A prioritized, ROI-backed roadmap you can actually fund.
We help enterprise leaders decide where AI pays, what to build versus buy, and in what order — then hand you a sequenced, costed roadmap with governance designed in from day one.
Strategy grounded in what your data and teams can really support, from a Stanford-rooted Responsible AI lab that also ships the work. You own the plan; take it to any partner, or have us deliver it.
Because most organizations buy AI before they have a strategy for it. Tools get adopted, pilots multiply, and the budget grows — but nothing reaches the bottom line.
The differentiator isn’t the model, it’s redesigning the work around it. The cost isn’t only the wasted spend — it’s the projects that quietly die, killed by poor data quality, escalating cost, and unclear business value.
A good AI strategy is the cheapest insurance against being in that 30%: it decides what’s worth building before you spend on building it. That is the entire purpose of AI consulting services — to make those calls deliberately, with evidence, before the budget is committed.
AI consulting isn’t a slide deck about “the future of AI.” It’s a set of specific, fundable decisions, each one removing a way an initiative typically fails. For each: what it does, the benefit it produces, and how that plays out.
Surfaces candidate use cases across your functions and ranks them on impact versus feasibility, so the first project is the one most likely to return. Benefit — capital goes to the work that pays, not the work that demos well.
Example: a team set on a flashy customer-facing copilot finds in prioritization that an unglamorous invoice-matching automation has triple the ROI and a tenth the risk — so that ships first and funds the rest.
Scores building in-house against off-the-shelf tools on cost, control, switching risk, and time-to-value — structured to be free of any bias toward us building it. Benefit — you avoid both the “rebuild what you could have bought” trap and the “locked into a vendor that can’t do the job” trap.
Example: a company about to commission a custom forecasting engine learns a configured platform covers 90% of the need — and the build budget moves to the 10% that’s genuinely differentiating.
Turns the prioritized use cases into a phased plan tied to business outcomes, with dependencies, milestones, and the order that compounds value. Benefit — a fundable plan with a defensible sequence instead of a scatter of disconnected pilots.
Example: rather than running five pilots at once and scaling none, the roadmap stages the data foundation first so every downstream use case lands faster.
Costs each initiative — build, run, and the token/infrastructure economics — against projected value, so the case survives a CFO’s scrutiny. Benefit — leadership funds AI on numbers, not enthusiasm, and the first invoice is a forecast they’ve already seen.
Example: an item modeled at a 9-month payback with usage-scaling run costs gets board approval knowing exactly what “good” looks like and when.
Defines oversight, accountability, model-risk controls, and audit trails before the first model ships — not as a compliance afterthought. Benefit — initiatives that clear legal and risk on the first pass, and a board that can answer “is this safe?”
Example: a regulated lender gets decision-logging and human-review thresholds written into the roadmap from the start, so the eventual build is approvable instead of stuck in review.
Each decision above rests on whether your data and systems can actually support the plan — the diagnostic we run as a dedicated AI readiness assessment when the gap needs auditing in detail. Benefit — a strategy grounded in what you can support, not what a deck assumes.
Example: a roadmap that looked fundable on paper surfaces a data-quality gap in the assessment — so fixing the foundation becomes step one instead of a stalled surprise at build time.
The consulting engagement is advisory: the deliverable is a set of decisions and a plan you own and can fund — distinct from the build work that may follow. The scope, line by line.
Translating where you want AI to take the business into objectives an initiative can be measured against — so “use more AI” becomes a target with a number on it.
Ranking candidate use cases across functions on impact and feasibility, so the sequence is defensible and the first project is the one most likely to return.
Scoring in-house build against off-the-shelf tools on cost, control, and switching risk, free of implementation bias since the recommendation isn’t steered by who builds it.
A phased, costed roadmap tied to business outcomes, with build and run economics modeled so leadership funds it on numbers rather than enthusiasm.
Oversight, model-risk controls, accountability, and audit trails defined up front, drawn from our Human-Led AI practice, so governance is part of the strategy rather than a later scramble.
The roadmap is yours to execute with any partner, or the same team carries it into the build via our AI development services with no re-scoping.
What you get when you hire us — all documented and owned by you
The same delivery discipline behind all our AI development work, applied to advisory — one accountable lead, fixed scope, no handoffs between strategists and a sales team.
We evaluate readiness across data, systems, skills, and governance, and pin down what the business is actually trying to achieve.
Output: a grounded picture of what AI you can support today
We surface and rank use cases on impact and feasibility, and run the build-or-buy call on each.
Output: a ranked portfolio & a build-or-buy decision per initiative
We sequence the priorities into a phased, costed plan with a Responsible-AI governance framework and an ROI model.
Output: a fundable roadmap you own
Take the roadmap to any partner, or have the same lead carry it into the build under full work-for-hire IP assignment.
Output: implementation, owned by your team or ours
The advisory engagement is scoped with you up front and reaches a roadmap on a fixed timeline — build engagements that follow typically reach steady state in 4–8 weeks.
The case for any AI consulting services firm is only as strong as its ability to deliver what it recommends. We advise and build, so the roadmap is written by people who’ll be accountable for making it work. The record behind the advice:
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll name the trade-offs plainly and tell you when not to build something, which a firm paid only to implement won’t.
We build what we recommend. Roadmaps come from a team that ships production AI, so the plan is grounded in delivery reality, not theory — and the build-or-buy call is honest because we’d be the ones building it.
Responsible AI is the founding charter. Governance, accountability, and model risk are designed into the strategy from day one, drawn from our Human-Led AI practice — not retrofitted to pass an audit later.
You own the plan, with no lock-in. The roadmap, the decisions, and the ROI model are yours; take them to any partner or have us deliver them.
Founder-led, one accountable lead. No account managers, no handoff from the strategist who scoped it to a team that didn’t — the person who writes the roadmap answers for it.
Our AI consulting services carry the most weight where regulation and risk make sequence and governance decisive:
Prioritizing AI where it clears HIPAA and clinical-risk review on the first pass, with governance and audit trails sequenced into the roadmap from the start. Healthcare software →
Build-or-buy and roadmap work for fraud detection and real-time decisioning, where model-risk controls and explainability are part of the plan, not a bolt-on. Fintech software →
Sequencing recommendation, dynamic-pricing, and post-purchase use cases by ROI, so the highest-return work ships first. Ecommerce software →
What teams want to know before they bring in an AI consultant.
A prioritized portfolio of use cases, a build-or-buy decision on each, a sequenced enterprise roadmap tied to business outcomes, an ROI model, and a Responsible-AI governance framework — all documented and owned by you. Our AI consulting services cover strategy through roadmap; building what the roadmap calls for is a separate, optional step you can give to any partner.
The readiness assessment is the diagnostic — a structured audit of whether your data, systems, skills, and governance can support AI. Consulting is the broader engagement that uses that diagnostic to decide what to build, in what order, and whether to build or buy it. The assessment answers “are we ready?”; consulting answers “what should we do, and what will it return?” Many engagements start with our AI readiness assessment and continue into the full roadmap.
Both — your call. The roadmap is yours to execute with any partner, with no lock-in. If you’d rather not re-scope with a new vendor, the same accountable lead carries it into delivery through our AI development services under full work-for-hire IP assignment. The advice doesn’t depend on you hiring us to build.
We score each initiative on cost, control, switching risk, and time-to-value against both off-the-shelf options and a custom build — and because the recommendation isn’t tied to who implements it, “buy” and “don’t build this yet” are real outcomes we reach often. The aim is to stop you over-building what you could configure, and under-building what’s genuinely differentiating.
No — finding those is the work. We assess your data and systems as the first step and surface the use cases for you; if the data foundation isn’t ready, sequencing that fix is the first item on the roadmap. Starting before you have it all figured out is normal and expected.
It’s part of the strategy from day one, not a compliance afterthought. We define oversight, model-risk controls, accountability, and audit trails as the roadmap is built — drawn from our Responsible AI practice — so initiatives clear legal and risk review on the first pass instead of stalling in it. This matters most in regulated sectors like healthcare and fintech.
Speed and honesty. An internal team is close to the politics and rarely free to say “don’t build the executive’s pet project.” An outside lab that also ships brings pattern recognition from real deployments and the independence to rank initiatives on ROI, not on who asked for them. It’s the difference between AI consulting services that challenge the plan and a team that rubber-stamps it.
The advisory engagement is fixed-scope and scoped with you up front; timeline scales with the complexity of your use cases and data, and you’ll know it before any work starts. Build engagements that follow typically reach steady state in 4–8 weeks. For build economics, our AI development cost guide gives real ranges.
Thirty minutes · No pitch deck
Tell us where you want AI to take the business — we’ll assess readiness, name the trade-offs, and give you a prioritized, ROI-backed roadmap you can take to your board or to any partner.