Ailtir

Ailtir is an AI-powered platform designed to automate and coordinate the full tender response process for construction contractors, particularly those operating in Ireland and the UK. The service addresses the inefficiencies and high costs associated with traditional bidding, where contractors often expend significant time and resources on unsuccessful tenders. By functioning as a virtual AI bid team, Ailtir enables contractors to win more projects without increasing headcount.

The platform operates through a multi-agent system that manages every stage of the tender workflow, from opportunity identification and bid/no-bid decisions to pre-qualification, tender document analysis, cost estimation, technical content creation, compliance checking, bid review, submission, and post-award review. Users upload their tender documents, after which Ailtir's agents extract requirements, identify risks, analyze the document pack, draft method statements, support pricing, check compliance, and review the submission. This process compresses what traditionally takes weeks into a matter of hours, resulting in a compliant and competitive bid ready for portal submission.

Ailtir incorporates a knowledge engine that codifies a contractor's institutional knowledge, including pricing patterns, project history, and methods. This structured knowledge is retained within the platform, ensuring that expertise is not lost when key personnel leave and that the AI bid team becomes more effective with each tender. The system is designed to orchestrate and integrate with a contractor's existing software stack, rather than replacing existing tools.

Pricing for Ailtir is stated as an annual cost of €82,500 per year, which is described as less than the cost of a single in-house hire. The platform is available by request for early access, with no commitment required at this stage. Ailtir positions itself as a comprehensive AI bid team solution for construction contractors seeking to improve win rates and margins while retaining institutional memory and scaling efficiently with bid volume.