About Apollo
Apollo combines a massive B2B contact database with AI-powered email generation and sequencing. That means AI is writing personalized messages using Apollo's own prospect data — and if the AI misinterprets that data, the mistake goes out at scale. Bookbag evaluates Apollo's AI-generated sequences before activation, catching hallucinated facts and data misuse.
How It Works Together
Apollo handles generation. Bookbag handles quality control. Every message is evaluated with structured human verdicts against your standards.
- Apollo generates, Bookbag evaluates before activationPull AI-generated email sequences, one-off messages, and phone scripts from Apollo. Route them through Bookbag's AI QA & Evaluation before activating in your Apollo sequences.
- Verdicts catch what Apollo's data can'tEach message gets a safe_to_deploy, needs_fix, or blocked verdict. Bookbag evaluates whether Apollo's AI correctly used the prospect data — wrong titles, outdated companies, and fabricated context get flagged.
- Corrections improve Apollo's AI outputExport needs_fix rewrites as SFT and DPO training pairs. Feed them back to improve how Apollo's AI uses its own data — fewer hallucinations, better personalization accuracy.
Benefits
Apollo's data is only as good as the AI using it
Apollo provides rich contact and company data, but AI can misinterpret or hallucinate on top of it. Bookbag catches the gap between what the data says and what the AI writes.
Scale Apollo sequences safely
Apollo's AI generates messages fast. Bookbag ensures scaling AI-generated outbound doesn't mean scaling quality risk — the majority of messages pass review, and the rest get human attention before activation.
Every Apollo correction is training data
Each needs_fix rewrite becomes a structured before/after pair. Export these to improve future Apollo AI sequence quality — the feedback loop compounds over time.
Frequently Asked Questions
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