TL;DR: Both platforms track your AI visibility across major engines. Only Bullseye turns that data into a weekly sprint plan with implementation support. Peec reports on visibility; Bullseye moves the needle.
What They Share
Both Bullseye and Peec offer prompt tracking, competitor benchmarking, citation/source tracking, and multi-engine coverage (ChatGPT, Gemini, Claude, Perplexity).
What Bullseye Adds
- Weekly Sprint Plans: A prioritized backlog tied to intent and impact
- Implementation Support: We help ship changes, not just report them
- Done-With-You Execution: Pages that get cited, content strategy, engineering fixes
- Revenue Intelligence: Ties AI visibility to actual revenue impact
Feature Comparison
| Feature | Bullseye | Peec |
|---|---|---|
| AI Visibility Tracking | ✓ | ✓ |
| Prompt Monitoring | ✓ | ✓ |
| Competitor Benchmarking | ✓ | ✓ |
| Citation/Source Tracking | ✓ | ✓ |
| Multi-Engine (4+) | ✓ | ✓ |
| Weekly Sprint Plans | ✓ | ✗ |
| Implementation Support | ✓ | ✗ |
| Done-With-You Execution | ✓ | ✗ |
| Revenue Impact Modeling | ✓ | ✗ |
The Bottom Line
Peec is a good analytics tool for teams that only need visibility reporting. Bullseye is for teams that want to actually improve their AI visibility — with sprint plans, implementation support, and revenue-tied metrics.
They measure visibility. We engineer revenue.