Designed with you, staffed by your people, built to run without us.
We stand up an AI Center of Excellence inside your organization — the charter, governance model, delivery standards, evaluation discipline, and a trained operating team.
We run it alongside your people through the first production use cases, then hand it over. Built on our two engines: Aegis AI for how things get shipped, Human-Led AI for how your workforce adopts them. Fixed scope, one accountable lead, every artifact yours.
An AI Center of Excellence is the in-house team that sets your organization’s AI standards — which use cases get built, how they’re governed and evaluated, and how the rest of the company learns to use them — so AI capability accumulates inside the business instead of inside a vendor.
Just as important is what it isn’t: not a tools budget, not an innovation lab that ships nothing, and not a standing army of consultants.
A working CoE is a small set of roles, decision rights, and standards — which is why the right question isn’t “who do we hire to run our AI?” but “who builds the thing our own people run?” That second question is the service on this page. How we stand one up →
Every workstream below produces artifacts your team keeps — charters, policies, scorecards, playbooks — not slideware. The building of AI systems themselves is the CoE’s delivery arm, and it draws on our LLM development services rather than being re-sold here.
Where AI can actually pay in your business, and what blocks it — data, skills, risk posture, pilot sprawl. Runs as our AI readiness assessment, with the candid use-case ranking that comes with it.
Centralized, federated, or hub-and-spoke — decided from your org chart, not a template. Decision rights, funding model, intake process, and the escalation paths that make the CoE real on a Tuesday afternoon.
Acceptable-use policy, model-risk review gates, and human-oversight rules your legal and security teams co-author. Governance is our founding charter as a Responsible AI lab — the full depth is in the governance layer below.
A scoring pipeline from idea backlog to funded roadmap — ROI, feasibility, and risk weighted the same way for every department, so the loudest voice stops winning by default.
The Aegis AI process installed as the CoE’s build standard: evals before prompts, staged rollouts, human gates, production monitoring. The discipline that decides whether pilots become systems.
Champions program, role-based training, and adoption measurement — the Human-Led AI arm. A CoE that only governs is a brake; this workstream is the accelerator.
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.
What your team keeps — artifacts, not slideware
Because the alternative on this search results page is renting one. Much of what’s sold as an AI Center of Excellence is staff augmentation with a new name — engineers on your org chart, indefinitely. Our model only works if yours ends up running without us, and that changes everything about how it’s built.
Responsible AI is the founding charter. Silicon Prime was built around governance from day one — 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. A CoE has a delivery half and a people half. Aegis AI is our delivery discipline; Human-Led AI is our adoption and enablement practice. We’re not adapting a body-shop to the job.
Founder-led, one accountable lead. No account managers, no handoffs — one accountable lead as your single point of contact, and the founder answering for the engagement. The honest counter to “global 24/7 delivery.”
Built to transfer, contractually. Playbooks, standards, and IP assigned to you under full work-for-hire terms. The engagement’s stated end-state is your people running the CoE — a promise no rent-a-CoE vendor can make.
Accountability · who answers for yours. A center of excellence is an accountability structure — so it matters who answers for yours. Silicon Prime is run by founder Kelvin Tran: 20+ years of production engineering across enterprise and global technology environments, multimillion-dollar systems delivered for one of the world’s largest automobile manufacturers, M.S. Computer Science, and Advanced Program Management from Stanford University — the program-governance discipline a CoE operating model is made of. He is personally accountable for every engagement.
The engagement rhythm runs on our standard model — scoped phases reaching steady state in 4–8 weeks, under a single point of contact. The transfer at stage six isn’t an exit clause; it’s the design objective the other five stages serve.
Where AI can pay, what’s blocking it, and an honest inventory of every pilot currently running in the building — including the ones nobody admits to. Ranked by ROI, feasibility, and risk.
Output: pilot inventory + opportunity map
Data, platform, and security baseline — what the first use cases can stand on today versus what needs building. The model-agnostic vendor posture is set here, with evaluation across all three frontier engines rather than a default to anyone’s favorite.
Output: data + platform baseline
Governance structure, decision rights, intake process, funding model, 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
One to three prioritized use cases shipped under the CoE’s own delivery standard — proving the model on real work, not a sandbox. At a 200+ location restaurant chain, release cadence quadrupled and held a four-year zero-critical-defect record — see the operating record below.
Output: 1–3 systems live, under the new standards
Champions trained, playbooks documented, and the second wave of use cases intaken and scored by your own team while we shadow. Adoption is measured, not assumed — with humans in the loop wherever systems act.
Output: your people running intake
ROI instrumentation, oversight cadence, and the formal transfer of ownership — runbooks, standards, and eval suites in your team’s hands, with overlap support through the transition. A product we built in 2012 is still in production 12+ years later, operated by the client’s own team. We step back to advisory, or all the way out.
Output: ownership transferred, advisory optional
The transfer at stage six isn’t the exit — it’s the design objective the other five stages serve.
A working center of excellence is six roles with decision rights, not a fifty-person AI department. Here’s the split between what we staff during stand-up and what we transfer to your people — because the second column is the whole point.
Designs the charter and operating model; your single accountable contact.
Example: senior people who build the machine — and work themselves out of it.
Ship the first use cases under the new standards, pairing with your team.
Example: senior people who build the machine — and work themselves out of it.
Builds the review gates, eval harnesses, and policy drafts your owners inherit.
Example: senior people who build the machine — and work themselves out of it.
Runs the champions program and role-based training tracks.
Example: senior people who build the machine — and work themselves out of it.
Owns the mandate and the budget; the charter is signed in their name.
Example: the people who own it on day one after we step back.
Your operator — runs intake, prioritization, and the review cadence.
Example: the people who own it on day one after we step back.
Inherits the policy and review gates; the name on the audit trail.
Example: the people who own it on day one after we step back.
The adoption network — trained, certified, and measured.
Example: the people who own it on day one after we step back.
A CoE’s promises are about discipline, longevity, and ownership — so judge ours on those three, with named clients and public links. These engagements weren’t labeled “CoE”; they’re the component disciplines a CoE institutionalizes, running in production.
Discipline, longevity, ownership — the same three things your CoE is for. Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran, personally accountable for every engagement. Book a scoping call →
Anywhere AI needs to clear a regulator, an auditor, or a risk committee before it ships, the CoE’s governance layer stops being overhead and becomes the thing that unlocks deployment at all.
HIPAA makes governance mandatory before the first model touches patient data — a CoE turns that from a per-project scramble into a standing capability: review gates, PHI boundaries, and audit-ready logs every team inherits. Healthcare software →
Model-risk management is already a regulatory discipline here; the CoE extends it to AI — evaluation gates, decision audit trails, and policy your compliance team co-owns from day one. Fintech software →
The risk isn’t a regulator, it’s scale: pricing and recommendation systems touching revenue on every session. A CoE gives experimentation shared guardrails, so teams move fast without re-learning the same incident. Ecommerce software →
Every pillar below ships as a working artifact — a policy your lawyers have read, a gate your engineers can’t skip, a name on every decision. Controls are designed and mapped to the frameworks your auditors already use.
Acceptable use & policy. What AI may touch, what it may never touch, and who decides the gray cases — written with your legal and security teams, versioned like code.
Data boundaries. What leaves your perimeter and what never does: tenant architecture, retention rules, and vendor data-use terms documented per system, so a security review verifies instead of trusts.
Evaluation gates. No model ships without passing its evals — accuracy, bias, and regression measured against golden sets built from your data, with results filed where an auditor can find them.
Human oversight & accountability. Named owners for every system, human-in-the-loop checkpoints where actions have consequences, and an escalation path that ends at a person, not a ticket queue.
Framework mapping. Controls mapped to SOC 2, HIPAA, ISO/IEC 42001, and the EU AI Act as applicable — designed to those frameworks from day one, so a future audit or certification effort starts from documented controls instead of a rebuild.
Straight answers — the same ones you’d get on the call.
A pilot team proves one use case; a CoE makes the second, fifth, and twentieth cheaper and safer than the first. The honest test: if you have more than two AI initiatives running, or any AI touching regulated data or customers, you already need shared standards — you’re just paying for them per-project right now. One pilot with no sequel planned? Start with the pilot, and we’ll say so.
It follows your org, not a framework diagram. Centralized fits when AI maturity is low and risk is high — one team owns everything. Federated fits strong autonomous business units — the CoE sets standards, units deliver. Hub-and-spoke is the common landing zone: a small central team owning governance, evals, and platform, with trained champions embedded in each unit. The charter workstream settles this in week one, from your org chart and risk posture.
The charter, governance gates, and first-use-case scoping reach steady state in our standard 4–8 week engagement rhythm. The operate-alongside phase then runs through your first one to three production use cases — that’s use-case dependent, and we won’t pretend otherwise. Transfer happens on a readiness trigger your sponsor signs off, not on a calendar date we invented to win the deal.
Smaller than the consultancies say. A CoE is roles and decision rights, not headcount — a six-person CoE with a signed charter and working eval gates beats a sixty-slide operating model every time. If you’re mid-market with three departments experimenting with AI separately, you’re the textbook case. What actually gates it is an executive sponsor willing to own the mandate.
It’s the core of the build, not a feature. Every engagement starts under NDA with a security review; systems run in your own cloud tenant under your access controls; and the governance layer — acceptable-use policy, evaluation gates, human oversight, data boundaries — is co-authored with your legal and security teams. Controls are designed and mapped to SOC 2, HIPAA, ISO/IEC 42001, and the EU AI Act as applicable to you.
You do, in writing: every charter, policy, playbook, eval suite, and line of code transfers under full work-for-hire IP assignment signed at kickoff. The one exception is our underlying Aegis AI methodology, which is patent-pending and licensed to you for use within your organization. And the dependency worry runs backwards here — the engagement is designed around the handover. At BJ’s Restaurants we didn’t replace the team or the stack; we restructured how work flows through it. That’s the model: your people, more capable, holding the keys.
Each phase is separately scoped and fixed-fee, with payment tied to ROI under our standard engagement model — the exact structure is set out in the proposal against the success metrics the charter defines. Build costs for the first production use cases follow our published AI development cost guide. The number nobody prices: what the current pilot sprawl is already costing you per quarter. The readiness audit puts a figure on that, too.
You stop at the next phase gate and keep everything. Because each phase is separately scoped, there is no long contract to escape — if the first use cases miss their metrics or the sponsor changes, you exit with every artifact produced to date: the charter, the policies, the eval suites, the trained people. Payment tied to ROI means we share that downside rather than billing through it. The readiness audit exists precisely to surface the deal-breakers before you’ve spent real money.
Thirty minutes · No pitch deck
Bring your pilot inventory. We’ll tell you honestly whether you need a center of excellence or just a better-run pilot, what standing one up takes, and what the transfer looks like at the end.