AI Powered Resume Cleaning and Fraud Detection

Last updated: March 4, 2026

Client Context

The client is a staffing and recruiting company that handles a steady flow of resumes from multiple channels. Candidates send profiles as Word documents, PDFs, or exports from job portals. Formats differ widely, and content quality ranges from polished to barely readable.

Before this project, recruiters had to clean every profile by hand. They fixed spelling and grammar, adjusted structure, removed personal contact details, and made sure the resume matched the company standard before it was sent to a customer. On busy days the team struggled to keep up.

The Manual Workflow Problem

For each incoming resume, a recruiter typically spent thirty to sixty minutes on routine work before they even reached the question of fit for the role. The steps were familiar to any staffing team:

• Open the source file and check basic readability.
• Fix typos and obvious grammar issues.
• Align fonts, headings, and bullet styles with the company standard.
• Remove or mask contact information if the profile is shared with clients through vendor systems.
• Scan for gaps, overlapping dates, or suspicious details that might point to a misrepresented profile.

This work was important, but it was also repetitive and hard to scale. As volumes grew, the time cost started to impact both turnaround time and the attention available for actual candidate evaluation.

What Sequoia Built

Sequoia designed an AI assisted resume preparation flow that sits between raw candidate resumes and the final profile sent to customers. Recruiters still control the outcome, but they no longer perform the tedious work by hand.

The solution accepts resumes in common formats, applies a set of focused AI prompts and rules, and then presents a standardized profile in the company template. The recruiter then spends a brief window checking content quality and reviewing any fraud indicators that were highlighted.

How the AI Workflow Runs

The architecture is intentionally lightweight. The system does not depend on heavy pipelines. Instead it uses targeted prompting and a small rule layer to produce predictable output.

1. The recruiter uploads a candidate resume or pastes content into the tool.
2. The service extracts the raw text and segments it into sections such as summary, employment, skills, and education.
3. An AI model refines the language, improves clarity, and removes obvious errors without rewriting the story.
4. The cleaned content is arranged into the standard profile format used by the staffing firm.
5. A separate analysis pass looks for fraud indicators and adds a simple checklist for the recruiter.
6. The recruiter reviews the result, makes final edits, and exports the profile for submission.

Fraud Detection Layer

The client wanted the system to do more than polish text. In their market, profile fraud is a real concern. Candidates sometimes inflate experience, reuse content from unrelated roles, or adjust timelines to fit a requirement.

To support the team, Sequoia added a light fraud detection layer that does not replace human judgment but helps direct attention.

• Highlight overlapping employment dates or sequences that do not add up.
• Note long unexplained breaks where the candidate has not provided context.
• Flag skill lists that are very broad for the stated experience level.
• Call out content that reads like generic AI generated text, so recruiters can verify with the candidate.

The result is a profile that is both easier to read and easier to trust, with potential issues surfaced early rather than after a client raises a concern.

Impact on Recruiter Productivity

Once the AI workflow was live, the team changed where their time went. Instead of manually editing every resume, recruiters focused on judgement calls and fit for role.

Fit With Wider Recruiting Workflows

This project is not only about resume cleaning. It also shows how AI can sit inside a larger recruiting workflow without forcing teams to change everything around it.

The same pattern can support related use cases such as:

• Creating multiple versions of a profile for different client formats.
• Preparing standardized profiles for project based staffing or managed service deals.
• Summarizing candidate history for busy hiring managers.
• Feeding cleaner data into applicant tracking systems and analytics tools.

For Sequoia, this case study is one more example of using AI to simplify work that already exists, rather than adding a separate tool that employees have to manage.