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Tag: Automation

  • Automating UNSPSC Expense Classification

    Automating UNSPSC Expense Classification

    Automating UNSPSC Expense Classification

    Overview

    We worked with the Department of Finance to automate mapping expenses to United Nations Standard Products and Services Code (UNSPSC) with a bespoke machine learning model, delivering high-accuracy classifications and freeing teams from manual coding.

    Problem

    Analysts spent significant time reading expense descriptions and assigning precise UNSPSC codes. Existing manual processes slowed throughput and introduced inconsistency.

    Solution

    • Custom ML classifier: Automated categorisation of financial transactions to UNSPSC codes.
    • Technique blend: Combined text processing, a custom learning mechanism, and semantic matching to improve coverage for future cases.
    • Accuracy focus: Tailored to the department’s data to maximise precision.

    Result

    • Over 99% classification accuracy, a 15% uplift versus out-of-the-box machine learning techniques.
    • Productivity and cost savings from automating a previously manual task.

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  • Automated Recruitment Pipeline

    Automated Recruitment Pipeline

    Automated Recruitment Pipeline

    Overview

    We worked with HireMii to streamline candidate onboarding and matching by introducing an AI engine that reads CVs, structures key details, and ranks candidates against job requirements with clear reasoning.

    Problem

    Candidate drop-off spiked at the step where applicants had to re-enter experience details after uploading a CV. Recruiters also needed faster, higher-quality matching between candidates and roles.

    Solution

    • AI CV parsing and structuring: Extracted and categorised the information recruiters need from thousands of CVs, ready for the onboarding platform.
    • Semantic matching with knowledge graph: Used AI-powered associations between skills, roles, and companies to find, match, and rank candidates. A knowledge graph built from public professional profile data captured skill rarity and company context, improving both parsing and match quality with explainable scoring.

    Result

    • +30% increase in candidates completing onboarding after integration.
    • Time and cost savings from automating manual review.
    • Better match quality with transparent reasoning behind candidate rankings.

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