Gigged.AI: Employing AI and skills-based matching to improve recruitment

Case Study
Written By Scottish AI Alliance

I. Company Overview

Company Name: Gigged.AI
Industry: Software Development
Location: Glasgow, Scotland 


​Gigged.AI is a Glasgow-based digital talent platform that leverages artificial intelligence to connect businesses with freelance technology professionals.

 I. AI Implementation and Impact

Business Problem 

Traditional hiring processes are slow, expensive, and inefficient, particularly for digital and tech roles.

Gigged.AI was built by our two founders Rich Wilson and Craig Short after more than 30 years combined experience of working in large enterprises and seeing these inefficiencies first hand. Rich and Craig both had a deep frustration with the recruitment industry but from very different angles. Rich was frustrated from being burnt out from over 12 years working long hours every week with clients and candidates who saw recruitment firms as a “necessary evil”. Having spent 12 years as a contractor, Craig hated the process of finding a new contract and all the old school admin he had to do through various outdated systems.

Gigged.AI was created to solve these issues by leveraging AI to match the best fit talent with the right opportunities in a more efficient, data-driven manner. Today, Gigged.AI is a skills-powered Talent Marketplace solving the tech skills shortage for enterprises through internal mobility and open talent. Gigged.AI’s products employ AI and skills-based matching to help tech leaders put the right people into the right roles with rapid speed and efficiency.

Identifying AI as the Solution 

Before starting Gigged, our CEO Rich spent more than a decade working in recruitment in the staffing industry, creating hundreds of Statements of Work and training large teams to do the same. The process was very time intensive and still left a lot of margin for error, leading to inconsistencies in how SoWs were put together. Using OpenAI’s API, Gigged.AI was able to design an AI-powered SoW Creator to help automate the process, saving hours of writing work and helping hiring managers to focus on other impactful tasks. With AI, the SoW creation process now takes minutes and allows hiring managers who use the platform to get roles live quickly, so that they can review matches and hire in days rather than weeks. Our generative AI automatically drafts comprehensive SoWs based on the project title, learning from previous, similar gigs to ensure relevance and accuracy. This significantly reduces the time clients spend on drafting and improves consistency across projects.

Gigged.AI’s skill-based matching algorithm, which matches projects to the best internal or external candidate, was based on our CTO Craig’s own experience of the challenges on both sides of the hiring process. He found that job specifications and project briefs were often too unclear to attract the right specialist talent, particularly for technical roles where things like languages and platforms are critical in ensuring the right match. Craig built the skills-matching algorithm to remove the guess work from finding the right talent and allowing hiring managers to quickly find the right people for the project.

Selecting the Right AI Technology and Partner 

We chose to build with OpenAI’s API primarily because of its robust API, which allows us to leverage advanced language models like ChatGPT to streamline the process of defining project deliverables and facilitating communication between organisations, employees and contractors on our platform.

Quantifying the Impact 

Gigged.AI’s mission is to solve tech skill shortages for enterprises, while giving employees and contractors the ability to leverage their skills and build rewarding careers. Some of the ways we measure the impact we have made in this area include:

  • SoW Accuracy and Time Savings: This feature has proven instrumental in streamlining project setup, saving clients hours of manual work. By automating scoping and project alignment, the SoW Creator eliminates the back-and-forth typically needed to finalise project details, allowing teams to focus on core tasks. Creating an SoW takes less than 3 minutes.

  • Streamlined Interviewing: Gigged.AI’s skills-based matching and AI-assisted SoW creation help hiring managers target only qualified talent and prevent hundreds of unqualified applicants. Projects created on Gigged.AI receive 9 vetted proposals on average, saving hours of screening and interview time. One financial services client even made a hire from a single interview, having been matched with the perfect consultant.

  • Accelerated Time to Hire: From SoW creation to a hire being made, it now takes just a few days on average, a marked improvement over traditional models that may take weeks​​. Currently 50% of hires are made from the top 5 matches highlighted by Gigged.AI’s algorithm.

  • Cost Efficiency: By providing freelancers on an as-needed basis, Gigged.AI’s platform allows companies to reduce reliance on high-fee consultancies and recruitment agencies, saving up to 30% compared to traditional models.

  • Access to Niche Skills: Gigged.AI’s platform offers access to contingent talent with over 500 skills, from data scientists to software developers, allowing companies to tap into specialised expertise previously out of reach​.

By building AI into our products, we are helping enterprises improve the way they hire for tech roles.

For example, with our Internal Talent Marketplace, our client Insights Learning and Development have now automated the process of matching employee skills to projects, helping them to streamline people operations and free up staff to focus on critical tasks. One of our other clients, The Access Group, now has more than 1600 employees signed up to the Internal Talent Marketplace who are showcasing their skills, experience and interests. Hiring Managers can create internal roles, gigs and secondments that are automatically matched with employees, enabling internal mobility across boundaries of departments and teams. This is allowing Access to drive innovation and create internal operational efficiencies.

To give some examples of our Open Talent Marketplace in action, a global shipping company needed a CISO urgently to do a Cloud Security Review. They put the project on Gigged.AI and it was hired and completed in less than 2 weeks for £4,000. Similarly, a £4 billion energy firm needed data lake expertise quickly after being let down by a big firm. They made a hire on Gigged in 48 hours and the contractor completed the work over 3 milestones. These are just some examples of how organisations are making tech hires quickly, compliantly and cost-effectively using our AI-powered platforms.

Challenges and Overcoming Them 

AI relies heavily on data, so ensuring the quality, accuracy and privacy of data when training our AI was of high importance. This is also not a one-and-done task, so we have continued to refine our SoW Project Creator and Matching Engine over time as we have served more clients and been able to observe the hiring process many times over.

Impact on Employees, Customers, and Stakeholders 

For our employees: Increased efficiency in operations, allowing us to keep a lean team that is able to focus on strategic growth.

For clients: Faster, more cost-effective hiring while improving talent retention and upskilling through internal mobility and open talent.

For contractors: Greater access to high-quality gigs without intermediaries, improving income stability​.

II. Adherence to Scottish AI Strategy Values

Identifying and Mitigating Ethical Challenges 

We are passionate about ensuring transparency in the hiring process. We built our proprietary talent-matching algorithm to identify the best-fit talent for each SoW, delivering a weighted score for each match based on multiple factors, including skills, experience, and project relevance. Crucially, the algorithm provides a weighted score and clear reasoning for each match, and has no knowledge of someone’s age, gender, location, race or religion. This ensures transparency and helps clients make faster, more confident hiring decisions.

Adopting Ethical Guidelines 

We conduct regular internal reviews and user feedback loops to identify and mitigate potential biases​.

There is always a level of human verification guiding the hiring process. Each SoW is supported by a member of our Customer Success team to help hiring managers by pre-vetting proposals and verifying the talent they are matched to.

Ensuring Fairness and Unbiased Systems 

We regularly update our AI models with industry-verified skills data. Most recently we have been preparing to launch a new feature in our Internal Talent Marketplace (ITM) which will give organisations the choice to include The Skills Framework for the Information Age (SFIA) v9 as the skill taxonomy. SFIA provides a globally recognised structure for defining, assessing, and developing skills within technology-driven industries. For CIOs and IT leaders, SFIA offers a structured and scalable approach to defining, assessing, and developing workforce capabilities.

We are also Cyber Essentials Certified - a government-backed certification scheme that helps keep our customers’ data safe from cyber attacks.

III. Sharing Best Practice

Lessons Learned:

  • Start with a clear problem statement: AI should solve a specific, measurable issue, not be used for the sake of innovation.

  • Ensure transparency. Users need to trust AI recommendations, so explainability is crucial.

  • Mitigate bias early: Bias can creep into AI through training data—constant monitoring is necessary.

  • Adopt an agile approach: Testing and user feedback loops will help you ensure continuous improvement​.

IV. Contact

Contact: Sean MacNicol           
Position: Head of Growth
Email: sean@gigged.ai
Website: https://gigged.ai

 

 

Scottish AI Alliance
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