LLM development for US teams that need models to actually ship.

Honest answer: our Mohali team runs IST, which gives a native two-to-three-hour window with US Eastern late afternoon. For US LLM engagements that need full business-hours coverage — code review, eval-run debugging, model-swap incident response — we run a dedicated US-hours pod out of our Frisco, TX office and a tech-lead-on-call rotation covering 9am to 6pm Central. Not a junior support shift, the same senior engineers building your LLM application. Twice-weekly demos run in US business hours with eval-run numbers and cost telemetry; written updates with overnight regression results land before your standup. If your engagement genuinely cannot survive without same-zone synchronous coverage at all hours, we will say so on the first call.

Because every LLM engagement we have audited that failed in production failed because nobody wrote the eval set. The team tuned a prompt until it looked good on three examples, the model swapped underneath them in a vendor update (Claude 3.5 to 4.6, GPT-4o snapshot changes, Llama 3 to 3.1), and quality regressed silently for weeks before someone noticed in a customer complaint. The eval harness is the regression test for the LLM — a fixed reference set of inputs, expected behaviours (faithful citation, refusal when out of scope, structured-output validity), and pass-fail criteria. We wire it in week one and run it on every prompt or model change. It is the difference between shipping an LLM application and shipping a demo. Frameworks we use: Braintrust, Langfuse, Phoenix Arize, or a bespoke harness when the standard tools do not fit.

Yes. Engagement controls are SOC 2-aligned and HIPAA-aligned. We sign BAAs before any PHI is shared. For LLM inference: Claude on Anthropic's HIPAA-eligible tier, GPT-4o on Azure OpenAI Service in a US region with BAA, AWS Bedrock with BAA for clients standardising on Bedrock, or self-hosted Llama 3 on vLLM inside your VPC for clients with strict no-third-party-inference requirements. Audit logs land on every model call (prompt version, model name, input, output, operator identity) and export to your SIEM. SOC 2 control evidence — change management, access controls, encryption at rest and in transit, key management — is documented as part of the engagement. We run the entire build inside your AWS, Azure, or GCP account if your security team requires customer-managed encryption and a zero-egress data path.

Your call. We default to AWS us-east-1 (N. Virginia) or us-west-2 (Oregon) for US clients, with us-east-2 (Ohio) for clients standardising there. For LLM inference, Claude routes to Anthropic's US endpoints, GPT-4o routes to Azure OpenAI Service in a US region, and self-hosted Llama 3 runs on GPU instances inside your us-east-1 or us-west-2 VPC. For clients with strict data-residency requirements (federal, healthcare, defence-adjacent), we deploy single-region with no cross-region replication and no LLM egress to non-US endpoints. AWS GovCloud deployments are supported for federal-adjacent clients with the appropriate clearance posture; Aiinfox engineers connect over a privileged-access path the customer controls.

Both. Bedrock is the right answer for clients who want a single compliance posture across multiple model families (Claude on Bedrock, Llama on Bedrock, Cohere on Bedrock, Titan where it earns its keep) and a single BAA covering inference. Direct provider APIs (Anthropic, OpenAI, Azure OpenAI) are the right answer when you want the latest model snapshot before Bedrock catches up or when your eval bar demands a model not yet on Bedrock. We pick per engagement on the kickoff call against your security and procurement posture — most US healthcare clients land on Bedrock or Azure OpenAI with BAA, most US SaaS clients land on Anthropic direct or Azure OpenAI direct, most US defence-adjacent clients land on self-hosted Llama 3.

Yes — LLM takeover audits are routine. Step one is reading the code, the prompts, the eval results (if any exist), the retrieval pipeline, the model and provider choices, and the cost telemetry. Step two is shipping the smallest valuable change to prove we understand the system — usually wiring the eval harness or fixing the retrieval layer the previous vendor skipped. Step three is the longer-term plan: incremental stabilisation, a model swap to a better-suited Claude or self-hosted Llama 3 build, or a parallel rebuild if the architecture is unsalvageable. Most takeovers we see did not need a full rewrite; they needed evals, guardrails, observability, and a senior engineer on the build. We will be honest on the first call about which category your project lands in.

Most v1 LLM engagements at Aiinfox land between $30,000 and $150,000 fixed-price for a focused build — a copilot, a RAG-grounded LLM app, a fine-tuned domain model, or an evals-and-guardrails programme retrofit. Larger multi-quarter engagements with custom fine-tuning, bespoke evals, HIPAA documentation, and integration into a regulated platform typically reach $180,000 to $380,000. The cost difference versus a Bay Area, NYC, or Boston LLM consultancy lands roughly 30 to 50 percent lower on senior rates — useful, but the headline is the engineer on your kickoff call writes your prompts, your evals, and your code through launch. No swap-out to a junior pool mid-engagement.

Healthcare (HIPAA-aligned medical-inquiry LLM with 98.4% citation accuracy in production), telco support (68% L1 deflection sustained over nine months on a 2M-subscriber LLM-powered bot), EdTech (47% completion lift on an adaptive interview LLM we ship ourselves as Mockinto), and self-hosted Llama 3 fine-tunes for healthcare-specific accuracy on regulated workloads. Reference calls available under NDA. 50+ production systems shipped across 12 verticals — see the documented case studies for the engineering and business outcomes we can show publicly.