For tasks that need judgment, not just rules.
AI Agents
AI agents that can reason, interpret context, and handle situations they weren't explicitly programmed for, backed by your own documents and data.
Automation follows rules: if X happens, do Y. AI agents are different. They read context, make decisions, and handle situations they weren't explicitly programmed for. An automation sends a follow-up email on a schedule. An AI agent reads the meeting notes, determines what follow-up actually makes sense, and drafts a personalized response, without you defining every scenario.
The power comes from RAG (Retrieval Augmented Generation): giving agents access to your specific documents: contracts, policies, client files, procedures. So they answer questions and make decisions based on your actual business information, not generic training data. It's like an assistant who's read everything in your filing system.
Security is built into the architecture, not bolted on. Confidential documents stay on private infrastructure. General-purpose tasks can use cloud APIs. Most businesses need both. We assess your data and risk profile and build accordingly.
Where we work
We build AI agents for companies in St. Augustine and Jacksonville, FL, and for remote clients across the country. Agent work depends on getting access to your real documents and processes, so engagements in Northeast Florida usually open with an on-site session; everything after that is remote.
Common use cases
- Document intelligence: 'What does our contract with Client X say about termination?'
- Client support: Answer questions from your knowledge base, escalate when needed
- Research and analysis: Summarize reports, surface trends across client feedback
- Decision support: 'Based on past projects, what's a realistic timeline for this?'
- Report generation: Pull from multiple sources and generate executive summaries
What you get
- Custom AI agent development for your specific processes
- RAG system connecting the agent to your document repository
- Security architecture matched to your data sensitivity
- Integration with your existing documents, databases, and systems
- Agent training on your actual business information
- Escalation logic and fallback handling
- Ongoing refinement as you identify new use cases
Examples
See what we've built
A voice agent that answers the phone for an auto tint shop, recognizes returning customers, books appointments live into the calendar, and escalates to the owner when needed. An MLB betting agent that reasons across pitcher stats, weather, line movement, and confirmed lineups to surface high-conviction plays daily.
Expand the buildsCommon questions
What is the difference between an AI agent and automation?
Automation follows fixed rules: if X happens, do Y. An agent reads context and decides. An automation sends a follow-up email on a schedule; an agent reads the meeting notes, works out what follow-up actually makes sense, and drafts it -- without you having defined every scenario in advance.
Where does our data go?
It depends on sensitivity, and we decide that with you. Confidential documents stay on private infrastructure; general-purpose tasks can use cloud APIs. Most businesses need both, so we assess your data and risk profile before choosing an architecture.
Can an agent work with our existing documents?
That is usually the point. We connect the agent to your actual contracts, policies, client files, and procedures through a RAG setup, so its answers come from your business information rather than generic training data.
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