AI Agents & Automation
Automate operating work on your systems with production agents, clear ownership, and logs leadership can audit.
We help leadership automate commercial operations, support, finance and reporting, data pipelines, and customer workflows. Engagements deliver agents integrated with your stack, designed so teams run them day to day and executives retain authority on high-impact decisions.
Who this is for
Consumer and B2B brands in regulated and fast-moving markets, including fintech, medtech, consumer, and wholesale.
Approach
- Define outcomes, constraints, and decision rights for the first workflow
- Design the agent operating model against your stack and data boundaries
- Pilot in production conditions with measurable success criteria
- Scale with operating ownership so value compounds after go-live
What we deliver
- Multi-agent workflow design and deployment
- Operations, support, and reporting automation
- Catalog, inquiry, and conversion agent systems
- Systems integration and operating handoff
- Governance, logging, and approval on sensitive actions
Common questions
How much review does a production AI agent need?
Enough that leadership can explain what sent, published, spent, or went live. Sensitive steps stay with a named approver; the agent prepares packets, runs read-only work, and logs every action. Scope grows with the number of systems touched and the compliance bar on each write path. We pilot one workflow with that approval model before expanding.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent does work: it reads from your systems, decides what to do next, takes actions, and reports what it did. The practical difference is accountability. A chatbot has little auditable state, while an agent produces a log of what it read, what it decided, and what it changed. That is why agents can hold operating jobs like reporting, triage, and reconciliation, with approval on the steps that matter.
How long until an agent is running in production?
Discovery usually starts within a week. A first pilot on real data, with logging and a working approve or reject path, typically lands in 30 to 60 days. Production means integration with the systems you already use and a named owner on your team, not a demo alone. We keep the first scope small so the second agent builds on one people actually use.
Do we have to replace our existing systems?
No. Agents are built against the CRM, ERP, ticketing, data warehouse, and internal APIs you already run. Replacing a system of record to accommodate an agent is almost always the wrong trade. If an integration is missing we build that connector, but the default is your estate stays and the agent works inside it.
What technology do you build on?
TypeScript and React on the front end, Cloudflare Workers and Deno at the edge, and Supabase with PostgreSQL and row-level security for data. Agent surfaces use the Model Context Protocol (MCP), remote MCP servers, ChatGPT apps, and Claude connectors against the OpenAI and Anthropic APIs. Delivery runs through GitHub Actions with Vitest and typed contract tests. If you already have a stack, we build inside it.
What work makes a good first AI agent?
Work that runs on named systems and explicit rules, happens often enough to matter, and has a clear owner today. Reporting, ticket triage, data reconciliation, quote and inquiry handling, and onboarding steps qualify. Work that depends on undocumented judgment, or that nobody owns, makes a poor first candidate.
Based in Sarasota, Florida. We work with companies anywhere in the United States and internationally; most engagements are remote.
Contact us about AI Agents & Automation or book a 30-minute call.