems throw away

Starts accumulating as soon as Data Hub is running

Work arrivesData HubCustomer / system

The AI agent does itHistory KeeperHistory that keeps accumulating, independent of what the source systems retain

A person decidesApprove in one clickYou decide how far back the history is kept, and personal data is masked where it is not needed before it is stored.

Resultnot capped by the sourceHow far back you can compare

Example: Keep the history your source systems throw away

LIVE DEMO

1 · Work arrives

You want to compare this year with last year, but the POS only keeps twelve months and anything older is gone — and marketplaces limit how far back you can download.

2 · The AI agent does it

History that keeps accumulating, independent of what the source systems retain
Values kept as they were at the time (price, cost, promotion), not as they are now
Period comparisons on the spot, year on year and month on month
A retention and deletion policy agreed with you, in line with PDPA

3 · You just approve

You decide how far back the history is kept, and personal data is masked where it is not needed before it is stored.

Approve in one click

Result

not capped by the source

How far back you can compare

Once Data Hub is pulling, we keep accumulating even after the source deletes ⇒ the longer it runs the deeper you can compare. It also stores the values as they were at the time — the price and cost when the sale actually happened, not today's — which is the usual reason historical profit comes out wrong.

Connects to

Data HubPOS / ERPMarketplaces

not capped by the source

How far back you can compare · Instead of being limited by each system's retention window, which is usually 6–12 months.

06 · Data Quality Watch

Know the moment data starts drifting, before the report is wrong

In use in 2 weeks (once Data Hub is in place)

Work arrivesData HubCustomer / system

The AI agent does itData Quality WatchDaily checks on row counts, value ranges, blanks and column structure

A person decidesApprove in one clickWhen the system holds back data it finds suspicious, a person decides whether to let it through or fix it at the source first.

Resultwithin a dayTime before you know the data has drifted

Example: Know the moment data starts drifting, before the report is wrong

LIVE DEMO

1 · Work arrives

The report looked normal all along, until you find the numbers have been wrong for two weeks because someone renamed a column in a source file, or one system quietly stopped sending.

2 · The AI agent does it

Daily checks on row counts, value ranges, blanks and column structure
Alerts that say which value is off and by how much
Clearly broken data held out of the reports until someone has looked at it
A monthly summary of which system the problems come from most

3 · You just approve

When the system holds back data it finds suspicious, a person decides whether to let it through or fix it at the source first.

Approve in one click

Result

within a day

Time before you know the data has drifted

The agent watches the shape of the data arriving every day — row counts, the normal range of values, how much is blank, and the column structure. When something changes it alerts you and says how it differs from usual, instead of waiting for a person to notice a strange-looking report.

Connects to

Data HubLINE / email

within a day

Time before you know the data has drifted · From the work we have done, without a watcher it usually takes 1–3 weeks to notice.

FAQ about this bundle

How is this different from the “Leadership sees it first” bundle?

That one is the using side — the morning report, asking for numbers in plain language, a warning when something looks off. This one is the side that makes the data exist and be trustworthy in the first place. If the data is still scattered and the definitions do not match, a beautiful morning report is still the wrong number. So most companies do this one first, or both together.

Do we have to replace the systems we use?

No. We read from your existing systems only — nothing is written back and the team's daily work does not change. Ripping out company-wide systems is a risk you do not need on the first project.

Will our company data be sent outside?

The data lives in a store you own, and personal data is masked where it is not needed before anything goes to a model. If you want everything to stay entirely on your own machines, there is an option to run the model on your own server.

A lot of our data is still on paper and in Excel. Can we start?

Yes, and it is one of the places that pays back best, because reading documents into a system is something AI does well. We start with the documents you use most often — you do not have to convert the whole company at once.

Services used to set this up

Assess the whole business Build it, then hand it over

Want a dashboard, but not sure the data is ready?

Take a free 30-minute call. We will work out which data the numbers you want would need, and whether what you have today is enough.

Book a free 30-minute call See all solutions

Make the company's numbers trustworthy, before you decide anything with them

A fit if you…

We usually start with

Data from every system in one place, updating itself

Settle once and for all what each number means

Clean duplicates and malformed records before they reach a report

A screen anyone can open themselves, with nobody to wait for

Keep the history your source systems throw away

Know the moment data starts drifting, before the report is wrong

FAQ about this bundle

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