Endeavor for domain experts and analysts
Endeavor puts the people who understand the work in charge of the agents doing it. You define what good looks like, score real outputs, and decide when an agent is ready — without writing code or joining an engineering queue.
Your judgement is the missing input
Most AI tooling is built for engineers, which means the person who can actually tell whether an answer is right is the one furthest from the controls. Model quality gets debated in meetings and tracked in spreadsheets that nobody trusts a month later.
Endeavor inverts that. Scoring happens in the platform, against real cases, and becomes the record that decides whether an agent ships.
What you actually do
Describe the work
Upload an interview, a procedure, or a runbook. Compass AI turns it into discrete tasks with objectives and KPIs attached.
Bring real examples
Test cases are the inputs you already deal with. Thirty to a hundred gives a reliable comparison between versions of a task.
Score the outputs
Rate results directly in the platform - a scale, a yes/no on invented content, or a percentage. This replaces the spreadsheet as the source of truth.
Compare and choose
Run the same task against different models side by side and pick the cheapest one that still meets your bar.
Approve or send it back
Promotion to production is an evidence-based decision you make, not a handover you hope goes well.
What you no longer depend on
- An engineer to change a prompt.
- A spreadsheet to track which model version was better.
- A demo to decide whether something is production-ready.
- A rebuild when a cheaper model becomes good enough.
- Trust as a substitute for measurement.
Frequently asked questions
Do I need to write code or prompts?
No. Compass AI decomposes your process from documents you already have, and you work with tasks, test cases, and scores rather than code.
What does scoring actually involve?
Reviewing real outputs and rating them - a scale, a yes/no on whether an answer contains something invented, or a percentage. Those scores become the evidence that promotes or blocks an agent.
How many examples are needed to trust a result?
Thirty to a hundred test cases gives a consistent comparison across different versions of a task. Bootstrapped examples are fine to start, but they should be replaced with real ones as soon as you have them.
Can I change how an agent behaves without an engineer?
Yes. Instructions can be revised, versioned, and rolled back, and the underlying model can be swapped, without a developer changing code.