When you need one
A CoE makes sense when four things are true at the same time:
AI is becoming central to the business, not a peripheral experiment
You operate in a regulated industry where governance is non negotiable
You have engineers who should be building this capability, but lack the patterns and standards to do it safely
The alternative, every department buying its own AI tools, is already causing problems or soon will
What the CoE owns
Standards
How AI gets built here: evaluation frameworks, prompt management, model selection criteria, and coding patterns that every team follows.
Tooling
The shared infrastructure: orchestration layer, vector stores, evaluation harness, monitoring dashboards, and prompt versioning.
Governance
Review gates for production deployment, risk classification of use cases, audit trail requirements, and compliance evidence generation.
Capability
Training your engineers on the patterns, pairing with them on real use cases, and building the muscle memory that outlasts our engagement.
Use case pipeline
Intake, prioritisation, feasibility assessment and sequencing of AI opportunities from across the business.
How it unfolds
Foundation + first use case
Establish standards and tooling. Select and start the first use case, something real, not a proof of concept. Your engineers are in the room from day one.
Delivery with your engineers
Ship the first use case to production. Start the second. Your engineers are building alongside ours, absorbing patterns through the work, not through workshops.
Inversion
Your engineers lead; ours support. The ratio flips. New use cases are scoped and built primarily by your team using the standards and tooling that now exist.
Handover
We step back to advisory. Your CoE runs independently. We remain available for complex architecture decisions and periodic review, but the team is yours.
What we need from you
Executive sponsorship with authority to set standards across departments
At least two engineers committed to the CoE full-time from month one
Access to the business teams who own the use cases, not just IT
A willingness to ship real use cases, not just produce strategy documents
Cloud infrastructure and model API access provisioned under your accounts
Common questions
Isn't this just staff augmentation?
No. Staff aug gives you people. A CoE gives you standards, tooling, governance and capability transfer with a defined end state. Our goal is to leave, not to stay.
What happens if our engineers leave during the programme?
It slows things down but doesn't break them. The standards and tooling are documented and transferable. We've designed for turnover because it's a reality in Indian engineering teams.
Can the CoE cover data engineering as well as AI?
Yes, if the data work is in service of AI use cases. We wouldn't use this model for a standalone data warehouse programme; that's better served by Build Run Change.
How is this different from Forward Deployed Engineering?
FDE puts our engineers in your operation to build specific things. A CoE builds your team's ability to do this work themselves. The output of FDE is a system; the output of a CoE is a capability.
Build your AI capability, not just your AI systems
Talk to us about standing up an AI Center of Excellence inside your organisation.
Book a working session