Las Vegas AI automation that works while you sleep.

AI automation uses artificial intelligence to handle repetitive tasks, make decisions, and streamline workflows that currently require manual effort. This includes things like auto-responding to customer inquiries, processing invoices, generating reports, scheduling appointments, and analyzing data. It frees your team to focus on high-value work instead of repetitive tasks.

Simple automations like chatbots and email workflows start at $3,000. Complex multi-system integrations with custom AI models range from $10,000–$50,000+. Every project gets a detailed scope and fixed-price quote upfront. Most clients see ROI within 2–3 months through time savings and reduced errors.

No. I design every automation with non-technical users in mind. You get a clean dashboard to monitor your automations, simple controls to adjust settings, and thorough documentation. I also provide training and ongoing support to ensure your team is comfortable.

I build custom solutions using OpenAI GPT models, Claude, LangChain, n8n, Make.com, and custom Node.js/Python backends. For simpler needs, I leverage tools like Zapier and Make.com. The choice depends on your complexity requirements, budget, and integration needs.

Yes. I specialize in connecting AI automations with tools you already use: CRMs (Salesforce, HubSpot), email platforms (Gmail, Outlook), project management (Asana, Monday), accounting (QuickBooks, Xero), and custom databases. If it has an API, I can connect it.

Claude (Anthropic) is my default for production work in 2026: better at following complex instructions, better at long-context reasoning, more reliable on tasks that require nuance. OpenAI's GPT-4 family is faster and cheaper for high-volume simple tasks (classification, summarization, simple chat) and has the broadest tool/function-calling ecosystem. For most client builds, I use both — Claude for the heavy reasoning calls, GPT-4 mini for the high-volume routine ones. Cost matters: Claude is roughly 2-3x more expensive per token, but the quality difference often pays for itself in fewer human corrections.

Critical question. By default, OpenAI and Anthropic API calls (the developer-facing platforms — not ChatGPT or Claude.ai consumer products) do NOT train on your data. Anthropic explicitly contracts that API data isn't used for training; OpenAI does the same as of their 2023 policy change. For more sensitive workloads (PHI, financial data, legal documents), I use HIPAA-eligible deployments or Anthropic's enterprise tier with zero data retention. The architecture itself stays in your environment — your data never goes to a third-party SaaS dashboard.

Several layers. (1) RAG (Retrieval-Augmented Generation) — the AI is forced to ground its answers in your real knowledge base, not its training data. (2) Strict system prompts that say 'if you don't know, say you don't know' instead of guessing. (3) Output validation — for structured tasks (extracting invoice data, classifying tickets), the response is parsed against a schema and rejected if it doesn't conform. (4) Human-in-the-loop for high-stakes outputs — the AI drafts, a person reviews before send. The cost of a hallucination in customer-facing automation is brand damage, so I over-engineer this layer.

Not entirely, and you wouldn't want it to. What AI does well: instant response to common questions (the 60-80% of inbound that has obvious answers), classifying and routing complex tickets to the right human, drafting suggested responses for human approval, summarizing long ticket histories before a rep responds. What it does badly: emotional situations, complex multi-step problems, edge cases not in its training, anything where being wrong has serious cost. Best results come from AI as a force multiplier on a smaller human team — not as a replacement.

RAG (Retrieval-Augmented Generation) is the pattern of giving the AI access to your specific knowledge base at query time, so it answers based on YOUR documents/data instead of its training. Technical implementation: documents are split into chunks, each chunk is embedded into a vector (pgvector or Pinecone), and at query time the most relevant chunks are retrieved and stuffed into the AI's context. You need RAG when (a) the AI needs to know facts about your specific business, customers, or processes, or (b) the source data updates frequently. Most production AI assistants for businesses use RAG — without it, the AI either makes things up or refuses to answer specifically.