Why a Center of Excellence
Ops, finance, and marketing are already building with AI.
Analysts, product leads, and engineers across your organization are creating working apps with AI in days. That momentum is worth protecting. A Center of Excellence is how you keep it, by giving builders a safe way to work and giving you a live picture of what exists, what it touches, and what changed in the last release.
NEKOD runs the programme as four modules. Enablement comes first, because governance holds when your builders are on side. Discovery, governance, and monitoring then follow the work rather than blocking it.
01 · Enable
Your people learn to build well before they build at scale.
Enablement is where a Center of Excellence earns permission to exist. Builders who learn secure patterns early produce apps that need far less correction later, and they become the people who advocate for the programme internally.
Sessions are shaped by role rather than by tool. Developers go deep on data access and secrets handling. Analysts and product leads focus on what makes an app safe to put in front of real users. Executives get the short version: what your teams can now do, and what you are accountable for.
A trained builder community, a secure-patterns playbook in your own stack, and named sponsors who understand the programme.
02 · Discover
Find every app your organization has already built.
You cannot govern what you cannot see. Discovery connects to the places work actually happens, repositories, cloud workspaces, and the AI builders your teams have adopted, and assembles a single inventory with a named owner against every app.
The inventory stays current on its own. New apps appear as they are created, ownership follows people as they move, and the picture does not decay between reviews the way a point-in-time audit does.
A defensible, continuously updated inventory of every AI-built app, with owners, criticality, and data classification attached.
03 · Govern
Decide your own risk tolerance, then let it apply itself.
Every app arrives in the portfolio already sorted by what it actually does, who uses it, and what data it touches. Nobody triages by hand, and nobody applies a payments-grade control set to an internal dashboard.
Guardrails attach themselves to the zone rather than to the app, so a policy decision made once holds across everything that follows. Approvals route to the people who own the risk, and every decision is logged as evidence without anyone assembling a pack.
Tuned risk zones, an attached policy library, routed approvals, and a Target Operating Model your risk function recognises.
Portfolio overview
87 apps · 6 teams
- Coverage
- 96%
- In prod
- 72/87
- Waiting
- 12
Scope
Status, owner, and data
- 72Auto-deployed in productionIn production
- 12Waiting approvalWaiting approval
- 3Information requiredInfo required
In scope
4 apps
| Status | App | Owner | Data |
|---|---|---|---|
| Waiting approval | Refunds consoleWaiting approval | Owned by Maya Chen | Sensitive · Payment data |
| Info required | Partner portalInfo required | Owned by Luis Ortega | Sensitive · Personal data |
| In production | Expense botIn production | Owned by Priya Shah | Internal only |
| Info required | Onboarding kitInfo required | Owned by Tom Hale | Sensitive · Employee PII |
- GreenRuns light
- AmberStandard checks
- RedFull set + review
04 · Monitor
Apps do not stop changing. Neither does the oversight.
A launch check tells you about one moment. Most of the risk in an AI-built app arrives in the changes that come after it, when a feature starts touching customer data or a dependency quietly changes behaviour.
Monitoring runs the zone's control set on every release and reports the delta. Green stays the resting state, so when something surfaces it means something, and your team spends attention where it changes an outcome.
Continuous release testing, risk trends over time, and standing evidence for DORA, NIS2, and the EU AI Act.
Where most programmes start
Run an internal hackathon. Watch the landscape appear.
Your employees build with AI for a day and check their apps in NEKOD for free. By the end of it you are not looking at a proposal, you are looking at your own portfolio: real apps, real owners, sorted by real risk. Governance is then a short review of defaults, not a six-month programme.
We built the hackathon scoring platform for Teens in AI in under a week, covering submissions, Q&A, scoring, and winner promotion. We run these from both sides.
Built for enterprise
The foundations that make org-wide adoption straightforward
Single sign-on and identity
Deployment flexibility
Roles and access
Portfolio and multi-team scope
Audit and evidence
Integration with your internal platforms
Start where your builders already are
Stand up your AI Center of Excellence.
A short session on what your teams are building today, which modules you need first, and what the first ninety days look like.