Brian Beals
I help enterprise organizations get real results out of their AI, analytics, and automation investments.
I've built and scaled enterprise data and AI practices three times, from a blank page to real revenue. The work I care about is the unglamorous middle: data foundation, integration, governance, the second budget cycle. The part where the technology either pays for itself or doesn't.
- $0 → $20M
- Mainline Information Systems Business Analytics, five years
- $20M → $78M
- Sirius Big Data & Analytics, 3.9× in under four years
Building in public
Marine Forecast
A live marine forecast for my home water, Charlotte Harbor and Pine Island Sound. Parses the NWS coastal waters product into wind, chop, and storm-risk cards, then adds Port Boca Grande tides with a tide-plus-light fishing read, live station observations, and NEXRAD radar cropped to the harbor, plus a red-tide status line from the state's sampling. A verdict line reads the day's chop, storm risk, and rain into a plain call on whether to go, and an hourly wind-and-gust chart shades the day green to red and names the best window to be out. It shows official NWS marine warnings and advisories the moment they are issued, and as an early backstop the browser samples the radar over the harbor and the pass every few minutes, flagging a cell on the water that the inland airport observation would miss. GitHub Actions polls after each NWS issuance and republishes the page as static HTML; the browser refreshes the live pieces on every load. Runs entirely on public NOAA endpoints and GitHub Pages, so hosting is free, making it the only part of this project that respected a budget.
Sector Rotation Screener
A Python pipeline that scores the 11 SPDR sector ETFs against three signals: seasonality, economic-cycle fit, and relative strength. Backtests 15 years against SPY. Runs every Sunday via GitHub Actions, asks Claude for a plain-language read on the output, and commits the dashboard back to the repo. The banner up top reports whether the strategy is beating SPY net of trading costs. Right now it isn't, and the dashboard says so up front.
Lobo
An always-on agent that lives on the Mac mini and texts like a member of the household. Every 30 minutes it checks the GitHub deploys for everything above, retries the transient failures, and texts me only when something stays broken. On Mondays it reads a legislative watch report and texts the highlights. It answers texts on its own iMessage identity, a dedicated Apple ID, because a bot on your own phone number can't tell your texts from its own echoes. Runs locally on OpenClaw with Claude doing the thinking. Getting iMessage working surfaced an upstream bug I reported and got fixed.
I don't sell technology I haven't tried to build myself.
This is where the work goes public. Each project above is a working answer to the same question: what can an enterprise sales leader build without an engineering team, now that the tooling has caught up. New work lands here as it ships.