LLM Integration Company in Miami

Integration surface is what separates a Miami LLM pilot from a Miami LLM programme: a scoped LLM integration POC at Miami rates, a production LLM integration into a cruise reservation system, real estate CRM and MLS, or financial services OMS, or multi-system enterprise integrations (cruise line guest experience across reservation, dining, excursion, and onboard payment under PCI-DSS Level 1; full mortgage origination LLM across Encompass, MLS, Salesforce). Scope drivers include integration design, tool-calling schema, baseline evaluation, and a demo connected to one or two systems of record. We deliver fixed-fee statements of work on a two-week sprint cadence rather than open-ended T and M.

Yes. We work with cruise line internal engineering teams and authorised system integrators on guest experience LLMs that connect to Oracle Cruise reservation platforms, dining and excursion booking systems, onboard payment flows under PCI-DSS Level 1 scoping, and crew operations consoles. The hard constraints are maritime network resilience (LLMs must degrade gracefully on satellite-backed networks with variable bandwidth), 6-10 language parity (guests speak Spanish, Portuguese, German, French, Italian, Mandarin, and more), and the cruise line's own incident response and disclosure obligations under maritime authorities. We design offline degraded modes that cache recent guest preferences and fall back to non-LLM rule-based handling when bandwidth drops, and we log every inference for incident response.

FINRA Rule 3110 requires broker-dealers to supervise associated persons' business communications, including LLM-generated outputs that reach clients, counterparties, or research consumers. Our integration pattern is explicit. Every prompt and completion is logged to a SEC 17a-4 WORM-compliant archive (we use Smarsh, Global Relay, or AWS S3 with Object Lock and Compliance Lock retention) before the output reaches the user surface. Outputs that meet supervisable communication criteria flow through a human-in-the-loop review queue with named registered principals approving release. MNPI containment is enforced through deal-team tenant isolation in vector stores and prompt-level filters. We do not act as a registered broker-dealer or registered investment advisor, we coordinate with your CCO and named registered principals to align the LLM workflow with your existing written supervisory procedures.

Yes, for latency-sensitive use cases. AWS Local Zone Miami (launched 2022) provides single-digit millisecond latency from Miami metro to compute resources, which matters for real-time conversational LLM experiences in cruise terminals, hotel front desks, and Brickell trading floors where the round-trip cost of going to us-east-1 (Northern Virginia, 25-35ms RTT from Miami) is perceptible to end users. For most LLM integration workloads (asynchronous content generation, batch document extraction, internal copilots) the latency difference is irrelevant and us-east-1 primary with Miami Local Zone reserved for specific edge cases is the better cost-to-performance trade. We also use Equinix MI1-MI3 and the NAP of the Americas for private interconnect into Latin America cable landings when cross-border LatAm latency matters.

Yes. Lennar's scale (largest US homebuilder by closings) and the Brickell and Coral Gables luxury sales ecosystem run on Encompass for mortgage origination, Salesforce Real Estate Cloud or HubSpot for CRM, MLS feeds for listing inventory, and warranty platforms post-close. We integrate LLMs through native API connectors (Encompass Smart Client SDK, Salesforce REST and Bulk APIs, Apex callouts, MLS RESO Web API), with tool-calling schemas that pin down read versus write actions. Common use cases are listing generation from spec sheets, mortgage pre-qualification narrative drafting (with explicit ECOA fair lending review gates), warranty triage and document extraction, and bilingual buyer communication. We avoid autonomous write actions on financial or legal documents and always route those through human approval queues.

Country-specific evaluation is the core practice. Spanish in Mexico is not Spanish in Argentina or Colombia or Spain, and Brazilian Portuguese is not European Portuguese. We staff native speakers per target country, build evaluation harnesses with held-out test sets per dialect, and configure LLM prompts and few-shot examples with country-appropriate terminology, regulatory language (Banxico, CNBV in Mexico; CNV in Argentina; CVM and BCB in Brazil; CMF in Chile), and currency localisation. For LGPD-covered Brazil residents we route through AWS sa-east-1 (Sao Paulo) for both retrieval and inference. Cohere Embed Multilingual handles cross-lingual retrieval well, frontier models (GPT-4o, Claude 3.5) handle generation, and Llama 3 70B fine-tuned on per-country corpora handles regulated workloads where hosted APIs are off limits.

A typical Miami production LLM integration (cruise concierge into a reservation system, real estate listing automation, financial services AML extraction, LatAm bilingual customer service copilot) takes twelve to twenty weeks from kickoff to production rollout. Week 1-3 is integration design, tool-calling schema, and regulatory scoping. Week 4-10 is build, prompt engineering, evaluation harness, and shadow-mode deployment. Week 11-16 is production rollout with observability, audit logging, and red-team prompt injection testing. Week 17+ is gradual scale-up with ongoing evaluation. Cruise line and FINRA-supervised workloads add 3-6 weeks for additional compliance documentation and human-in-the-loop workflow design. We deliver fixed-fee statements of work and ship every two weeks.

Yes. We deploy Llama 3 70B, Llama 3.1 405B, and Mistral Large on private GPU clusters (H100 and A100 on AWS Capacity Blocks, Together AI dedicated endpoints, or self-managed Kubernetes on EKS) for clients whose FDBR, HIPAA, FINRA, or LGPD requirements rule out hosted frontier model APIs. We tune with LoRA and QLoRA on bilingual corpora when language parity matters, expose inference through vLLM or TGI behind an internal API gateway with the same observability and audit logging as hosted-API integrations, and design failover into hosted APIs for non-sensitive workloads. Self-hosted GPU economics make sense when sustained tokens per second exceeds roughly 50-100M tokens per month, below that hosted API pricing is usually better.