Step-by-step: deflect ats keywords questions for Applican…

Before training an agent, define the questions it will answer and the resources it will use. The most common ATS keyword support tickets usually fall into three buckets:

• “How do I find the right keywords for my resume?” – candidates want a method for extracting keywords from job descriptions.
• “Will my resume get rejected if I don’t match every keyword?” – anxiety about parsing rules and rejection myths.
• “How do I format my resume so the ATS can read it?” – questions about PDFs, tables, columns, and file types.

Gather the content that already addresses these. Support ticket replies, help centre articles, blog posts, and internal training notes all work. The goal is a single agent that resolves these queries autonomously, so your team only spends time on edge cases – candidates who need personalised review or have a unique situation.

Decide on success metrics now. Two that matter for a keyword-deflection agent: deflection rate – the share of ATS-keyword conversations the agent handles without human handoff – and time saved per week from eliminated ticket replies. Chatref’s insights engine will track topic clusters once the agent is live, so you can compare before-and-after.

You will build one Chatref agent trained on your ATS keyword resources. There is no code required.

1. Create a new agent in your Chatref workspace. Give it a name like “ATS Keyword Coach” and set the primary colour to match your brand.
2. Add your training content. Upload PDFs, point it at your help centre URL, or paste in plain text. Include articles that walk candidates through keyword extraction, formatting checklists, and any FAQ-style snippets your support team uses as macros.
3. Configure a lead-capture action (optional but valuable). When a candidate asks “Can you review my resume?” or “Do you offer a keyword report?,” the agent can collect their name and email for your sales or success team. This turns a support question into a warm lead without adding work for your staff.
4. Test the agent in the playground. Fire the top ten keyword questions at it and review the answers. If any response feels generic or misses a nuance, add that specific answer into your training content and re-test.

The agent is now answering from your own material – no internet search, no hallucinations – and can capture lead details inside the chat.

A soft launch on your help centre or candidate-facing portal lets you validate the agent before a full public deployment.

• Embed the widget on your FAQ page or a dedicated “Resume Keyword Assistant” page first. This confines the initial volume and lets you watch how candidates interact.
• Invite two to three team members to the shared inbox. They monitor the conversation feed for the first few days, watching for patterns the agent might miss. Use what they find to fine-tune the training content – adding an example about ATS-friendly fonts, for instance, if candidates keep asking about it.
• Set a human-handoff threshold. If the agent cannot match a response with high confidence, it can hand the conversation to a human with full context. Tell your team to handle those manually, then turn the resolution into a new training snippet so the agent learns for next time.
• Announce the assistant to your candidate community with a short email or in-app banner: “Ask our new ATS Keyword Coach anything about optimising your resume. Instant answers from our own guides, 24/7.”

Keep the rollout phased. After a week of quiet monitoring, add the widget to your main support page, and then to the logged-in candidate dashboard if you have one. This lets you scale exposure as confidence in the agent grows.

Chatref’s insights feature turns conversation data into operational metrics without manual tagging.

• Check the top topic clusters. The insight digest will surface categories like “keyword extraction”, “scoring myths”, and “formatting errors”. Compare these to your ticket backlog before the agent went live – you should see a clear reduction in the volume of those manual replies.
• Track lead capture volume. The number of candidates who voluntarily leave their contact details during a keyword conversation shows how well the agent moves people from self-service into your pipeline.
• Monitor handoff rates. If the agent is consistently handing off a specific question (e.g. “How does your ATS handle synonyms?”), that is a signal to add or tighten the relevant content in your training set. Over a few cycles, handoff rates should drop.
• Calculate time saved. Estimate the average time your team spent answering keyword tickets, multiply by the deflected volume, and report it to your ops lead. Most teams see that a single agent covering one high-frequency topic recovers enough time to take on higher-value work within the first month.

Treat the first month as a tuning loop. Update the agent’s content as new keyword questions emerge, and let insights guide which articles to write next. The system gets more accurate and more useful the more it interacts with real candidates.

What causes ats keywords problems for Applicant Tracking Software?

Most problems come from a mismatch between how candidates write resumes and how ATS parsers structure job requirements. Candidates rely on generic keyword lists instead of role-specific phrases, they stuff keywords unnaturally, or they use document formats (tables, columns, image-based PDFs) that parsers cannot read. The result is a high volume of support tickets from qualified applicants who are frustrated because they think the software rejected them unfairly – when the real issue is how the resume was prepared.

How do I improve ats keywords for Applicant Tracking Software?

Train an AI agent on your own guides that teach candidates to extract keywords directly from job descriptions, use exact phrasing, and format resumes in plain-text-friendly layouts. Use the agent’s conversation insights to spot the most frequent keyword mistakes, then write new help content that addresses those mistakes directly. Also surface the agent prominently on your candidate-facing pages so people self-serve instead of emailing support. For more on scaling support for Applicant Tracking Software, see our industry guide.