AI Consulting in Silicon Valley

Large Silicon Valley technology companies typically run AI tool adoption through a governance layer that includes security review, legal sign-off on data processing agreements, and sometimes an internal AI ethics committee before any external capability gets approved for production use. We build for that process rather than around it. Every engagement starts with a written data-flow map — what data the capability touches, where it moves, what model endpoint processes it, and what the retention posture is on that endpoint. For enterprise clients, we default to zero-retention model contracts with Anthropic or Azure OpenAI enterprise tiers, scoped API credentials with least-privilege access, and a deployment architecture that can run inside a corporate VPC if the security team requires it. The audit deliverable is specifically designed to be the artifact that moves through your vendor-approval pipeline, not a sales deck.

Fixed-price, fixed-scope builds — no open-ended retainer required to get started. The $99 AI readiness audit is the entry point: it produces a written report that identifies the two or three operational workflows leaking the most time or revenue, ranked by ROI and deployment complexity. If the audit surfaces a clear target, we scope a fixed-price capability build — typically in the range of a few weeks of work, priced upfront before any code is written. You know the number before you commit. We don't charge hourly and we don't do "phase one of an ongoing engagement" language designed to expand scope after the first invoice. If the build needs to be staged because of budget timing, we scope stage one so it delivers standalone value on its own. Cap table pressure is a real constraint and we'd rather scope something you can actually ship than propose something that stalls in a finance committee.

Yes, and we build compliance in rather than layering it on afterward. For law firm clients — including BigLaw branch offices and boutique tech-practice firms operating in Palo Alto and along Sand Hill Road — every capability that touches client matter data is routed through model endpoints with signed zero-retention data processing agreements. That satisfies the confidentiality requirements under ABA Model Rule 1.6. Ethical walls inside document management systems like iManage or NetDocuments propagate to the AI layer, so a build for one practice group can't access matter data the attorney couldn't see manually. On Model Rule 5.3 (supervision of nonlawyer assistance), the builds we deliver keep a licensed attorney in the review loop at every output stage — the AI drafts, the attorney approves before anything leaves the firm. The written documentation we hand over at go-live is specifically formatted for review by a firm's general counsel or ethics committee, not just its IT department.

Stanford's technology licensing office and the biotech companies spinning out of Stanford Medicine carry IP sensitivity that's different from standard enterprise SaaS clients. Licensing agreements, patent prosecution documents, and FDA regulatory submissions are all materials where confidentiality isn't just a preference — it's a legal and competitive requirement. Our standard architecture for life-sciences and spinout IP clients uses zero-retention enterprise model endpoints, keeps all document processing within a defined network perimeter (on-prem or VPC), and never indexes source documents to a third-party vector store without explicit written approval from the client's IP counsel. We also map the specific document types the capability will touch before any access is granted — so the scope of what the AI layer can see is documented and approved before the first API credential is provisioned, not discovered during a security review six weeks later.

At that headcount, the most common operational breaks are: (1) KPI reporting that's still manual — someone exports data from three systems every Friday and builds a deck that goes to the leadership team; (2) customer onboarding that's underdocumented and dependent on two or three people who've been there since early days; and (3) internal knowledge that lives in Slack threads and Notion pages that new hires can't actually find. The $99 audit will tell you which of those is costing the most time per week. The KPI snapshot automation typically ships in two weeks and eliminates a recurring Friday afternoon for whoever owns it. The knowledge-onboarding build typically takes three to four weeks and reduces ramp time for new hires meaningfully without requiring a headcount change. We scope one capability, build it completely, and hand it to your team with documentation — not a roadmap for twelve more things we want to sell you.