AI-first GCC
Design the team topology, workflows, platform architecture, governance, and run model for an AI-first Global Capability Center built to operate AI in production - not just experiment with it.
3-6 mo
to operational AI center
6 roles
core AI team topology
24/7
model monitoring baseline
AI-native
operating cadence
Traditional GCC operating models were designed for predictable, deterministic work - tickets, projects, releases. AI workloads are different. They are probabilistic, data-dependent, GPU-hungry, and require continuous evaluation. Trying to run them in a delivery model designed for application maintenance creates friction at every layer.
An AI-first GCC needs a deliberately different setup - role mix weighted toward data, ML, and AI engineering; workflows that handle experimentation, evaluation, and drift; platform ownership for compute, data, and MLOps; governance integrated into the lifecycle; and a run model that monitors models as carefully as applications.
NeoIntelli designs and stands up this operating model so the center can deliver AI in production from quarter one - not after 18 months of organisational learning.
Deliverables
01
Define roles - data engineers, ML engineers, AI app engineers, platform engineers, product owners, governance leads - reporting lines, and collaboration model with HQ and business.
02
Establish delivery cadences for experimentation, evaluation, deployment, and run - covering both classical ML and GenAI/LLM workloads.
03
Design the compute (GPU strategy), storage, data, feature store, model registry, and MLOps/LLMOps tooling stack tailored to your scale.
04
Decision rights, review gates, model approval workflows, risk classification, and escalation paths embedded into the delivery model.
05
Define how AI systems are monitored, retrained, supported, and improved post-deployment - including on-call, incident response, and cost discipline.
06
Daily standups, weekly evaluation reviews, monthly model and value reviews, quarterly governance and roadmap reviews tuned for AI delivery.
01
Translate AI strategy into team topology, workflow design, platform stack, and governance integration tailored to the mandate.
02
Hire core roles, configure the platform, establish workflows, instrument governance, and run a first end-to-end use case as the proof point.
03
Add capacity, expand use-case portfolio, mature MLOps, and embed evaluation and monitoring across every production model.
04
Move from project delivery to platform delivery - shared services, internal developer experience, and a portfolio operating cadence.
Standard delivery topologies do not include the data, ML, and platform roles AI needs. Hiring against them creates capability gaps.
Buying platforms before defining roles and workflows creates expensive shelfware.
GPU economics break AI programs that did not plan for them. Capacity, sourcing, and burst patterns need a deliberate design.
Models that ship without evaluation pipelines cannot be governed or improved.
Without on-call, monitoring, and retraining workflows from day one, production AI becomes brittle.
Late governance forces rework. Approval, classification, and oversight have to live in the delivery flow.
First production AI use case live within 90 days
Core AI team in seat with full role mix within 4-5 months
MLOps / LLMOps platform live with deployment, registry, and monitoring
Evaluation framework enforced for every production model
Governance integrated into delivery flow - not a separate review
Operating rhythm consistently running across weekly and monthly cadences
An AI GCC requires specialised roles (data, ML, AI app, platform), experimentation workflows, GPU infrastructure, evaluation pipelines, and governance models that traditional GCCs do not need. The operating cadence is also different - probabilistic systems need continuous evaluation, not just periodic releases.
Initial setup typically takes 3-6 months to first production use case, with full operational maturity over 12-18 months as the use-case portfolio, platform, and governance mature.
We design the team topology and role definitions, define competency frameworks, and support hiring strategy, interview kits, and onboarding. Hiring delivery can be supported via our specialist talent pods.
We are platform-agnostic. Common stacks include AWS SageMaker, Azure ML, Vertex AI, Databricks, MLflow, Weights & Biases, and emerging LLMOps tooling - chosen based on existing stack, scale, and governance needs.
Yes. This is one of the most common engagements - capability redesign, workflow migration, platform upgrade, and governance integration without disrupting current commitments.
We define a tiered compute strategy across cloud GPU, reserved capacity, and burst patterns - tuned to use-case mix, training versus inference balance, and cost discipline.
Through clear interfaces - product owners aligned to business units, shared platform services, API contracts, governance touchpoints, and a single value-realisation framework.
Yes. We can run a managed Build-Operate-Transfer model where NeoIntelli operates the center during stand-up and transfers ownership to the enterprise on an agreed schedule.
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