# Deb Kowalczyk, VP People & Operations at Parcel Labs -- read of HR Operations AI, May 9 2026

> 14 years in HR and people ops, currently running the full people function at a 38-person SaaS company that's had 5 voluntary exits in 6 months. My CEO thinks I have a retention problem. He's probably right.

## How I got here

I Googled "predict employee turnover small company" on a Tuesday morning around 6am before my twins woke up. This came up maybe fourth or fifth in results. Not sponsored. I clicked the organic result because the meta description had the phrase "60-90 days out" and I wanted to know what that meant. That is a specific enough claim that it got past my filter.

## What I clicked first

The hero line: "See Your Team Before You Lose Them." That's a real sentence. It doesn't say "unlock the power of people data" or "transform your workforce intelligence." Someone wrote that who has actually watched a person put in their two weeks and thought: why didn't I see this coming.

I also immediately scrolled to pricing because I've spent time on five of these pages this month and three of them had no pricing at all, which tells me they want to talk to a human before they tell me it's $2,000 a month. This one said $299 upfront. That bought it another 4 minutes of my time.

## Where I paused

"Behavioral data that predicts departures 60-90 days out." I stopped here for a while. This is either the actual valuable thing or it's complete nonsense, and I can't tell which from the page. What behavioral data? Performance scores from my HRIS? Login frequency? Survey responses? Time-to-respond in Slack? The specificity of "60-90 days" implies someone has run a model and validated it, but the page doesn't say what the inputs are. That matters enormously. If it's just "performance dropped in Lattice," I already see that. If it's something I can't currently see, that's interesting.

## What I distrusted

Three stats in the social proof block: "34% Average reduction in time-to-hire," "2.3x Improvement in retention prediction accuracy," "$18K Average savings found per company in first quarter." No n. No methodology. No time range. "Average" across how many customers? Two? Fifty? And what is "retention prediction accuracy" being measured against -- a baseline of gut feel? That's a very easy bar to clear.

The "Early customers see changes in their first month" sentence is doing a lot of heavy lifting without saying anything. Changes in what? Behavior? Metrics? The dashboard just having numbers in it is a change.

Also, "Built by Wishdeal Studio" in the footer. Not "Wishdeal Studio, the team behind HR Operations AI." Just a footer attribution like it's a client deliverable. That makes me wonder if this is a product or a portfolio piece. I'd want to know who is actually running this and whether there's a team behind it that will be here in 18 months.

## What would convince me

One real customer, by name, in a LinkedIn or G2-style quote, from a company I could look up. Not "a 40-person logistics company." A company with a name. Tell me it's Bench Accounting or Fathom Analytics or whoever. Let me Google them. Let me see if they have 38 employees and actually stopped losing people.

I also want to know specifically what "behavioral data" means. A one-paragraph explanation of the model inputs would do it. Not a white paper, just: here are the three to five signals we watch and here is what they correlate with. That would tell me whether the 60-90 day claim is real.

## What I'd ask in an email reply

1. The retention signal feature: what are the actual data inputs you're using to predict departures? I use Gusto and Lattice. Are you pulling from both, and what specific fields?

2. Do you have any customers in B2B SaaS specifically? The dynamics of turnover in SaaS are different from retail or logistics and I want to know if the benchmarks are relevant to my industry.

3. The $18K savings figure -- can you walk me through how one customer got there? What was the specific thing they changed, and how did you attribute the savings?

## Verdict: curious-enough-to-reply

The page communicates what the product does better than most I've seen this month, and the pricing is real. The retention prediction claim is specific enough to be either true or falsifiable, and I want to find out which. I'd send a short email with those three questions.

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*Memo by skeptic persona, generated 2026-05-09. Studio breaks own self-grading loop.*