A client asks for a Python developer. But which Python developer?

The answer depends on what they are building, the experience the project demands, their budget and how quickly they need someone to start. A technology keyword alone cannot capture all of that.

At BeGig, understanding those details is an essential part of connecting clients with the right talent. It is also work that takes time. Before a recruiter can prepare a shortlist, they need to interpret the requirement, identify what matters most and assess which candidates have relevant experience.

Working with ModernEdge, we introduced Claude into this workflow to reduce the manual effort involved. Across more than 100 projects, we have seen approximately a 70% reduction in the time spent preparing an initial shortlist, saving roughly 40 recruiter hours each month.

Here is how we built it - and why recruiter judgment remains central to the process.

Turning a client request into a useful project brief

Client requirements arrive in different forms. Some are detailed specifications. Others are a few paragraphs, a technology stack or information gathered during a conversation.

BeGig already had a vetted talent pool and an existing matching system. The time-consuming step was translating those varied requests into clear, structured information that the matching process could use.

Claude now helps with that step. It analyzes the request to identify technologies, relevant experience, seniority, availability, budget and other constraints that affect the match.

It also helps interpret the context behind a requirement. A request for a Python developer, for example, may call for particular backend experience, familiarity with a certain type of product or experience working at a specific scale.

Those distinctions help recruiters move beyond broad skill matches and focus on what the engagement actually requires.

The analysis becomes a structured project brief, which feeds into BeGig’s existing talent-matching process.

Making recommendations easier to review

Finding relevant profiles is only part of preparing a shortlist. Recruiters also need to understand why each candidate could be a good fit and communicate that clearly to the client.

Once BeGig’s matching system surfaces candidates, Claude helps explain the matches. It highlights relevant parts of a candidate’s background and prepares summaries that can be tailored to the client’s project.

This gives recruiters a more useful starting point for review. Much of the repetitive analysis and summarization is already done, allowing them to focus on validating the recommendations and adding context.

Building AI into the existing workflow

The implementation began with understanding how our team already worked.

ModernEdge worked with BeGig to map the journey from an initial client requirement to a shortlist, identify the most time-consuming steps and design the Claude-powered layer around them.

That included requirement analysis, project structuring and candidate summaries, all connected to our existing matching process. Recruiters could use these capabilities within their workflow without having to move between the matching platform and a separate AI tool.

We also built in opportunities for recruiter feedback. Recruiters can correct an interpretation, add missing context, reject a recommendation or refine the requirement before another set of recommendations is generated.

That review matters because a project description rarely contains everything needed to make a good talent decision.

A recruiter may know something from a client conversation that was never written down. They may have worked with a candidate before. They may recognize that someone meets the technical requirements but is unsuitable for the particular engagement.

Recruiters remain responsible for deciding which candidates move forward.

What changed for our team

Before introducing the workflow, a complex requirement could take several hours of recruiter time to understand and turn into a shortlist. Initial recommendations are now typically available in under 30 minutes.

Across the projects processed, the workflow has delivered:

• Approximately 70% less time spent preparing an initial shortlist.
• Roughly 40 recruiter hours saved each month.
• Use across 100+ projects.

The time saved allows recruiters to spend more attention on client conversations, clarifying requirements and checking whether a candidate is right for the engagement.

What this taught us about practical AI

For BeGig, the value of this implementation comes from helping people reach a decision with better-organized information and less repetitive work.

Claude handles much of the language analysis and summarization. BeGig’s matching system surfaces relevant talent. Recruiters contribute experience, context and final judgment.

We see opportunities to extend this approach into team formation and project delivery. For now, it is already improving a core part of our business: turning a client’s project requirement into a useful talent shortlist, faster.