Why we left BairesDev for Devlyn after 6 months

This is a real story from a CTO at a $80M Series-C SaaS company. Names anonymised; calendar and numbers exact, as described in a CXO peer call last quarter. The pattern is not specific to BairesDev — it shows up across every large staffing-agency model when the engagement scales — but BairesDev was his vendor.

The opening: BairesDev’s scale pitch fit the Series-C ambition

The CTO had a $1.6M monthly engineering budget and a roadmap that required scaling from 22 engineers to roughly 35 over twelve months. Hiring at that pace through US FTE pipelines was unrealistic; he needed a staffing partner that could ramp 13 engineers across multiple stacks inside two quarters. BairesDev’s positioning — large LatAm staffing agency, scale capability, mature delivery process, US account management — fit the requirement on paper.

He briefed BairesDev. The sales cycle was four weeks of scoping, account-team meetings, and statement-of-work drafting. The first cohort of six engineers landed in his Slack at month two; the second cohort of four landed at month three; the third cohort of three landed at month four. Combined burn at peak was around $98,000/month at $85–$110/hour rates including BairesDev’s account-management margin.

Months one through three: the agency overhead

The BairesDev engineers were senior, vetted, and Spanish-language-fluent across LatAm time zones with strong English. The work product was solid. The CTO’s problem was not engineer quality; the engineers were generally on par with mid-level US contractors at lower rates.

The structural problem was agency overhead. BairesDev’s engagement model includes account managers, delivery managers, internal team leads, and a multi-layer escalation path — appropriate for very large multi-year engagements, but heavy for a Series-C velocity environment. The overhead showed up in three places:

1. Decision latency — architectural decisions involving the BairesDev team required round-tripping through delivery managers and team leads, which added 2–4 days per decision compared to the in-house team’s same-day turnaround.
2. Communication margin — the account-management layer cost roughly 15–18% on the hourly rate vs direct-engagement vendors, which added up materially across thirteen engineers.
3. Process drag — BairesDev’s internal SDLC norms (formal change control, periodic delivery reviews, structured sprint reviews) added overhead that the Series-C team had been deliberately running lean to avoid.

The CTO told me his honest reaction at month three was that BairesDev was a good choice for an enterprise context and a heavy choice for a Series-C context. He had bought scale capability and gotten enterprise overhead alongside.

Months four and five: the AI-augmented competitive set

By month four the CTO had been to two Series-C peer dinners where AI-augmented pods kept coming up. Two of his peers had moved engineering scaling work from large staffing agencies to AI-augmented vendors over the prior two quarters. The reported velocity differential was substantial — 3–4× on similar scopes at 50–60% of the burn.

He did the same retrofit attempt many CTOs run first: bought Cursor and Copilot licenses for the BairesDev engineers, asked the BairesDev account team about adopting AI-augmented practices. The account team agreed and rolled out individual tooling. Velocity per engineer climbed to roughly 1.4× historical pace — the same individual-AI-tool retrofit ceiling every other 2026 case hits. Pod-level compounding velocity does not come from individual tool adoption; it comes from workflow design that integrates AI generation, automated review, integrated testing, and senior validation across the engagement structure.

By month six:

• $588,000 cumulative BairesDev spend over six months.
• Thirteen engineers placed across stacks.
• Velocity per engineer with retrofitted AI tools: 1.4× historical.
• Decision latency on cross-cutting work: 2–4 days vs in-house same-day.
• Roadmap completion: 55% against a target of 65%.
• Account-management margin alone over six months: ~$95,000.

The Devlyn discovery call

He booked a 30-minute Devlyn discovery call. Brought the scaling roadmap, the BairesDev burn pattern with the agency-overhead breakdown, and the individual-AI-tool retrofit math. The discovery call ended with a recommended pod composition: a six-engineer pod plus shared DevOps/QA leads, dedicated PM line (no separate account-management margin), AI-augmented engineering as the workflow standard at pod level (not retrofitted individual tools), retainer of $32,000/month.

Against the BairesDev burn at $98,000, the math was: lower burn for fewer-but-faster engineers, no agency-overhead margin, AI-augmented pod-level workflow producing 4× compounding (vs 1.4× retrofit), and direct engagement without the multi-layer escalation path.

The total-output comparison was sharp: 13 BairesDev engineers at 1.4× = 18.2 effective engineer-equivalents. 6 Devlyn engineers at 4× = 24 effective engineer-equivalents. Same calendar, one-third the burn, more effective output.

Devlyn proposed a 3-day free trial against a real cross-cutting refactor that had been bottlenecked at the BairesDev decision latency. The trial ran Friday through Monday. The pod returned a working refactor including the cross-cutting decisions that had been waiting for two weeks of BairesDev round-tripping. The 3-day output validated the engagement-shape difference.

He hired Tuesday. 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 seven through twelve

The CTO ran the BairesDev engagement out for a structured 60-day transition. Eight of the thirteen engineers shipped open work and rolled off; five transitioned to wind-down on long-running engagements. The Devlyn pod scaled from 6 engineers to 9 over the next three months as the roadmap demands grew.

By month nine the team was shipping at the velocity the board had budgeted at month one. By month twelve the original twelve-month roadmap was delivered plus two additional initiatives the team had not budgeted at the start. The total engineering spend for months seven through twelve was approximately 45% lower than the BairesDev run-rate would have produced for similar scopes.

The structural reason was that AI-augmented engineering compounds at pod level with direct engagement. Agency overhead and individual-tool retrofitting both dampen the compounding. Removing both produced the 4× the board was looking for.

The honest reckoning: when BairesDev was still right

BairesDev was not the wrong vendor for the wrong company. BairesDev’s model fits enterprise contexts running multi-year engagements at scale where the account-management layer adds value through governance, contract management, multi-vendor coordination, and structured SDLC compliance. In those contexts the agency overhead is not overhead; it is service.

The vendor became wrong because the CTO’s company was not running an enterprise context. Series-C velocity environments run lean intentionally; agency overhead at that stage adds friction without commensurate value. Combined with the retrofit ceiling on individual AI-tool adoption, the engagement structurally could not produce the velocity multiplier the board had budgeted.

The CTOs who get this right in 2026 match engagement model to company stage. Enterprise contexts can absorb agency overhead and benefit from the governance layer. Series-A through Series-C contexts cannot. The CTOs who get it wrong assume scale capability is the differentiator and end up at month six with the velocity gap and the burn rate both higher than expected.

What the numbers looked like, side by side

Lever	BairesDev months 1–6	Devlyn months 7–12
Engagement model	13 contractors under agency account management	6-engineer pod scaling to 9
Monthly burn	$98,000 (peak)	$32,000 (initial) scaling to $45,000
Account-management margin	~15–18% on hourly	None (direct engagement)
Velocity per engineer (individual AI tools)	1.4×	N/A
Velocity per engineer (pod-level AI workflow)	N/A	4×
Effective engineer-equivalents	18.2 from 13 paid seats	24 from 6 (then 36 from 9)
Decision latency on cross-cutting work	2–4 days	Same-day
Roadmap completion vs target at month 6	55% / 65%	Caught up by month 9

The line that mattered most was decision latency. Series-C environments live or die on cross-cutting decision velocity; agency multi-layer escalation paths are fundamentally incompatible with that.

What he tells other Series-C CTOs now

I asked the CTO what he tells his peers. His answer:

“BairesDev’s agency model is excellent for enterprise scale and wrong for Series-C velocity. The agency overhead does not look like overhead until you are six months in and the math is obvious. AI-augmented retrofitting on top of agency engagements produces 1.4× velocity. Pod-level AI-augmented workflow with direct engagement produces 4×. Same engineers’ caliber; different engagement structure; different velocity outcome. The board cared about the second number.”

He still recommends BairesDev for friends running enterprise IT organisations. The framing is enterprise-governance-mode versus startup-velocity-mode.

What to do if you are at month three or four with BairesDev

If you are reading this from inside a BairesDev engagement that has scaled headcount and is producing flatter velocity than the burn warrants — the pattern is structural. The diagnostic questions are:

1. Does the company stage match the agency-governance value? Enterprise contexts benefit from the governance layer; Series-A through Series-C contexts pay overhead without commensurate value.
2. Has retrofitting AI tools onto agency engagements produced more than 1.5× velocity? If not, the engagement structure is the bottleneck.
3. What is the decision latency on cross-cutting work? If it exceeds same-day for cross-cutting decisions, agency escalation paths are slowing the roadmap.
4. What is the effective engineer-equivalent calculation? Headcount × velocity multiplier. Compare against direct-engagement pod models on this metric.

Cheapest move from month four is parallel evaluation. Keep the BairesDev engagement running on stable lanes. Open a 30-minute Devlyn discovery call. Run a 3-day free trial against the cross-cutting work. Decide based on effective output per dollar, not on rate cards or scale claims.

If you are running a $20M–$200M Series-A through Series-C IT organisation and the agency model is producing scale headcount with linear-addition velocity, the structural ceiling on retrofit AI-tool adoption compounds against you. Book a 30-minute Devlyn discovery call → — no contracts, no commitment. For retainer-grade engagements, the Standing Invitation is where briefs get sent.