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| Source: | Find a Tender Service (FTS) |
| Notice Type: | Award notice |
| Buyer: | East West Rail Company Limited |
| Main Category: | Services |
| Procurement Method: | Direct award |
| Method Rationale: | This direct award is proposed under Section 41 and Schedule 5, paragraphs (5) and (6) of the Procurement Act 2023 on the basis that the contract may be awarded without competition where, for technical reasons, only a particular supplier is capable of delivering the requirement. EWR Co has determined that the services can only be supplied by Nplan Ltd for the following technical reasons: • A common AI-based schedule risk analysis solution, underpinned by a proprietary and unique relevant dataset, is required across the East West Rail programme to ensure consistency of methodology, outputs and reporting. Nplan's service operates on the basis of a proprietary dataset comprising over 750,000 schedules across various industries but critically including historical Crossrail, Network Rail and HS2 data which is are of particular relevance to EWR given that these examples also pertain to rail construction; and • EWR does not believe any other supplier offers a comparable service with the same functionality based on real GB rail data. There are therefore no reasonable alternatives to the Nplan service offering. For these reasons, EWR Co considers that Nplan Ltd is the only supplier capable of meeting the specific and complex technical requirements of the project while ensuring value for money and continuity of delivery. |
| Tender Status: | Closed |
| Estimated Value (ex. VAT): | Not specified |
| Release Date: | 2 April 2026 |
| Application Deadline: | — |
| Contract Start Date: | — |
| Contract End Date: | — |
| Contract Duration: | NaN years |
| Procurement ID (OCID): | ocds-h6vhtk-067c3d |
| Notice Reference: | 031097-2026 |
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Awarded to
| Award value: | £3,014,851.02 |
| Award status: | Pending |
Licence of an Artificial Intelligence Schedule Risk Analysis Tool, which uses a proprietary dataset and analysis of previously completed projects to apply learning to current schedules, to better predict the outcome
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