United States
Contract with a US entity, keep processing inside your own account and region, and work with a team whose seniority you could not otherwise afford on the same budget.
In short
US companies work with Leverge through a US contracting entity, with delivery led by the senior engineering team in Bengaluru and a fixed daily overlap with US business hours. Architecture is designed for US compliance review from the start — SOC 2 controls, HIPAA business associate arrangements where protected health information is involved, state privacy law obligations, and processing kept inside your own cloud region and account wherever the use case allows.
Working with us here
A US entity for contracting, invoicing and travel under your preferred governing law. Procurement, legal and finance all deal with a domestic counterparty.
Deployment inside your own cloud account with model inference through your existing tenancy, so residency and retention questions are answered by the architecture rather than by a policy statement.
SOC 2 control expectations, HIPAA arrangements and state privacy obligations treated as design inputs, with evidence produced as the system is built.
A fixed daily window with your business hours, agreed at kickoff, with async written updates and a defined escalation response time outside it.
Fixed daily window with US Eastern, Central or Pacific business hours, agreed at kickoff
Market context
US buyers of AI engineering services are generally past the experimentation stage and under pressure to show a system in production, which changes what they need from a partner: not enthusiasm about what models can do, but evidence of having operated something through a security review and a year of drift. At the same time domestic senior AI engineering capacity is scarce and expensive enough that most mid-market companies cannot staff a full team internally on the timeline they have been given. That combination — urgency, a hard compliance gate, and constrained hiring — is the situation where a US-contracted, India-delivered team is genuinely the better structure rather than merely the cheaper one.
Most objections to offshore AI delivery are not about engineering quality. They are procurement objections: who is the counterparty, under whose law, where does the data go, who signs the business associate agreement, who appears at the security review.
Contracting through a US entity answers all of those with a domestic answer, while delivery is led by the senior team that actually builds the system. Your legal and finance functions deal with a US company. Your security team gets someone in the room.
The cleanest way to resolve data residency is not a policy document — it is an architecture where the data never leaves.
Deployment goes into your AWS, Azure or GCP account. Retrieval indexes, embeddings and logs live in storage you control. Model inference runs through your own tenancy on Bedrock, Azure OpenAI or Vertex with zero retention configured, so customer data does not leave your region or reach a vendor you have no agreement with.
Where a use case cannot tolerate any external inference at all, an open-weight model on your own infrastructure is the fallback. We quantify the accuracy trade-off before you commit to it, because it is usually real and occasionally acceptable.
The pattern we see most often in remediation work: a team builds the system, takes it to security review at the end, and is sent back because logging, access scoping or retention was never designed.
SOC 2 control expectations and HIPAA obligations are cheap to build in and expensive to retrofit. We establish which apply in week one and produce the evidence — access control design, change history, audit logs, evaluation results — as build artefacts rather than as a documentation exercise before an audit.
Next Step
A 30-minute technical call with an engineer who has shipped this before — not a sales qualification round. You leave with a feasibility read, a rough shape for the build, and an honest answer about whether it is worth doing at all.