Generative AI Company in Miami

Miami generative AI work is costed by department first, then by channel: a scoped GenAI proof of concept at Miami rates, a production RAG or copilot deployment for a mid-sized cruise line department, real estate developer, or fintech, or full multi-channel generative AI rollouts (concierge across web, app, in-cabin, and crew interfaces for a cruise line; full listing-to-close mortgage automation for a national homebuilder). Scope drivers include data audit, baseline prompts and evaluation, a RAG implementation, and a hosted demo. Miami rates sit between Atlanta and New York. We deliver fixed-fee statements of work and ship every two weeks rather than waterfall releases.

Yes. Cruise line generative AI is a Miami specialty because three of the top four global operators are headquartered here. We build multilingual concierge and operational copilots that maintain quality parity across English, Spanish, Portuguese (Brazilian and European), German, French, Italian, and Mandarin, using Cohere multilingual Embed for retrieval and frontier models (GPT-4o, Claude 3.5, Llama 3 70B) for generation. Evaluation harnesses test each language against held-out ground truth, not just English, because guest experience cannot tolerate a model that degrades from English to Portuguese. We integrate with onboard maritime networks (low-bandwidth, intermittent), respect PCI-DSS Level 1 cardholder data boundaries on integrated payment flows, and log every inference for audit and incident response under the cruise line's incident response playbook.

The Florida Digital Bill of Rights took effect July 1, 2024 and applies to controllers with $1B+ global revenue that derive 50%+ revenue from ad sales OR operate consumer device app stores OR are smart-speaker manufacturers (a narrower scope than CPRA or VCDPA). For most Miami AI builds the FDBR is not directly triggered, but the practical reality is that Miami enterprises serving California (CPRA), Colorado (CPA), Virginia (VCDPA), and Latin America (LGPD in Brazil) need a state-comprehensive privacy posture anyway. We deliver a controller-processor record, a Data Protection Assessment for high-risk processing (profiling, sensitive data), a Universal Opt-Out Mechanism implementation where reachable users include CPA-covered residents, and a bilingual privacy notice (English and Spanish) for any consumer-facing surface.

Yes, with explicit FINRA and SEC guardrails. We build research and trading copilots for buy-side firms in Miami including the post-2022 Citadel relocation cohort, Apollo's Miami office, and crypto-native trading firms. The hard constraints are FINRA Rule 3110 supervision (every communication and analytical output must be logged and supervisable), SEC Rule 17a-4 retention (7-year WORM-compliant archiving), MNPI containment (research models cannot leak material non-public information across deal teams), and IP licensing of training data (most data vendors prohibit LLM fine-tuning on licensed feeds). We architect with strict tenant isolation, prompt-level audit logging, MNPI redaction, and human-in-the-loop approval gates for any output that reaches external counterparties. We do not act as a registered broker-dealer and we do not produce trade recommendations directly.

Yes. Miami is the operational gateway to Latin America, and MercadoLibre's US headquarters, Mastercard's Latin America regional HQ, and Visa LatAm all run workflows where Spanish and Portuguese accuracy is non-negotiable. We build content generation pipelines with country-specific terminology (Mexican Spanish vs Argentinian vs Colombian, Brazilian Portuguese vs European), currency and regulatory localisation per country (Banxico, CNV Argentina, CVM Brazil, Comision para el Mercado Financiero in Chile), and evaluation harnesses tested by native speakers rather than translators. For Brazil-resident data under the LGPD we hybrid into AWS sa-east-1 (Sao Paulo) with explicit data flow controls. Native Spanish and Portuguese reviewers are part of the engagement, not localisation as an afterthought.

A typical Miami production GenAI build (RAG-based concierge or copilot for a cruise line department, listing generation for a real estate developer, AML document extraction for a fintech) takes fourteen to twenty-two weeks from kickoff to production rollout, assuming clean source data and FDBR or HIPAA documentation as part of scope. Week 1-4 is data audit, evaluation harness, and baseline. Week 5-12 is RAG or fine-tuning, multilingual evaluation, and prompt engineering. Week 13-18 is observability, prompt injection defences, audit logging, and shadow-mode deployment. Week 19+ is gradual rollout. Cruise line workloads add 2-4 weeks for maritime network testing and 6+ language QA. We deliver fixed-fee statements of work and ship every two weeks.

Yes. We run fine-tuning workflows on Llama 3 (8B and 70B) and Mistral (7B, Mixtral 8x7B, Mistral Large) using LoRA and QLoRA on H100 or A100 clusters via AWS SageMaker, Together AI, Replicate, or self-managed Kubernetes on EKS. For Miami bilingual workloads we fine-tune on English-Spanish and English-Portuguese parallel corpora, with held-out evaluation sets per language and per industry domain (cruise, real estate, financial services). We deliver a model card documenting training data lineage, bias evaluation, hallucination rate per language, and known failure modes. Fine-tuned models are deployed either on hosted endpoints (Bedrock custom models, Azure AI Studio, Together) or on private GPU clusters when FDBR, HIPAA, or LGPD data flow restrictions rule out cross-border hosted inference.

When a problem genuinely requires novel research we scope collaborations with University of Miami's Institute for Data Science and Computing, FIU's Knight Foundation School of Computing, and Miami-Dade College's emerging tech programs. Most production GenAI work in Miami does not require academic partnership and we will say so directly. Where it does (rare bilingual rare-event detection for AML, novel multilingual evaluation methodology, cross-lingual reinforcement learning) we coordinate with named faculty, manage the IP and publication terms in the contract, and keep our core engineering team responsible for productionisation. Most Miami GenAI projects stall at productionisation, not at the research frontier, so we focus engineering capacity there first.