Purchase Orders Over €20,000 Q2 2023

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2023	MICROMAIL LTD	ICT Software Maintenance Paid	Purchase Order	€1,009,553
30 Jun 2023	M.J. FLOOD IRELAND LTD	Managed Print Services	Purchase Order	€190,000
30 Jun 2023	STATE CLAIMS AGENCY	HR Services	Purchase Order	€150,000
30 Jun 2023	eCOM SOLUTIONS LTD	ICT Services Paid	Purchase Order	€136,615
30 Jun 2023	WARD SOLUTIONS LTD	ICT Software Maintenance	Purchase Order	€129,919
30 Jun 2023	OFFICE OF PUBLIC WORKS	Office Premises Maintenance	Purchase Order	€100,000
30 Jun 2023	PFH TECHNOLOGY GROUP	ICT Services Paid	Purchase Order	€66,420
30 Jun 2023	NETCRAFT LTD	ICT Software Maintenance	Purchase Order	€61,500
30 Jun 2023	SURESKILLS	ICT Software Maintenance	Purchase Order	€60,885
30 Jun 2023	OFFICE OF PUBLIC WORKS	Office Premises Maintenance	Purchase Order	€50,000
30 Jun 2023	SURESKILLS	ICT Software Maintenance Not Paid	Purchase Order	€39,870
30 Jun 2023	SURESKILLS	ICT Software Maintenance Paid	Purchase Order	€29,520
30 Jun 2023	ROGERSON REDDAN and ASSOCIATES	Consultancy Services	Purchase Order	€25,000
30 Jun 2023	INVENTIVE MARKETING LTD	Marketing Services Paid	Purchase Order	€22,448
30 Jun 2023	THE HIRE LAB	HR Services Paid	Purchase Order	€22,140

^* Payment date may be the end of the reporting quarter when the entity does not publish an exact date.

How to read these figures

These are reported values from published purchase order and payment records — not verified cash paid, and not total spending by this organisation. A row may be a purchase order, a payment, or another reported transaction; the source files often do not say which.

Coverage and quality vary. Some fields are blank, supplier names are free text (the same firm can appear under many labels), and descriptions are inconsistent rather than a controlled taxonomy. An amount alone says nothing about value for money and it may or may not include VAT.

Records are retrieved from each organisation’s published files such as spreadsheets, PDFs, and other formats of uneven quality. Extraction techniques (including automated parsing) can introduce gaps, misreads, or incomplete rows when source layout or quality is poor. Treat the data as a starting point for investigation, not a definitive ledger.

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