GET AI Labs serves US-based organizations as a remote-first applied AI research and consulting partner. The senior team is distributed and Canadian-anchored, delivering strategy, evaluation, and engineering across every US time zone — with US AI compliance treated as a first-class input, not an afterthought.
The United States runs the deepest AI ecosystem in the world. The San Francisco Bay Area anchors frontier model research and AI venture capital; Boston, New York, Seattle, and Austin each anchor their own concentrations of enterprise software, financial-services AI, cloud-scale machine learning, and scaling startups.
But the frontier is not where most US organizations live. The widest opportunity sits in the mid-market and in regulated industries — manufacturing, insurance, logistics, regional banks, healthcare systems — where the gap is not access to frontier models but the applied work of turning them into reliable production systems. These firms rarely need another foundation model. They need an honest assessment of where AI creates durable advantage, a system evaluated against their real data, and a deployment that survives their regulatory environment. That is applied AI consulting, and it is distinct from frontier research.
Frontier model labs, AI infrastructure, venture-scale startups
The largest concentration of foundation-model research and AI venture capital in the world. Home to the major frontier labs and the infrastructure companies they depend on.
Enterprise software, healthtech, university research
A dense academic-industrial cluster anchored by MIT and Harvard, with deep healthcare-AI and enterprise-software activity. HubSpot is headquartered here.
Financial-services AI, media, applied enterprise adoption
The center of US financial-services AI and a fast-growing applied-enterprise market, where regulated-industry adoption matters more than frontier research.
Cloud platforms, large-scale ML, developer tooling
Anchored by the major cloud providers, with deep strength in large-scale machine learning, MLOps, and the developer tooling that supports production AI.
Enterprise relocation hub, semiconductors, scaling startups
A growing technology hub that has attracted enterprise headquarters and a widening base of scaling startups, with a lower cost structure than the coasts.
Manufacturing, insurance, logistics, healthcare systems, regional banks
The largest pool of unmet applied-AI demand. Outside the coastal clusters, mid-market and regulated firms need production help, not frontier research.
US AI compliance is not one statute you can point to — it is a set of frameworks, a patchwork of state laws, and the sector regulation that already governs AI in healthcare, finance, employment, and government. We design AI systems with these obligations as first-class inputs.
The United States has no comprehensive federal AI statute. Federal direction has come through executive action, agency guidance, and existing sector regulators applying their authority to AI. The practical consequence is that obligations are assembled from frameworks and sector laws rather than read off one statute — which is exactly the assembly an applied partner has to get right.
The NIST AI Risk Management Framework (AI RMF) is voluntary, but it is increasingly the expected baseline — enterprise buyers, procurement teams, and partners cite it as the reference for trustworthy AI. We map system design and risk documentation to its functions so the output stands up to that scrutiny.
Several states have enacted or proposed AI-specific legislation, including the Colorado AI Act, the Texas Responsible AI Governance Act, and California's privacy and automated-decision rules. The obligations differ by state, so the states a client operates in change the design. We scope against the specific regimes in play rather than a generic national assumption.
AI used in a regulated sector inherits that sector's law. HIPAA governs AI touching protected health information; GLBA, FCRA, and ECOA govern AI in financial services and credit, with explainability expectations for adverse-action and fair-lending decisions; the EEOC has signaled scrutiny of AI hiring and assessment tools. These are first-class inputs, not afterthoughts.
Selling AI into federal and public-sector buyers brings procurement and security frameworks into scope — FedRAMP for cloud authorization, FISMA for federal information security, and the NIST 800-53 control set. System architecture and data-handling decisions account for these obligations where the client is pursuing public-sector work.
Across all of the above, the operating principle is the same: we design AI systems with the applicable obligations as first-class inputs, scored in the risk register alongside technical and operational risk. Where a question is legal rather than technical, we work alongside the client's counsel — we do not substitute for it.
This page describes regulatory frameworks in general terms and is not legal advice. GET AI Labs works alongside the client's counsel on legal determinations.
GET AI Labs is Canadian-anchored and distributed by design. For a US organization, working with a remote-first partner from this base carries concrete advantages — not compromises. The team operates in full overlap with every US time zone, contracts under the familiar USMCA framework, and delivers entirely in English under common-law IP norms a US legal team already understands.
It is also frequently more cost-effective than an equivalent engagement with a US coastal consultancy — without trading away seniority, because the practitioner who writes the strategy is the one accountable for it. There is no US office and no US legal entity here; that is a deliberate, honest description of the model, not a gap. One direct US-enterprise touchpoint is worth naming: principal Nicholas Rose is a Technical Lead II at HubSpot, a US-headquartered public enterprise-software company in Cambridge, Massachusetts — a working line of sight into US enterprise engineering practice.
A distributed senior team gives full working-hours overlap with every US zone, Eastern through Pacific. Scheduled sessions, reviews, demos, and urgent troubleshooting happen inside the US business day — not across an offshore offset that pushes responses to the next morning.
Cross-border services between Canada and the United States operate under the USMCA framework, the successor to NAFTA. It is a stable, familiar basis for a US organization contracting professional services from a Canadian-anchored partner.
Canada and the United States share a common-law tradition and broadly compatible intellectual-property norms. Engagement terms, IP assignment, and confidentiality arrangements map cleanly onto what a US legal team already expects — and every deliverable is client-owned at handoff.
Every artifact, working session, and decision document is produced and delivered in English. There is no translation layer and no language friction between the people writing the strategy and the US team that has to act on it.
Senior applied-AI work delivered from a Canadian base is frequently more cost-effective than an equivalent engagement with a US coastal consultancy — without trading away seniority, because the practitioner writing the strategy is the one accountable for it.
US clients work directly with the senior people doing the work. There is no account team, no offshored analyst pool, and no handoff from the people who scoped the engagement to a more junior team that delivers it.
US engagements draw on the same capabilities as every other GET AI Labs engagement — scoped to the US regulatory and market context. The work spans written strategy, independent evaluation, and production engineering, all delivered remotely with client-owned outputs at handoff.
AI strategy consulting produces adoption roadmaps, opportunity maps, and risk registers for the board and executive team. AI evaluation services benchmark a model or system against your real data before you commit to it. LLM engineering builds the production systems — RAG, agents, fine-tuning, and the pipelines around them. Applied AI research answers the harder domain questions that precede a build. Each one is available to US clients as a fixed-scope engagement.
Direct answers about how a Canadian-anchored, remote-first applied AI lab serves US organizations — time zones, US AI compliance, regulated industries, and how engagements start.
Yes. GET AI Labs works with US-based organizations as a core part of its practice. The lab is remote-first and distributed, with a senior team operating across multiple continents, so US engagements are delivered the same way as any other — through written scopes, scheduled working sessions, and client-owned deliverables. Time-zone overlap with every US zone is full, from Eastern through Pacific, so working sessions, reviews, and incident response happen inside the US business day rather than across an awkward offset.
No. GET AI Labs is a Canadian-anchored applied AI research lab, not a US legal entity, and it does not maintain US offices or US-based staff. Its institutional roots are in Canadian applied-AI research — affiliations with the AI Hub at Durham College in Ontario and the University of Alberta — and the team is distributed and remote-first. GET AI Labs serves US organizations remotely from that base. One direct US-enterprise touchpoint is that principal Nicholas Rose is a Technical Lead II at HubSpot, a US-headquartered public software company based in Cambridge, Massachusetts.
GET AI Labs treats US AI obligations as first-class engineering inputs rather than a compliance footnote added at the end. There is no single federal AI statute in the United States; instead there is the NIST AI Risk Management Framework (voluntary, but increasingly expected by enterprise buyers and procurement), a growing patchwork of state laws, and long-standing sector regulation that already governs AI used in those sectors. Engagement deliverables — risk registers, evaluation plans, architecture decisions — are produced with the specific framework and laws that apply to the client's sector and the states it operates in. Where a legal determination is required, GET AI Labs works alongside the client's counsel rather than substituting for it.
Yes, across all of them. The team's distribution gives it full working-hours overlap with US Eastern through US Pacific, so scheduled sessions, design reviews, demos, and urgent troubleshooting happen inside the US business day. Engagements are run asynchronously by default — written scopes, documented decisions, recorded artifacts — with live sessions booked into US-friendly windows. This is a deliberate part of the delivery model, not an accommodation made case by case.
Yes. GET AI Labs works across high-stakes and regulated domains, and treats the governing regime as a design constraint from the first engagement phase. For US healthcare that means designing around HIPAA obligations for protected health information; for financial services and credit it means accounting for GLBA, FCRA, and ECOA, and the explainability expectations that come with adverse-action and fair-lending rules; for hiring tools it means awareness of EEOC scrutiny of automated employment decisions. For federal and public-sector buyers, system design accounts for procurement and security frameworks such as FedRAMP, FISMA, and the NIST 800-53 control set. The depth of work in any given regime depends on the specific obligations the client is subject to.
The same way every GET AI Labs engagement starts: a free initial conversation to understand the problem, followed by a written scope with defined deliverables, timeline, and a fixed fee before any commitment. For US clients that first conversation is booked into a US-friendly time window. Many engagements begin with a two-week Technology Opportunity Mapping or a Systems Evaluation, which lets a US organization test the working relationship on a contained, fixed-scope piece of work before committing to anything larger. NDAs are available where the work requires them.
Bring us the problem. We will help determine what is possible, what is practical, and what should be built next.
Response within two business days · NDAs available when required
