Job Matches is AI Build Group's UK CV-matching platform. Our engineering emphasis was on relevance, candidate control and repeatable vacancy discovery rather than automating job applications without permission.
This AI Build engineering case study explains the product decisions behind [Job Matches](https://www.job-matches.co.uk) and what businesses can learn from the approach. It is a description of the architecture and design choices, not a claim that every planned feature is already released.
Why did we develop Job Matches?
Candidates spend time repeating the same search and preparing applications. Employers need relevant candidates, but using candidate CVs introduces consent and access questions that cannot be treated as an afterthought.
How did we structure the platform?
The workflow separates CV-derived career profiles, candidate preferences, vacancy collection, matching and document preparation. Candidate-facing ranking is distinct from employer access to opted-in profiles, and the user retains the final application action.
Which design decision mattered most?
We deliberately kept job submission under candidate control. Employer search is also permission-led rather than assuming CV submission means public discoverability.
What were the engineering trade-offs?
A matching score is a recommendation signal, not an employment decision or guarantee of suitability. Clear explanations and human review matter more than presenting model outputs as certainty.
What can other teams learn from this build?
A narrow, repeatable workflow can become a product when its responsibilities, data boundaries and exceptions are explicit. This is the same discipline we apply to wider AI business automation.
Learn more: [Explore Job Matches](https://www.job-matches.co.uk) · Read the original Job Matches build story.
Editorial note: this article describes product architecture and implementation approach; it does not claim unverified results, customer numbers, independent certification or measured performance improvements.