Klepify vs Teal: which one doesn't hallucinate?

Both rewrite your résumé with AI. Both surface a match score. One has a server-side guardrail that makes invented job titles physically impossible — the other doesn't. Here's the architectural difference and what it costs you.

Prompt-only vs prompt + server-side overwrite

Both Klepify and Teal feed your résumé + the job description into an LLM and ask it to rewrite the bullets. The system prompts say "don't invent facts." That works most of the time. The interesting question is what happens the rest of the time.

Teal's documented pipeline ends at the LLM's response. If the model says your last role was "Lead Frontend Engineer" instead of "Senior Software Engineer" — because the JD asked for a Lead — that's what appears in your résumé. The user has to catch it.

Klepify's pipeline doesn't end there. After the LLM returns its HTML, the server runs a deterministic regex pass that finds every <h3> in every role block and overwrites it with the exact title @ company <dates> string from your parsed résumé. Same for the contact header. The model's drift is silently corrected. There is no way for a hallucinated title to reach you.

This is not a model-quality difference. It's an architecture difference. The same LLM call could produce either output — Klepify just doesn't trust the call on the four fields where invention is most damaging (your name, your title, your company, your dates).

What each tool actually does

What Teal users actually complain about

This isn't theoretical. Trustpilot reviews of Teal cluster around a few recurring issues with their AI output. These are illustrative of the pattern third-party reviewers like Qwyse have documented:

These complaints don't mean Teal is a bad product — they reflect a predictable consequence of prompt-only LLM guardrails. When you instruct an LLM "don't invent skills" but also paste in a JD demanding 12 specific skills, the model is in an instruction conflict. Sometimes the JD pressure wins. The user has to be the audit layer.

Klepify's deterministic overwrite makes the same class of bug structurally impossible for the four locked fields. Your name is whatever's in your parsed résumé. Your title is whatever's in your parsed résumé. The model can suggest changes; the server doesn't honor them.

Keyword overlap vs semantic similarity

Both tools score your résumé against the job description. They mean different things by "score."

Teal's score: keyword overlap percentage

Teal counts how many JD keywords (hard skills, soft skills, buzzwords, title, education) appear in your résumé text. Their target: 80%. Their UI tells you which keywords you're missing so you can add them. This is a Boolean lexical match — the underlying algorithm is fundamentally TF-IDF-flavored.

This was the right score for 2018 ATS systems that did literal keyword matching. Modern ATS platforms increasingly use semantic matching, where "developed customer-facing applications" matches a JD asking for "user-facing software" without sharing a single keyword.

Klepify's score: semantic similarity, before-and-after

Klepify compares your master résumé, the tailored output, and the JD via semantic similarity scoring — a numeric measure of how closely two pieces of text mean the same thing. We surface both scores plus the delta. So you see:

• Match before: 42% — your master résumé without any tailoring
• Match after: 71% — after Klepify's tailored rewrite
• Delta: +29 points — concrete evidence the rewrite did something

The delta is the part you can't get from Teal's UI. You see whether the rewrite actually moved the needle, not just whether the final score looks high.

$9/wk really means $468/year

Teal's pricing page leads with $9/week. That number is designed to feel small. Annualized, it's $468 — more expensive than Teal's own monthly tier ($29/mo = $348/year), more expensive than every other tool in the category. The weekly framing is a psychological lever, not a discount.

That's not a savings claim — it's just subtraction. For a job search lasting six months, the gap is $174. For a year, $348. For the same class of LLM doing the same rewrite, with stronger guardrails on Klepify's side.

When you should pick Teal anyway

Teal has real strengths and we're not going to pretend otherwise:

Pick Teal if:

• You need a polished, mature job-application tracker with a strong Chrome extension. Teal's tracker UX is the most refined in this category.
• You apply to 50+ jobs/week and need bulk-edit features for the tracker. Klepify's tracker is solid but younger.
• You don't mind reviewing AI-generated bullets carefully before exporting. If you're already in the habit of catching invented metrics, the architectural difference matters less to you.

Pick Klepify if:

• You care about your résumé not having made-up job titles or skills reach a recruiter, ever.
• You want to see the delta — concrete evidence the AI rewrite actually moved your match score, not just a final number.
• You also want ghost-job detection, AI exposure context (Atlas), and the Pro toolkit in the same subscription.
• You want flat monthly pricing at $9.99 rather than $9/week.