Hire an AI Developer: Custom AI Development for US Companies

Who Builds Custom AI Systems for US Companies?

Nic Chin builds custom AI platforms for US companies. Unlike general software agencies, the focus is exclusively on production AI - RAG systems with verifiable citations, multi-agent automation, enterprise chatbots, and AI document processing. Every system is architected for production reliability and compliance-ready deployment, not just demos.

Production track record includes:

• SureCiteAI - a live multi-tenant document intelligence RAG SaaS with a 94% retrieval hit-rate and 0 hallucinated citations across 297 benchmark cases, 255 of them on public corpora (PatronusAI FinanceBench, CUAD, openFDA, SEC EDGAR)
• SystemAudit - a live codebase intelligence SaaS that turns any GitHub repo into a full architecture, security, and AI-readiness report in under 3 minutes
• A 12-component legal RAG system processing 150-200 page documents in minutes
• A 20-agent trading intelligence ensemble handling real-time market analysis
• An AI marketing automation platform saving the client 5-7 hours a week

Methodology and benchmark artefacts are public - see the /proof page.

How the Engagement Works

New US builds follow a fixed, de-risked sequence - designed so you never sign a large commitment before working software exists:

1. Discovery call (no charge, 45 minutes) - understand your problem, data, and constraints. Honest assessment of whether AI is even the right tool.
2. Paid discovery sprint (2 weeks, fixed scope) - deep dive into your data, systems, and requirements. Produces a working prototype on your real data, an architecture document (including a build-vs-buy recommendation), an evaluation set with a pass threshold, and a fixed build estimate. You keep every deliverable whether you proceed with the build or not.
3. MVP build (typically 4-6 weeks, quoted from the sprint's estimate) - core system targeting your highest-value workflow, weekly demos, success criteria agreed up front.
4. Production deployment - US region, monitoring, security review, and compliance verification for SOC 2-aligned environments.
5. Iterate and scale - fractional CTO support to expand to adjacent workflows once value is proven.

Exact scope and investment are settled on the strategy call - every engagement is priced against the specific system being built, not a generic rate card. This avoids the most common AI buying mistake: committing to a five- or six-figure transformation engagement before any working software exists.

AI Architect vs Junior Developer vs Agency

US companies buying custom AI development typically choose between three options. Honest comparison of what you actually get:

Private GPT for Your Company Documents

The pattern most US companies ask for under the phrase "private GPT" is a multi-tenant RAG system: your company documents stay in your data store, an AI assistant answers questions about those documents, every answer cites the source page, nothing is sent to OpenAI or Anthropic for training, and access is gated by your existing SSO.

SureCiteAI is the production reference implementation. The same architecture transplants into a US client deployment in 4-6 weeks. Data residency stays in US regions; the citation verifier blocks hallucinated sources; audit logs are retained per your retention policy.

SOC 2, HIPAA, and US Data Compliance

Every AI system for US clients is designed with compliance from the architecture stage:

• Data residency: US regions for data at rest and in transit
• Access control: role-based access mapped to your existing SSO, with least-privilege defaults that align with SOC 2 Trust Services controls
• Source attribution: every AI-generated answer is traceable to the underlying document
• Audit logging: every prompt, retrieval, and answer is logged with retention configurable to your policy - the evidence trail your auditor asks for
• PHI and PII handling: masked or omitted in embedding and training paths where not strictly required; BAA-compatible model providers for HIPAA-covered entities
• State privacy laws: data-minimisation patterns that hold up under CCPA/CPRA and the growing set of state-level privacy statutes

For regulated US industries - financial services under SEC/FINRA expectations, healthcare under HIPAA, life sciences under FDA - additional safeguards include VPC or on-premise deployment, role-based access controls, and complete audit trails surfaced to your compliance team.

Industries I Build For in the US

• Financial services and fintech - KYC/AML automation, SEC filing analysis, regulatory reporting, fraud detection
• Legal and professional services - contract review, due diligence, clause extraction, internal knowledge bases (see AI for law firms)
• Insurance - claims triage, policy document analysis, underwriting decision support
• Healthcare and life sciences - clinical decision support, regulatory document automation, FDA-aligned workflows
• Accounting and fund administration - document review, compliance, internal knowledge management
• Enterprise SaaS - adding AI features to existing US products, multi-tenant RAG, AI-powered customer support

How Remote Delivery Works for US Teams

Delivery is remote-first with daily US business-hours overlap - standing calls land in your morning, async updates land before you start work, and weekly demos run live on your calendar. Code, infrastructure, and documentation live in your accounts from day one: your GitHub org, your cloud, your observability stack. There is no handoff cliff at the end of the engagement, because nothing needs handing off.

For UK businesses, see Custom AI Development UK. Before a system you already run goes to enterprise customers, see the AI security and architecture review.