Custom RAG development in Miami.

Miami runs on four languages and seven jurisdictions.

Questions from Miami buyers.

We process documents in English, Spanish, and Portuguese. Which embedding model actually handles all three well?

The honest answer depends on your domain. For general Latin American and Iberian business content, Cohere embed-multilingual-v3 and OpenAI text-embedding-3-large both handle Spanish, Brazilian Portuguese, and English well enough that retrieval quality is not the bottleneck. For financial and legal Spanish (Mexican CNBV filings, Chilean CMF disclosures, Brazilian CVM material) we typically benchmark BGE-M3 and Voyage AI multilingual against your actual corpus before choosing. BGE-M3 has a meaningful edge on long Spanish documents because it natively supports 8192-token sequences, which matters for filings that come in as 200-page PDFs. For Caribbean French Creole or Haitian content we lean toward Cohere multilingual because the smaller models have very uneven coverage. We always run a 50-question evaluation against your corpus in your actual languages before picking, because published benchmark scores do not predict real-world recall well.

We are a Miami-based fintech serving Latin American customers. How do you handle data-residency and cross-border banking-regulation overlap?

Data residency is the binding constraint, not the model choice. Mexican users covered by the LFPDPPP (Mexican federal data-protection law) get their documents indexed in a Mexico-region deployment. Brazilian users under the LGPD get an aaaaaa-region or in-country deployment. Colombian users under Habeas Data and Chilean users under Law 19.628 typically tolerate a US deployment but require explicit consent and cross-border transfer documentation. We design the architecture as a federation of regional indexes with a single query router that picks the right region based on the user's jurisdiction. Cross-border queries go through a redaction pass first. The router and the regional indexes are auditable end-to-end so a CNBV or CVM inquiry can be answered with the exact data flow rather than a hand-waved diagram.

Carnival, NCL, and Royal Caribbean are all HQ-ed in South Florida. What does maritime RAG actually look like?

Cruise and shipping operators carry a regulatory document load that few outside the industry appreciate. SOLAS (Safety of Life at Sea) and MARPOL conventions, USCG NVIC bulletins, port-state-control inspection reports for every port in a global itinerary, IMO Polar Code rules for Alaska and Antarctic routes, vessel-specific class society reports from Lloyd's, DNV, or ABS, ISM Code safety-management documentation. A safety officer asking 'what does the most recent USCG NVIC say about lithium battery storage and how does it interact with our flag-state Bahamas Maritime Authority rules' is doing a multi-corpus retrieval task across English-language documents from at least four authorities. The pipeline ingests each authority's document feed with metadata for issuing body, publication date, applicable vessel class, and applicable flag-state. Hybrid retrieval surfaces the relevant clause from each authority with citations a port captain can act on.

We are a crypto exchange or RegTech-focused fintech. Can RAG support compliance review at the volume that exchanges actually run?

Yes, and Miami is the right market to have built this for given the local concentration of crypto and Latin American RegTech. The retrieval problem is to surface the relevant FinCEN guidance, SEC enforcement order, applicable state money-transmitter regulation, and internal compliance precedent for a specific transaction or counterparty in seconds rather than minutes. A compliance analyst reviewing a high-risk wire to a Colombian counterparty needs the OFAC SDN list status, the FATF jurisdiction risk rating, the relevant Wolfsberg principles section, and the firm's prior decisions on similar transactions. We ingest each source with metadata that matters (regulator, publication date, jurisdiction, transaction-type tag) and run hybrid retrieval with regulator and jurisdiction filters. The system gets faster than a senior analyst at this lookup task while keeping the decision authority with the human.