Service · AI · Est. 2011
An AI Center of Excellence — designed with you, staffed by your people, built to run without us.
We stand up an AI Center of Excellence inside your organization — charter, governance, delivery standards, evaluation discipline, and a trained team — run it through your first production use cases, then hand it over.
Build · operate · transfer — to your people
YOUR TEAM KEEPS
What it is
The in-house team that sets your organization's AI standards.
An AI Center of Excellence decides which use cases get built, how they're governed and evaluated, and how the company learns to use them — so capability accumulates inside the business, not a vendor. Not a tools budget, not an innovation lab that ships nothing, not a standing army of consultants.
It's a small set of roles, decision rights, and standards. The question isn't "who do we hire to run our AI?" but "who builds the thing our own people run?"
Of enterprises report no EBIT impact from AI at all — what scattered pilots structurally cannot produce, since each starts from zero.
McKinsey, 2025 ↗
The number of times each cost should be paid — one vendor evaluation, one set of guardrails, one harness, one owned path to production, reused by every team after.
The CoE thesis
Teams that should start from scratch — every vendor evaluation, guardrail, harness, and path to production is built once, then inherited by the next.
Capability accrues in-house
What we stand up
The seven workstreams of a working AI center of excellence.
Every workstream produces artifacts your team keeps — not slideware.
AI readiness & opportunity audit
Where AI can pay and what blocks it — data, skills, risk, pilot sprawl — with a candid use-case ranking.
Charter & operating model
Centralized, federated, or hub-and-spoke — decided from your org chart, not a template — with decision rights, funding, intake, and escalation paths that make the CoE real.
Responsible-AI governance & policy
Acceptable-use policy, model-risk gates, and human-oversight rules your legal and security teams co-author — governance is our founding charter.
Use-case portfolio & prioritization
A scoring pipeline from backlog to funded roadmap — ROI, feasibility, and risk weighted the same way for every department, so the loudest voice stops winning.
Pilot-to-production delivery standard
The Aegis AI process installed as the CoE's build standard: evals before prompts, staged rollouts, human gates, production monitoring — what decides whether pilots become systems.
Workforce enablement & AI literacy
Champions program, role-based training, and adoption measurement — the Human-Led AI arm. A CoE that only governs is a brake; this is the accelerator.
Platform foundations & vendor posture
Model-agnostic evaluation across OpenAI, Anthropic, and Google, plus the shared eval harness and abstraction layer that keep every team's work portable when the vendor landscape moves.
Delivery arm & build capacity
Building the AI systems is the CoE's delivery arm — drawing on our LLM and AI development services, so the standards aren't theory but the way real use cases ship.
What your team keeps — artifacts, not slideware
How we stand one up
Six stages, ending in your hands.
Scoped phases reaching steady state in 4–8 weeks, under one point of contact — the transfer at stage six is the design objective, not an exit clause.
STAGE 01
Readiness audit
Where AI can pay, what's blocking it, and an honest inventory of every pilot running — ranked by ROI, feasibility, and risk.
Output: pilot inventory + opportunity map
STAGE 02
Foundations check
Data, platform, and security baseline — what the first use cases can stand on versus what needs building. Model-agnostic vendor posture set here.
Output: data + platform baseline
STAGE 03
Charter & operating model
Governance structure, decision rights, intake, funding, and review cadence — signed by the executive sponsor. The difference between a CoE and a committee is that this document has teeth.
Output: the CoE exists — on paper and in calendars
STAGE 04
First use cases to production
One to three prioritized use cases shipped under the CoE's delivery standard — real work, not a sandbox. At a 200+ location chain, release cadence quadrupled and held a four-year zero-critical-defect record.
Output: 1–3 systems live, under the new standards
STAGE 05
Enable & expand
Champions trained, playbooks documented, the second wave of use cases scored by your own team while we shadow. Adoption measured, not assumed — humans in the loop wherever systems act.
Output: your people running intake
STAGE 06
Measure & hand over
ROI instrumentation, oversight cadence, and formal transfer of ownership — runbooks, standards, and eval suites in your team's hands. A product we built in 2012 is still in production 12+ years later, run by the client's own team.
Output: ownership transferred, advisory optional
Who sits in it
Six roles with decision rights, not a fifty-person department.
The split between what we staff at stand-up and what transfers to your people.
We staff at stand-up
CoE architect / engagement lead
Designs the charter and operating model; your single accountable contact.
Delivery engineers
Ship the first use cases under the new standards, pairing with your team.
Governance & evaluation designer
Builds the review gates, eval harnesses, and policy drafts your owners inherit.
Enablement lead
Runs the champions program and role-based training tracks.
Your people, at transfer
Executive sponsor
Owns the mandate and the budget; the charter is signed in their name.
CoE lead
Your operator — runs intake, prioritization, and the review cadence.
Governance owner
Inherits the policy and review gates; the name on the audit trail.
Business-unit champions
The adoption network — trained, certified, and measured.
Track record
A CoE's promises are discipline, longevity, and ownership — so judge ours on those three.
Not labeled "CoE," but the component disciplines a CoE institutionalizes, running in production.
Run by founder Kelvin Tran — 20+ years of production engineering, multimillion-dollar systems for one of the world's largest automakers, M.S. Computer Science and Advanced Program Management from Stanford. Personally accountable for every engagement.
Discipline · 200+ locations · 4 years
BJ's Restaurants — operating discipline installed and held: release cadence quadrupled to twice a week with a sustained four-year zero-critical-defect record. The delivery standard a CoE makes the house rule.
Longevity & ownership · since 2012
Bridge Athletic — a product we built in 2012 is still in production 12+ years later, run by the client's own team, now used by USC, the LA Rams, and MLB/MLS teams. The proof that build-operate-transfer actually transfers.
Money-critical delivery · acquired 2017
YardClub — marketplace and payments infrastructure built end to end; $120M+ processed, acquired by Caterpillar — the delivery rigor a CoE standardizes for every team after.
Why hire a lab to build a thing you'll own.
Because the alternative is renting one — much of what's sold as a CoE is staff augmentation with a new name. Our model only works if yours runs without us.
Responsible AI is the founding charter. A Stanford-rooted Responsible AI lab, founded 2011. For a CoE, governance isn't the add-on — it's the product.
Two engines, two halves of a CoE. Aegis AI is our delivery discipline; Human-Led AI our adoption practice. We're not adapting a body-shop to the job.
Founder-led, one accountable lead. No account managers, no handoffs — one point of contact, the founder answering for the engagement.
Built to transfer, contractually. Playbooks, standards, and IP assigned to you under full work-for-hire. The end-state is your people running the CoE — a promise no rent-a-CoE vendor can make.
Questions buyers ask before they commit.
Thirty minutes · no pitch deck
Ready to build an AI capability your own people run?
Bring your pilot sprawl and your AI ambitions — we'll inventory what's running, tell you honestly whether you need a CoE yet, and scope the stand-up around the handover from day one.