Purchase Orders over €20,000 Q1 2026

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2026	Silverbear Limited	Registration Database maintenance & development	Purchase Order	€84,732.00
31 Mar 2026	Paragon Customer Communications Ireland Ltd	Print, fulfillment and Postage Services	Purchase Order	€23,851.00
31 Mar 2026	Nostra Systems	ICT Managed Services and Equipment	Purchase Order	€22,628.00
31 Mar 2026	Maynooth Business Campus Management Ltd	Business Campus Management Fees	Purchase Order	€51,050.00
31 Mar 2026	FLEXEIR Facilities Management Services Ltd (K-MAC)	Facilities Management & Maintenance	Purchase Order	€52,722.00
31 Mar 2026	Fieldfisher	Legal Fees	Purchase Order	€239,184.00
31 Mar 2026	EBSCO Internation Inc.	Online Research Library annual fees	Purchase Order	€26,125.00
31 Mar 2026	Datapac	ICT Equipment	Purchase Order	€30,597.00
31 Mar 2026	Cloud Application Services	Annual fees re Online eVetting portal	Purchase Order	€37,935.00
31 Mar 2026	BH Associates	Preparation of Insights Report	Purchase Order	€28,840.00
31 Mar 2026	Applied Materials Ltd	Costs relating to fire-stopping and remediation works	Purchase Order	€38,812.00
31 Mar 2026	Abtran	Managed Administration Services	Purchase Order	€230,582.00

^* 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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