Customer-facing assistants
Answer approved questions, qualify intent and hand off to a person when the system does not know.
AI Consulting & Chatbot Development
AI Consulting & Chatbot Development for South Florida Businesses
We build customer-facing assistants, internal knowledge tools and workflow automation for businesses across Miami, Coral Gables and South Florida. Try the controlled demo below—including what it does when it does not know.
Try the chatbot demoStart with the problemBOT INT AUT API
What we build—and when we recommend not building it
We use AI where a repeated task has reliable source information, a measurable outcome and a named human owner. If the process is unclear, the content is inaccurate or a simple form and workflow rule would solve the problem more safely, we say so before proposing a model.
Internal knowledge tools
Help staff retrieve answers from controlled business documents without searching across disconnected folders.
Workflow automation
Classify, extract, route and summarize information inside a defined process with human review where risk requires it.
System integrations
Connect approved AI workflows to CRM, scheduling, database and API services after access and security review.
Live controlled example
Ask the Lighthouse AI services assistant
This demo answers questions about service scope, pricing approach, timelines, integrations, accuracy and ownership. Ask something outside that knowledge and it should refuse to guess, then offer a human handoff.
How this demo works
When a server-side OpenAI key is configured, WordPress calls the Responses API without exposing the credential. Without a key, the demo switches to a small approved knowledge base so the page remains functional and demonstrates safe refusal.
Lighthouse AI AssistantReady
Built in-house as a scoped service demo. It is designed to admit uncertainty rather than invent an answer.
Prototype is not production
How an AI project runs
Assessment
Define the repeated problem, business owner, success measure and reasons AI may be the wrong tool.
Content and access
Audit source information, permissions, integrations, privacy requirements and the human escalation route.
Prototype
Build the smallest useful workflow and test it with real questions before expanding the scope.
Evaluation
Measure accuracy, refusals, retrieval quality, edge cases and handoff behavior against an agreed test set.
Launch and monitoring
Release gradually, review failures, update approved content and document responsibility after handover.
Boundaries are part of the build
What we will not do
Hide that it is software
Customers should know when they are interacting with AI and should have a visible path to a person.
Ship what we cannot evaluate
If we cannot test the system against real questions and measure its failure behavior, it is not ready for customers.
Use AI to disguise a process problem
Automation scales the process underneath it. When that process is broken, AI can make the failure faster and harder to inspect.
Promise perfect accuracy
No responsible provider can. We reduce risk through grounding, evaluation, refusal, access control and human escalation.
Scope before a build commitment
How we price AI work
Cost moves with the number of connected systems, the condition of your content, privacy and accuracy requirements, and whether ongoing monitoring is needed. Most engagements begin with defined discovery so we can map the problem and say whether a build is justified.
Direct answers
Frequently asked questions
How much does a custom AI chatbot cost?
Cost depends primarily on the systems involved, the condition of the source content, the accuracy and privacy requirements, and whether post-launch monitoring is included. We begin with an assessment and provide a defined scope before asking for a build commitment.
How long does an AI chatbot take to build?
A typical scoped implementation takes about four to eight weeks. A prototype can appear quickly, but content preparation, integration, evaluation and safe fallback behavior normally take longer than the first demo.
How do you stop a chatbot from making things up?
We ground answers in approved content, restrict the permitted subject area, test against known questions, define refusal behavior and provide human escalation. No model is perfectly accurate, so we design and test what happens when the system is uncertain.
Will my data be used to train AI models?
That depends on the selected provider, product and account settings. We document the data path and applicable provider terms during discovery rather than making one universal promise. Sensitive projects require a specific privacy and retention review before implementation.
Which AI platform do you use?
We select the platform after reviewing accuracy needs, integrations, privacy, latency, cost and the client’s existing technology. A familiar model name is not a substitute for evaluating the system against the actual task.
Can AI connect to my CRM, scheduling system or database?
Often, when the system provides a suitable API and secure access. Discovery confirms available endpoints, permissions, data mapping, error handling and whether a human must approve actions before anything is written back.
What happens when the chatbot does not know?
It should say that it does not know, avoid guessing and offer a clear human handoff. The demo on this page is designed to show that behavior when a question falls outside its approved knowledge.
Do I own the AI system you build?
Code ownership, repository access, hosting, business accounts and handover are written into the engagement. Third-party AI model usage remains governed by the selected provider’s service terms and usage charges.
What happens after launch?
Post-launch work can include monitoring unanswered questions, reviewing incorrect outputs, updating approved knowledge, evaluating costs and adjusting integrations. Ongoing support is scoped separately and is not hidden inside an undefined retainer.
Does my business actually need AI?
Not always. AI is useful when there is a repeated, measurable task, reliable source information and a clear owner. If the process itself is unclear or the content is wrong, we normally recommend fixing those issues before adding AI.
How this page was produced
Human responsibility stays visible
Written and reviewed by Emilio Yepez, with generative AI used for structural outlining and selected FAQ drafts. Business claims, service positions and implementation details were reviewed by a person before publication. We disclose this because the same standard should apply to client systems: people should know when AI is involved and who remains responsible.
A lower-commitment first step
Start with a 30-minute AI assessment
Tell us the problem you are trying to solve. We will explain whether AI is appropriate, what a responsible build would involve and what determines the cost. If a simpler tool is the better answer, we will say so.
Book the assessment Call (305) 987-2506