Our AI-powered ODC model turns offshore delivery into a business-specific capability hub, combining agentic AI pods, domain-ready talent, and a Build-Operate-Transfer pathway that scales as a strategic extension of the GCC.
What the AI-native GCC delivers
3
strategic entry models
30–40%
faster time-to-hire
4
Talent readiness pillars
14
Global delivery locations
AI-powered capability creation: What CIOs and GCC leaders must do
Build capability and capacity: ODC pods are organized around product outcomes, engineering velocity, and governance readiness.
Design around agents and talent: AI agents handle repeatable engineering workstreams while the specialists at Brillio provide judgment, architecture, domain context, and accountability.
Use the right entry model: Dedicated ODC, BOT, and Assisted Captive GCC sit on a maturity curve that lets the enterprise choose the right balance of speed, control, transfer readiness, and risk.
Keep transition discipline visible from day one: Operating metrics, knowledge artifacts, governance cadence, and talent retention mechanisms are built for eventual continuity, not retrofitted at handover.
Three models on one capability curve
Dedicated ODC Model
Fastest path to business-specific capacity with Brillio-owned operations, dedicated pods, secure workspace, transparent governance, and scalable talent fulfillment.
BOT Model for GCC
Builds and operates the capability hub through a defined maturity runway, then transfers people, process, assets, knowledge, and governance to the client-owned GCC.
Assisted Captive GCC
Advisor-led model for enterprises that want maximum control from day one while using Brillio for framework design, operating model guidance, and flex capacity.
Dedicated ODC model
A secure, scalable, AI-powered capability hub operated by Brillio and aligned to the enterprise’s product roadmap.
Operating tenet: Dedicated pods with business ownership
Dedicated talent and teams: Engineering pods with clear ownership, structured onboarding, role-based skill mapping, and cultural ways of working aligned to the client’s product organization.
Secure enterprise workspace: 24×7 security, biometric access, CCTV, anti-passback controls, dedicated server rooms, and enterprise-grade physical controls.
Network and cyber security: Firewalls, IDS/IPS, VPN, RBAC, VDI security, SIEM, endpoint compliance checks, and vulnerability management across ODC environments.
Agile and SAFe delivery: SAFe-trained resources, distributed PI planning, sprint governance, milestone reporting, and delivery health index aligned to release cadence.
Engineering excellence: CI/CD pipelines, DevOps automation, test automation, real-time engineering insights, and high-performance engineering practices.
Governance and transparency: Program management, escalation paths, real-time dashboards, DR/BC planning, and communication protocols with onshore teams.
Dedicated hiring for the ODC
The hiring engine uses a rolling forecast to prioritize demand, assess available bench and buffer pools, source through digital channels, screen through standardized and AI-supported methods, and route talent into practice benches or project pods based on immediate and future requirements.
Brillio Academy: Readiness pipeline for business-specific ODC talent
The Brillio Academy creates predictable onboarding, upskilling, and cross-skilling by combining continuous quarterly feedback loops, customized training plans, and structured learning pathways. The framework produces business-ready ODC talent across four readiness pillars:
Tech ready: Tool stacks, toolchains, and product/program/project management tools.
Learning and assessment partners referenced in the framework include Udacity, Udemy, Coursera, LinkedIn Learning, Pluralsight, Percipio, Buddy Interview, Mettl, LeetCode, and CodeSignal. These partner ecosystems
support learning, evaluation, coding assessment, and readiness validation in a concise capability pipeline.
BOT Model for GCC
A disciplined Build-Operate-Transfer engine for enterprises that want long-term ownership, stronger IP control, and a risk-managed transition path.
Build phase: From intent to fully operational capability hub
Assess and blueprint: Assess the GCC operating model across engineering, IT operations, business processes, and data; evaluate AI maturity, governance, and readiness for scale; baseline productivity, cost, cycle time, and quality metrics; identify AI-ready value pools aligned to enterprise priorities.
Set up operations in parallel: Establish the operating model, secure workspace, IT infrastructure, governance cadence, talent strategy, and knowledge-transfer mechanisms while existing delivery continues without disruption.
Use AI to compress talent readiness: AI agents scrape, parse, and pre-screen technical candidates at scale, reducing time-to-hire by 30-40%.
Create the first readiness assets: GCC maturity scorecard, AI-readiness scorecard, target AI-native operating model, POD and AI CoE design blueprints, and prioritized agentic transformation roadmap.
Operate phase: From functional entity to high-performance engineering unit
Deploy AI-native pods: Operate pods and agentic workflows against a defined AI delivery lifecycle spanning discover, business case, implementation, engineering, assurance, and sustenance.
Embed agentic delivery: Use requirements, impact analysis, solution design, engineering, QA, deployment, and documentation agents orchestrated by a development orchestration agent with human-in-the-loop governance.
Scale reuse and governance: Expand agent libraries and shared platforms across engineering, data, and operations while embedding security, observability, and governance controls.
Track performance: Measure productivity, effort reduction, quality metrics, throughput, cost efficiency, and reusable AI assets as the capability matures.
Transfer phase: Capability handover with legal transition
Readiness assessment: Assess planning, governance, process, people, financials, go-live, and change management before handover.
Structured transition governance: Run steering committee, transition office, and daily delivery cadence to keep transfer visible and risk-managed.
Readiness assessment: Assess planning, governance, process, people, financials, go-live, and change management before handover.
Structured transition governance: Run steering committee, transition office, and daily delivery cadence to keep transfer visible and risk managed.
People transition: Move people in waves — leadership first, then senior engineers, then the broader team—with targeted retention levers for high-flight-risk talent.
Our AI-native GCCs are already moving the needle
Specialty chemicals: 40% cost reduction by Year 2, 30% order-processing efficiency gain, and 45% headcount reduction across 40+ countries.
Health services: GCC in India stood up as core innovation hub, with product and engineering ownership progressively transitioning offshore.
Telecom: 850+ India-based experts deployed on cloud-native platform, delivering NPS of 89 and AI innovation partner status.
Hiring engine: AI-agent-led screening and rolling-forecast sourcing compress time-to-hire by 30–40% across ODC and GCC pods.
What most GCC business cases still get wrong
Boards that evaluate GCCs on headcount savings will underwrite the wrong model and miss the innovation upside entirely. The AI-native advantage is capability creation, not seat arbitrage and that requires a different business case from the start.
What CIO and GCC leaders should ask next
ROI shifts from cost-per-seat to capability metrics—engineering velocity, agent-driven effort reduction, reuse of AI assets, and time-to-market gains that headcount arbitrage alone cannot deliver.
Flex capacity and pod-based structures absorb volatility better than fixed captive builds. BOT pauses or downshifts are contractually possible, but require modeling retention and infrastructure sunk costs upfront.
Transfers rarely fail on legal entity setup; they fail on tribal knowledge loss, mid-transition attrition of senior engineers, and governance cadence collapsing once the steering committee dissolves post-handover.
DPDP compliance, data residency, and cross-border transfer controls must be baked into the operating model at blueprint stage, retrofitting them after go-live inflates cost and delays scale.
Yes, but the Assisted Captive model is not the right starting point. First-time captive builders should begin with Dedicated ODC or BOT to inherit our delivery governance and reduce blind spots.
Expect targeted retention levers for high-flight-risk talent—leadership first, senior engineers next—with market-rate premiums explicitly modeled, not treated as rounding errors in the transfer P&L.
Forward-looking thoughts and compelling stories
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