Generative AI Engineering Services in Miami | IoTree Minds

Enterprise AI, from strategy to production

Miami, FL · Generative AI Engineering

Generative AI Engineering for Miami businesses

Generative AI for Miami teams: bilingual guest and client assistants, listing content and policy search, grounded in your approved content.

Generative AI Engineering in Miami

Generative AI in English and Spanish

Miami is the gateway between the US and Latin America: private banks and wealth managers, trade and air cargo, cruise lines and hotels, real estate and a large health sector. Much of the work happens in two languages, which shapes how AI needs to be built here.

Where it fits

What Miami teams build with it

Hospitality and cruise

Bilingual guest assistant

Guests get answers and requests handled in their language.

  1. 1Answers from your policies
  2. 2Takes simple requests
  3. 3Hands off to staff
Real estate

Listing content

Listing descriptions drafted in both languages.

  1. 1Drafts from property data
  2. 2Keeps fair housing rules in mind
  3. 3Routes to agents for review

Built for Florida rules

Regulations we design around

Anyone holding Floridians' data

FIPA

Reasonable security measures and fast breach notification.

Very large businesses

FL Digital Bill of Rights

Opt-outs from profiling and limits on sensitive data.

Banks, lenders, fintechs

GLBA Safeguards

A written security program covering every system that handles customer financial data.

Healthcare providers, plans and their vendors

HIPAA

Safeguards and a business associate agreement for anything touching patient data.

Working together · Eastern Time

Working with us from Miami

Your morning is our evening, so we meet live at the start of your day and build while you sleep.

  1. 8–10 AM ET Live call Review progress and make decisions together
  2. Your working day Your feedback Your team tests and comments in writing
  3. Overnight ET We build Progress is ready for your next morning

How we work

From first conversation to production

  1. 1

    Define quality

    We agree what a good answer looks like and build a test set.

  2. 2

    Ground the model

    We connect trusted sources and design retrieval around them.

  3. 3

    Evaluate

    We measure accuracy, safety and cost, and iterate until it holds up.

  4. 4

    Ship and monitor

    We launch with monitoring and feedback loops that keep it improving.

FAQ

Questions from Miami teams

Ask us something else
How do you check quality in Spanish?

We build a separate Spanish test set with native reviewers, and measure accuracy and tone in each language before launch.

What is RAG and why does it matter?

Retrieval-augmented generation (RAG) finds the most relevant passages in your own content and gives them to the language model with the question, so answers are grounded in your data and can cite their sources.

Do we need to fine-tune a model?

Often not. Good retrieval and prompt design solve most enterprise use cases. We recommend fine-tuning only when evaluations show it clearly improves quality, cost or speed.

How do you keep our data private?

We design around your security requirements: private deployments where needed, access controls applied to retrieval, no training on your data without consent, and full logging.