# ZLDA Labs

- **Event:** [Built with Opus 4.7: a Claude Code hackathon](https://cerebralvalley.ai/e/built-with-4-7-hackathon)
- **When:** Apr 21 at 12:00 PM – Apr 27 at 2:00 AM (EDT)
- **Where:** Online
- **Team:** [David Tanis](https://cerebralvalley.ai/u/Tanis226)
- **GitHub:** https://github.com/tanis226-hue/operator-agent
- **Demo video:** https://www.loom.com/share/e1fbff0ad5a24d56b30d9ea4db564db3
- **Gallery:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/215

OpsAdvisor is an operations diagnosis and improvement tool that runs the same Lean Six Sigma workflow I run manually today, compressed into a single structured pass. My background is operations, systems, analytics, and process improvement across healthcare, legal, and construction. In my current role I'm building structure inside a fast-growing physician group where complexity lives across SOPs, handbooks, handoffs, and reporting. The hard part isn't storing that information. It's turning it into principled insight and clear next actions, and that is the gap OpsAdvisor is built to close.

For this MVP, OpsAdvisor focuses on the Lead Intake and Conversion Bottleneck, a problem almost every services business has and almost none diagnose rigorously. It takes a business context brief, a process note, and workflow data, then runs a structured Define, Measure, Analyze, Improve, and Control flow to identify where the process is leaking, what is driving the loss, which fix should happen first, and how to monitor the change so it does not decay. The output is not a chat transcript and not a dashboard. It is an executive summary, baseline metrics, root cause analysis, a ranked recommended fix, a workflow rule update, and a control package with monitoring guidance and alert thresholds. As a concrete example, a run might surface that Stage 3 to Stage 4 conversion drops to 18 percent driven by SLA breaches on intake callbacks, and recommend a 4 hour callback rule paired with a daily breach alert wired into the control plan.

Three things I would want judges to weigh. First, the output is deterministic in shape and validated against a TypeScript contract before it leaves the API, so the model cannot quietly change the report structure between runs. Second, nothing is persisted. Analysis runs in session and any database credentials supplied for a one time pull are never stored or logged, which is a deliberate choice for the kind of operational data this tool is meant to handle. Third, this is genuinely the diagnostic I do by hand for real operators today, not a hypothetical use case dressed up for a demo

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Markdown version of https://cerebralvalley.ai/e/built-with-4-7-hackathon/hackathon/gallery/215. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt