We offer time-zone-aligned options (U.S., nearshore, and offshore). Teams work in your tools with overlapping hours for planning and reviews.
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AI GCCs and Engineering Pods are dedicated, cross-functional teams that deliver product features, platform modernization, and AI use cases. We stand up pods (design, FE/BE, data/ML, SRE) or an entire GCC with shared services—focused on outcomes, not just headcount.
We define a thin-slice roadmap, skill mix, and ways of working—Scrum/Kanban, CI/CD, and DORA metrics. Pods collaborate in your tools, code repos, and cloud accounts with feature flags, golden paths, and automated checks to keep quality and speed in balance.
A staffed pod/GCC with hiring pipelines, onboarding playbooks, design system alignment, observability, and runbooks. Weekly demos and velocity reports (cycle time, CFR, lead time) make progress visible. Shared accelerators reduce time-to-first-value for AI features.
Enterprise-grade controls: SSO/SCIM, RBAC/ABAC, SOC 2/ISO-ISO-aligned practices, VPC peering, and data residency by region. NDAs, IP assignment, and role-based access protect your assets. Change management, audits, and release gates ensure safe scale-up.
We offer time-zone-aligned options (U.S., nearshore, and offshore). Teams work in your tools with overlapping hours for planning and reviews.
SSO/SCIM, RBAC, least-privilege access, NDAs, and IP assignment are standard. We align with SOC 2/ISO practices and your security review.
Yes—pods cover design, engineering, QA, data/ML, and SRE. They ship features, maintain SLAs/SLOs, and manage on-call with runbooks.
DORA metrics, SLO attainment, customer/partner KPIs, and business impact (activation, retention, cost per ticket/task) are reported weekly.
Yes. We recruit and operate, then transition to your entity with knowledge transfer, process docs, and system access hand-off.
Initial pod in 2–4 weeks with core roles; scale to multiple pods/GCC functions in 8–12 weeks, depending on scope and clearances.
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