Data to AI™ / ChatGPT/Personio
Review pending time off, missing medical certificates and attendance gaps from Personio in ChatGPT, approving each query so HR knows exactly which data was read.
Better answers, fewer tokens.
ChatGPT
Which departments took the most approved sick leave days in September?
Approved sick leave in September added up to 96 days. Customer Support took 41 of them, more than Engineering and Sales together.
| Department Name | Days Count |
|---|---|
| Customer Support Absences | 41 |
| Engineering Absences | 23 |
| Sales Absences | 17 |
Days Count is summed over absences with an approved Status; Department Name comes from Employees, matched on Employee ID.
Personio is an HR platform for small and medium-sized businesses that covers personnel administration, time tracking and absence management. Dataddo extracts Employees, Absences and Attendances, so People teams can ask about headcount, time off and working hours in plain language.
List Absences with a Start Date in the next 14 days whose Status is not approved, with Time Off Name and Days Count.
The HR coordinator confirms the query, then nudges the managers whose team members are about to start leave that nobody has approved yet.
Which Absences with the Time Off Name Sick leave from the last 30 days have a Certificate Status showing a certificate is still outstanding?
Payroll asks for the missing medical certificates in one round of emails before the month closes, instead of finding gaps during the payroll run.
Run data_status on the Personio flow, then show Attendances from last week that are pending or fall on a Holiday or while On Time Off.
Before timesheets are locked, the operations manager reviews entries that conflict with holidays or booked time off and asks employees to correct them.
Data to AI™ is priced per seat, like the AI assistants your team already uses, with ~25% off on annual billing. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
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per seat
3 seats minimum
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Pending time off | HR clicks through the absence overview team by team to find requests that are still open before they start. | ChatGPT queries Absences by Status and Start Date from the latest extraction and returns one list across all teams. | Open requests found early |
| Medical certificates | Missing sick notes surface during payroll, when there is no time left to chase them. | Absences carries Certificate Status, so ChatGPT lists sick leave that still needs a certificate whenever you ask. | Sick notes chased on time |
| Control over each lookup | Nobody can tell which rows of an uploaded attendance export shaped the summary in the chat. | ChatGPT asks you to confirm each tool call, and Dataddo tools such as refresh can be switched off in the app settings. | Every query approved |
| Token consumption | ChatGPT spends tokens exploring raw Personio columns, sampling rows and retrying until it understands the data. | Field definitions and relations are supplied up front, so the model skips the exploration and goes straight to the answer. | Fewer tokens per answer |
| Answer quality | Prone to wrong joins, invented fields and numbers that are hard to check. | Answers grounded in defined fields. In the Dataddo benchmark, 78.5% of questions were answered correctly, against 65.5% with plain CSV files. | Better answers |
The Personio datasets HR and payroll teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
| Dataset | Metrics and attributes |
Ask ChatGPT
|
|---|---|---|
| Employees |
|
Which employees with a Last Working Day this month still have a Vacation Day Balance above five days? |
| Absences |
|
Which absence requests starting next week have a Status other than approved? |
| Attendances |
|
Which attendance records fall on a Holiday or on a day the employee was On Time Off? |
We asked 43 pre-registered questions of three real datasets - Google Search Console, HubSpot deals and Google Ads - and delivered the same rows to the same model in four ways. Only the data layer changed.
of questions answered correctly - the best of the four delivery methods tested
fewer confidently wrong answers - 14.0% of runs against 24.0% and 26.3%
more correct answers on messy CRM data - better on 8 questions, worse on none
Same model, prompts and rows in every condition; 759 graded runs, ground truth frozen before any run. On Google Search Console alone, documented CSV files did as well as Data to AI™ - the advantage comes from messier data such as HubSpot deals.
Read the benchmark →Extract Personio in Dataddo with Absences and Attendances, using your Personio client ID and secret, then attach the flow to a ChatGPT destination or AI Model. In ChatGPT on the web, enable Developer mode in Settings > Security and login, create a Dataddo Data Access app with https://headless.dataddo.com/mcp-data and OAuth, and sign in.
Developer mode is needed, which is available on the web on Pro, Plus, Business, Enterprise and Education. On Business and Enterprise, a workspace admin may need to allow it first.
Yes. ChatGPT asks you to confirm each tool call, so you approve every lookup of Absences, Attendances or Employees before any result appears in the chat.
As current as the last extraction. Absences and Attendances re-read their date window on each scheduled run, and data_status shows when the flow last ran.
ChatGPT can call known_gaps, which lists what the data cannot answer. If you extracted only Absences, for example, questions about recorded working hours need Attendances too.
Untick columns such as Employee First Name, Employee Last Name and Employee Email when you create the Absences source. ChatGPT then works with Employee ID only, because it can query just what reaches the flow.
An API credential with Read access to personal data and the readable employee attributes selected. Dataddo uses the client ID and secret; ChatGPT never sees them.
