What Actually Makes a Placement Last

The number that matters in staffing isn’t the placement rate. It’s how many people are still on your team a year later. Every departure costs you the ramp you paid for, the context that left with them, and the momentum of whatever they were mid-way through.

We are not going to quote you a retention percentage, and here is exactly why.

Here is our entire placement history, in one sentence: we have placed one engineer, the engagement ran about twelve months, and it ended because the client company shut down. Not because the fit failed. That is N=1.

There are fourteen years of production engineering behind the team — you can click three of those systems and they are still running — but placement is the newest thing we do. Anyone in our position who shows you a retention percentage computed on one engagement is showing you arithmetic, not evidence. We published a number once. We pulled it. A figure you cannot reproduce is a brag with a decimal point.

For calibration, here is what the market publishes. General IT employee retention runs around 87% annually, and the large outsourcing firms — EPAM, Cognizant, Globant, TCS, Wipro — report voluntary attrition between roughly 11% and 15%, which puts them in the 85-89% band. Any boutique claiming to clear that by a wide margin should be asked for the sample size before you believe it. Starting with us.

What we can defend is the mechanism — the design, not a track record. Here are the four things that decide whether a placement lasts. Check every one of them against any vendor you are evaluating, including this one, and treat the ones we cannot yet evidence as exactly that.

1. We vet for fit, not just skill

Most churn isn’t “the engineer couldn’t code.” It’s a mismatch that was visible at the interview and got waved through because the technical bar was met. Our third stage screens communication and ownership precisely because those are what break remote placements — not algorithms. Screening for how someone handles ambiguity, disagreement, and async writing is screening for whether they’ll still be thriving in month eleven, not just shipping in week one.

2. Timezone removes the daily friction that burns people out

An engineer who’s perpetually blocked waiting on answers, or working hours misaligned with the team, doesn’t last — not because they’re weak, but because the job is quietly miserable. LatAm placement inside US business hours means real-time collaboration instead of a 12-hour round trip on every question. That’s most of the nearshore-vs-offshore argument restated as a retention factor: overlap isn’t just faster, it’s more sustainable for the person doing the work.

3. The exit terms align our incentives with yours

If the fit is wrong, you end the engagement on 30 days’ notice, there is no claw-back, and we don’t bill you for the transition. We eat the lost revenue.

Note what we are not promising: a replacement candidate in your inbox within a week. At our scale there is no bench, and a vendor who promises you one either has idle engineers they are paying for or is planning to improvise. We’d rather hand you a deliverable commitment than an impressive one.

The commitment still does the work the guarantee was supposed to do, because the mechanism was never the replacement — it was the cost. When a bad match costs us the contract, we don’t push a marginal candidate to close a deal. Retention is what you get when the incentives at the moment of matching point the same direction as yours.

4. Boutique relationships, not a marketplace transaction

We work primarily with engineers we know personally or by direct referral. That’s a deliberate cap on scale, and it’s also a retention mechanism: people placed through a relationship, into a team that was chosen for them rather than scrolled past, treat the engagement as a relationship too. A marketplace optimizes for volume of matches; a boutique optimizes for matches that stick. Different objective functions produce different retention curves.

What churn actually costs you

If you want to reason about this in your own numbers: a senior engineer who leaves at month four cost you weeks of ramp before they were fully productive, takes undocumented context with them, and leaves you re-opening a req you thought was closed. Two of those in a year on a small team is a quarter of lost velocity that never shows up as a line item. Retention isn’t a soft metric — it’s the difference between compounding and restarting.

You can watch it happen in your delivery data rather than taking our word for it. The DORA four keys degrade in a specific pattern when a team keeps losing and re-acquiring context: lead time stretches first, then change failure rate climbs as people ship into systems they don’t fully understand yet.

That’s the whole point of the Hiring-as-a-Service model: not just filling a seat fast, but filling it with someone who’s still there — and still good — a year on.

One last piece of honesty, since this whole post is about not overclaiming. Placement is the newest thing we do and the one with the thinnest record; custom software and production AI are where the fourteen years actually live, and where we can point you at running systems instead of mechanisms. If you want a seat filled, tell us the role and we’ll tell you straight whether we’re the right call. Sometimes the answer will be no.