AI Consultant for US Companies

Why US Companies Hire a Production AI Systems Architect

AI models are improving every month. AI systems are still failing every day. The model is rarely the problem - the architecture usually is. Most US companies that reach out have the same story: a promising AI pilot that works in demos but breaks with real users, real data volumes, or real compliance review.

Nic Chin designs and ships production AI systems for exactly that gap. Two live SaaS products built as sole architect - SureCiteAI (multi-tenant document intelligence RAG) and SystemAudit (codebase intelligence). The accuracy claims behind those products are auditable against public benchmarks; the methodology is on the proof page. Independent verification: Upwork Top Rated with a 100% Job Success Score and 5.0 from 9 client ratings, and a Crunchbase-listed prior venture that raised $350K in seed funding.

How Does Remote Collaboration Work with US Teams?

• Daily US business hours overlap - East Coast teams get a half-day of real-time collaboration; West Coast teams get morning syncs
• Weekly structured demos - working software every week, not status decks
• US contracting norms - a direct MSA and SOW with your company
• Cloud-based development - your repos, your cloud accounts, your data stays under your control

Most of the anonymised case studies on this site are US, UK, and European clients engaged this way - including a US pharmaceutical AI platform taken from prototype to enterprise pilot approval and a US fitness platform (CoachIQ) shipped with multi-modal computer vision.

How Much Does an AI Consultant Cost in the US?

Engagements are quoted to scope rather than to a rate card, because cost is driven by the number of data sources, integrations, and compliance requirements rather than by hours. For a new build, the route in is a two-week discovery sprint that scopes one workflow, then a first build that typically takes four to six weeks, quoted from the sprint's estimate. A team with an existing AI system or an AI-built app starts instead with a fixed-price review. If the build does not prove measurable value, you stop there.

For market context on what this kind of work costs generally, see what AI implementation actually costs.

The most cost-effective entry is a paid 2-week discovery sprint. You get a working prototype on your real data, an architecture document, an evaluation set with a pass threshold, and a fixed build estimate either way - keep the deliverables whether you proceed with the build or not.

US Compliance: SOC 2, FDA, CCPA

Compliance is an architecture decision, not a checkbox. Recent US-relevant work includes a pharmaceutical AI SaaS taken to enterprise pilot approval: immutable audit trails, prompt injection guardrails, PII filtering, Kubernetes hardening with 0 critical CVEs, and a gap assessment against FDA 21 CFR Part 11 - 22 of 22 contracted deliverables completed, each with an evidence artifact. For consumer-facing systems, CCPA/CPRA deletion rights are designed across vector stores as well as relational data, because the right-to-delete does not stop at your Postgres instance.

Which US Industries Get the Most from AI Consulting?

• Legal - contract review, due diligence, clause extraction. See the legal AI case study: LPA review reduced from hours to minutes with verified citations inside Microsoft Word
• Financial services and fintech - document analysis grounded in SEC EDGAR, trading signal systems, KYC/AML automation
• Pharma and life sciences - FDA-aligned AI workflows, compliance-ready document intelligence
• Enterprise SaaS - adding production AI features to existing products, multi-tenant RAG architecture, AI-powered support

When AI Is the Wrong Fit (and I Will Tell You)

• The workflow is already deterministic and low-volume. If a clean rule-based script handles it, AI adds cost and unpredictability. Build the script.
• You have no reliable source documents to ground the AI on. RAG without good source data is just confident guessing. Fix the data first.
• You want AI to make legally binding decisions autonomously. In regulated sectors, the right architecture is human-in-the-loop AI assistance - not autonomy.
• The budget and timeline only stretch to a prototype. Under two weeks, with no room for evaluation and integration work, you get a demo, not a production system. I will tell you to use a no-code tool instead.

On a strategy call, if any of these apply to your situation, I will say so directly and not bill you for the build.