Hire a Dedicated AI Development Team
ยท Typical sprint one launch: 2 to 3 weeks
Reviewed by Javier Uanini, CEO and delivery lead at Siblings Software.
AI product roadmaps often fail where deployment risk, evaluation uncertainty, and delivery ownership collide. Our dedicated nearshore AI squads from Cordoba, Argentina are built to run production-grade execution with measurable quality gates and practical release governance.
This page explains who should buy a dedicated AI squad, what composition works, how pricing bands map to scope, and how we use a production model readiness check to avoid expensive false starts.
Buyer fit and commercial scope
Dedicated AI squads are best for roadmap slices where model behavior is core product value.
Strong fit
Products that need sustained model iteration, measurable evaluation discipline, and coordinated engineering delivery across app and platform layers.
Better served by augmentation
If you already have senior internal AI leadership and only need extra implementers, staff augmentation may offer better economics and less overhead.
Production readiness scope
We gate delivery on readiness fundamentals before scaling model exposure.
Evaluation confidence
Known datasets, measurable quality thresholds, and explicit release accept criteria.
Cost guardrails
Practical controls on request-level spend and runtime behavior before broad rollout.
Operational governance
Human-review paths, escalation ownership, and release-risk observability from day one.
Squad composition
Lean pod
Technical lead plus AI and product engineering support for focused roadmap phases.
Product squad
AI lead, engineers, QA support, and delivery management for stable sprint execution.
Program setup
Multiple aligned squads when model platform and product streams must evolve in parallel.
Engagement and pricing bands
Lean pod
USD 12,000 to 22,000 per month. Focused delivery where internal stakeholders remain hands-on.
Product squad
USD 24,000 to 42,000 per month. End-to-end execution with stable release and governance rhythm.
Program
USD 45,000 to 60,000 plus per month. Multi-stream scope with platform dependencies and tighter controls.
First 30 days
Days 1 to 5
Discovery, readiness check, and architecture baseline.
Days 6 to 12
Team onboarding, tooling alignment, and quality criteria lock.
Days 13 to 21
Sprint zero with evaluation and observability flow in place.
Days 22 to 30
Sprint one release outcomes and governance calibration.
Mini case study
Evaluation discipline reduced production ambiguity in a legal AI workflow
Composite benchmark based on recurring AI delivery patterns.
A legal workflow product needed to improve model reliability before broad launch. A dedicated AI squad established evaluation thresholds, release gates, and production observability. Model updates shifted from ad-hoc experiments to controlled sprint outcomes with clearer stakeholder confidence.
Comparison against alternatives
Vs in-house hiring
In-house remains strategic long term, but dedicated squads reduce time to impact when roadmap urgency is immediate.
Vs freelancers
Freelancers can solve isolated tasks. They rarely provide full-team governance and sustained release accountability.
Vs staff augmentation
Augmentation fits mature internal leadership. Dedicated squads fit buyers who need complete stream ownership.
Frequently Asked Questions
A typical AI squad includes a technical lead, AI or ML engineers, product or backend engineers, QA support for evaluation workflows, and delivery management. The team works in your tools with explicit sprint and release accountability.
Lean pods usually run USD 12,000 to 22,000 per month. Product squads generally run USD 24,000 to 42,000. Multi-pod initiatives with platform support typically run USD 45,000 to 60,000 or more.
Discovery usually takes three to five business days, team setup takes five to ten days, and sprint zero starts in week two or three. Initial production outcomes typically land around week four.
We run a practical readiness check on evaluation data, cost controls, and human review requirements. This prevents teams from scaling model features before quality and governance fundamentals are stable.
Choose staff augmentation if your internal AI leadership is mature and you only need additional execution capacity. Choose a dedicated team when you need full-stream ownership of delivery rhythm, quality gates, and release governance.
Yes. Teams in Cordoba, GMT-3, overlap substantially with US Eastern and Central time zones, enabling fast feedback loops and same-day operational decisions.
We define measurable release criteria, evaluation thresholds, and escalation paths before launch. Stories do not close until agreed quality checks and observability controls are in place.
OUR STANDARDS
Measured quality gates, clear ownership, and practical production discipline.
AI delivery quality is earned through explicit release criteria and accountable ownership. We prioritize production stability, measurable evaluation discipline, and transparent governance over demo-driven momentum.
Contact Siblings Software Argentina
Tell us about your AI roadmap, model risks, and release constraints. Prefer the US host? Use siblingssoftware.com for the .com version of this page.