How a Staffing Agency Cut Manual Applicant Screening with a Custom FileMaker + AI Workflow

Yes, you can automate applicant screening without making recruiters read every resume first. A regional full-service staffing agency now pulls applications straight off its website, extracts the resume, and gives its team an AI-written summary of each candidate’s experience. Nobody has to open a form, download a file, or read a full resume before seeing the relevant details. Kyo Logic built that workflow on top of the FileMaker system the agency already ran on. Here’s how it came together.

The team was reading every resume by hand

The agency places temporary, temp-to-hire, and direct-hire workers, so applicant volume is the business. Candidates applied through the agency’s website and uploaded a resume as part of that form. Everything after that submission was manual.

Someone had to log into the form tool. Someone had to find the right application. Someone had to download the resume, open it, read the whole thing, and pull out the handful of details that actually decide whether a candidate moves forward.

For a staffing agency, that is not a small tax. Every extra step slows a placement, and it gets worse exactly when it hurts most: a busy hiring week with a stack of new applicants. The agency wanted to cut the manual review down without losing the information its recruiters needed to make a call.

What the agency asked Kyo Logic to build

The ask was direct: could the whole application-review process be automated end to end? Gather the application data from the website, get the information out of the resume, store it in FileMaker, and use AI to generate a short, readable summary of each applicant’s work history.

That is more than standard FileMaker development. It meant connecting four things that do not naturally talk to each other:

• FileMaker, to store and display applicant information in one place the recruiters already worked in
• The website form, where applications and resumes actually came in
• Resume files, PDFs and Word documents that had to be read programmatically
• An AI model, to turn raw resume text into a concise candidate summary

Kyo Logic’s first job was to say whether this was feasible and safe, then to build it.

A four-part build

Kyo Logic started with consulting: was the workflow feasible, and what were the security implications of pulling website-submitted application data into internal systems? Once the approach was confirmed, development ran in four stages, chained into one flow using three different APIs to gather, read, and process the data.

1. The applicant applies on the website.
2. The form API pulls the new application into FileMaker.
3. A custom microservice parses the resume file into plain text.
4. An AI model returns a short summary of the candidate’s history.
5. The recruiter opens one FileMaker record with everything already in it.

Part 1: FileMaker foundation. Standard FileMaker development stores and displays the incoming application data, giving the agency one central place to review candidates.

Part 2: Pull applications off the website. Applicants filled out forms and uploaded resumes through the site’s form tool. Kyo Logic learned and used that tool’s API to query for new applications while respecting the site’s existing security configuration.

Part 3: Get the text out of the resume. Resumes came in as PDF or DOCX files, so the system needed to read them programmatically. Kyo Logic built a microservice using multiple JavaScript libraries to extract the resume text and pass it back into the workflow.

Part 4: Summarize with AI. With the resume text in hand, Kyo Logic integrated an AI model and wrote the prompt structure to generate a brief outline of each applicant’s work history and experience.

Together, those four parts turned a manual, multi-step review into a single automated intake-and-evaluation flow.

What changed for the team

Before the project, screening an applicant meant navigating the form tool, finding the application, downloading the resume, reading the document, and manually pulling out the relevant experience. After it, the system gathers the application, extracts the resume content, and produces a concise summary on its own.

The recruiters spend less time on administrative steps and more time on the part that needs a human: judging candidate fit. The capability is genuinely new for them, not just faster. Web form submissions, FileMaker, document parsing, and AI now run as one connected process instead of four disconnected chores.

What this engagement delivered:

• Eliminated the manual “locate, download, and read every resume” step from the screening workflow
• Automated a complex, multi-system flow connecting the website form, FileMaker, resume text extraction, and AI-generated summaries
• Put a ready-made outline of each candidate’s history in front of recruiters inside the system they already use

Why it’s a FileMaker story, not a rip-and-replace

The agency did not throw out its systems to get here. It had run on FileMaker for years, and Kyo Logic extended that platform rather than replacing it. The build wired modern web-form data, document parsing, and AI onto the system the team already trusted.

That is the pattern Kyo Logic runs for FileMaker shops: modernize in place, add the new capability where it earns its keep, and keep the system of record the team knows. For another practical example, see How to Build AI-Assisted Workflows in FileMaker Without Losing Control.

Kyo Logic: custom Claris/FileMaker and manufacturing software, New England.

If your team is still reading every application by hand, that’s automatable. Let’s talk about your workflow.