Why we left Turing for Devlyn after 6 months

This is a real story from a CIO at a $120M healthtech platform. Names are anonymised at his request; the calendar, the numbers, and the engagement pattern are exactly as he described them in a closed CXO peer call last quarter. The pattern is not specific to Turing — it shows up across every AI-driven matching marketplace at the same six-month mark — but Turing was his vendor, and Turing is the one I will name.

The opening: Turing’s matching engine performed exactly as advertised

The CIO had a clear capacity gap. His healthtech platform was running on a Spring Boot backend with a React frontend, HIPAA workloads, and a roadmap that included a major data-pipeline rebuild plus three integration partnerships before quarter-end. His in-house team was eight engineers and his FTE pipeline was running at five months from offer to ramped engineer. He needed two senior backend engineers immediately.

He posted a brief on Turing on a Tuesday. By Friday Turing’s matching engine had returned five candidate profiles. By the following Wednesday he had interviewed three and hired two — one with strong Spring Boot and HIPAA-adjacent experience, one with a stronger Python and data-pipeline background. Turing did exactly what Turing markets it does. The matching cycle was clean. The vetting was rigorous. The English was fluent.

The hourly rate was $85/hour for the Spring Boot engineer, $90/hour for the data engineer. Combined monthly burn was around $28,000. The CIO had budget through quarter-end and was prepared to renew if the engagement worked.

Months one through four: solid execution, structural problem

Both Turing engineers performed. The Spring Boot engineer hit standups, reviewed PRs, shipped his tickets. The data engineer landed the first integration partnership on schedule.

The structural problem was not execution — it was team coherence. Turing places individuals. The two contractors did not own architecture together; they owned their respective tickets. Security review on the HIPAA workload stayed on the in-house team. DevOps stayed on the in-house team. Integration testing across the contractors’ work stayed on the in-house team. The CIO was managing two parallel contractor relationships plus the in-house team — three threads instead of one.

He told me he tried to articulate this to his Director of Engineering at month three. The Director’s framing was: “We have two more engineers. The math should work.” The math did not work because team coherence does not scale linearly with headcount. Three threads of work meant three sets of standups, three sets of design discussions, three sets of context overhead. The CIO was spending fifteen hours a week coordinating Turing-shaped work that he had assumed would self-coordinate.

Month seven: the engineer-churn pattern

In month seven the Spring Boot engineer left for a higher rate at a competing platform. Turing’s two-week trial replacement is real for the initial match — but mid-engagement churn after the trial window is on the buyer’s calendar. The CIO posted a new brief. The new matching cycle ran another nine business days. The replacement Spring Boot engineer onboarded; ramp on the HIPAA workload took three more weeks because the codebase had domain context his predecessor had built up over six months that was lost when he left.

In month eight the data engineer informed the CIO that her Turing rate was being raised at the next billing cycle by 15% — competitive with what she could earn elsewhere. The CIO accepted the increase. Combined burn climbed to $32,500.

In month nine the replacement Spring Boot engineer dropped to half-capacity citing a “scheduling conflict” — which the CIO suspects (correctly, in 2026 marketplace terms) meant a higher-paying engagement on the side. He posted a third brief. By the time the third Spring Boot engineer was in his Slack, the CIO had run four Turing screening cycles in nine months for what was structurally the same seat.

The math by end of month nine:

• 4 Turing screening cycles run for the same role.
• Roughly 7 calendar weeks of velocity loss to mid-engagement churn and reonboarding.
• ~$280,000 in cumulative Turing spend.
• Two integration partnerships shipped against a target of three.
• Board had started asking why headcount and budget were both up while delivery was flat.

He had also been reading the same 2026 IT-CXO content I had — AI-augmented pods compounding 4× the historical pace at similar engineer counts. By month nine he was open to the possibility that the marketplace shape itself was the structural problem.

The Devlyn discovery call

He booked a 30-minute Devlyn discovery call on a Wednesday. He brought his roadmap, his Turing burn rate, and the integration partnerships still on the docket. The discovery call ended with a recommended pod composition: two backend engineers (one Spring Boot lead, one data lead), shared DevOps capacity, a dedicated PM line, AI-augmented engineering as the workflow standard, and a single retainer covering all of it.

The proposed retainer was $11,400 a month. Against his then-current Turing burn of $32,500, the line-by-line math was: same engineer count, one PM line included, one retainer line, AI-augmented workflow promise of 4× historical pace, replacement guarantee internal to the practice, no marketplace churn risk. Devlyn proposed a 3-day free trial against a real scoped task — same task he would have posted on Turing that week.

The trial ran Friday through Monday on a HIPAA-adjacent integration spec. The pod returned a working implementation that his Turing team had been quoted at fifteen days of work. The 3-day output was not theatre; it was the AI-augmented workflow operating as advertised, with senior validation paying attention to the security review the Turing contractors had been leaving for the in-house team.

He hired Tuesday. The pod was in his Slack and repos within 24 hours.

Want to see the model against your actual roadmap? Book a 30-minute Devlyn discovery call → — no contracts, no commitment.

What changed: months ten through twelve

The CIO ran his Turing engagements out for two more weeks while the Devlyn pod ramped, then closed the Turing contracts cleanly. By month eleven the team’s shipping cadence had compressed: integration partnerships that the in-house-plus-Turing team had been quoting at three weeks were landing in five to seven days. The 4× compression was not against a single engineer’s output; it was against the team’s historical output for similar scopes.

The structural reason — the pod lead walked him through it in a one-hour kickoff — was that AI-augmented engineering is a workflow design, not a tool selection. Turing contractors using personal AI tools produce a 1.2–1.5× velocity bump in honest reporting. The same tools embedded in pod-level workflow with senior validation, automated review pipelines, integrated security testing on HIPAA workloads, and compressed-cycle as the practice standard produce 4×. The delta is the workflow.

By month twelve he had shipped the third integration partnership, closed the data-pipeline rebuild, and converted one of the Devlyn pod members to a permanent FTE through Remote.com’s EOR product — Devlyn introduced the engineer, Remote.com handled the compliance machinery. The remaining pod stayed on retainer for ongoing platform work.

The board stopped asking about velocity.

The honest reckoning: when Turing was still right

Turing was not the wrong vendor in months one through four. The CIO had a two-engineer capacity gap that needed clean vetted contractors, and Turing delivered that. If his roadmap had been two engineers of bounded contractor work rather than team-coherent integration partnerships at AI-augmented velocity, the original Turing engagement would have been correct and he would have renewed it.

The vendor became wrong when the engagement shape mismatched the work shape. Turing places individuals. The CIO needed pod-level coherence on architecture, security, DevOps, QA, and integration testing — under one PM line, with retention protection against marketplace churn. Turing does not solve that problem. Devlyn does.

The CIOs who get this right in 2026 use marketplace matches for bounded contractor work and pods for roadmap velocity that needs team coherence. The CIOs who get it wrong run nine-month marketplace engagements through four screening cycles and end up at month ten with a board that has stopped trusting the velocity story.

What the numbers looked like, side by side

Lever	Turing months 1–9	Devlyn months 10–12
Engagement model	Two parallel contractor matches	One pod retainer
Monthly burn (peak)	$32,500 (rate increases + 3rd cycle)	$11,400 (pod with PM + DevOps)
Mid-engagement churn	4 screening cycles in 9 months for the same seat	0 (internal practice replacement)
Velocity vs historical	Linear addition	4× compounding
Coordination overhead	15 hours/week of CIO time	Pod PM line absorbs coordination
Replacement ramp	9–14 business days per cycle + reonboarding	24 hours via internal practice
HIPAA security review	Stayed on in-house team	Inside pod scope under senior validation

The line that mattered most to him was the last: HIPAA security review staying on the in-house team during a Turing engagement was a structural risk he had been absorbing without naming. Devlyn’s pod-level ownership of security review was the differentiator that finally let him scale the platform without compounding the in-house team’s review burden.

What he tells other CIOs now

I asked the CIO what he tells his peers. His answer was short.

“If your work is two contractors on an existing team and the in-house team will own architecture, security, and DevOps — Turing’s matching is excellent. If your work needs a coherent pod that owns architecture and security as part of the engagement, the marketplace shape is wrong for it. Nine months of contractor churn taught me the difference. The board does not care about engagement shape; the board cares about velocity. The shape determines the velocity.”

He still uses Turing twice a year for bounded contractor work — a niche-stack engagement, a one-off migration assist. The framing is roadmap-with-security-coherence-mode versus contractor-mode. The two vendors are not mutually exclusive when the work is genuinely different shape.

What to do if you are at month four or five with Turing

If you are reading this from inside a Turing engagement that started clean and is now flattening or churning — the pattern is structural. The diagnostic questions are:

1. Is the work contractor-shaped or pod-shaped? Contractor-shaped means individual roles on an existing team that does not need shared architectural ownership. Pod-shaped means architecture, security, DevOps, QA, and integration testing as a coherent owned unit.
2. Has mid-engagement churn started costing calendar? If you have run more than one screening cycle for the same role in six months, the marketplace shape is the cost.
3. Is security review staying on the in-house team during the engagement? If yes, the marketplace is structurally adding contractor capacity but not pod-level coverage — and the in-house review burden is compounding.
4. What does the board ask in the next QBR — is velocity multiplying? Marketplaces add headcount. Pods compound velocity.

The cheapest move from month five is parallel evaluation. Keep the Turing engagement running. Open a 30-minute Devlyn discovery call. Run a 3-day free trial against a real scoped task. Decide based on output, not on rate cards.

The CIOs who run this parallel test in 2026 are converging on the same conclusion: Turing is correct for vetted bounded contractor work, AI-augmented pods are correct for team-coherent roadmap velocity. The two are not competing; they are different tools for different shapes of work.

If you are running a $5M–$500M IT organisation and your engineering capacity is the constraint — and the marketplace screening cycles are starting to feel like a roundabout — the gap is structural. Book a 30-minute Devlyn discovery call → — no contracts, no commitment. For retainer-grade engagements, the Standing Invitation is where briefs get sent.