Before MQ Hunter existed, SDRs and AEs worked through market-qualified leads by hand. The average ramped SDR completed only 10 MQLs per hour because every lead required pulling and cross-checking context across Salesforce, Gong, and Glean.

The workflow was scatterbrained beyond just being slow. There was no central view of which MQLs had been actioned, so the team could see what was coming in but not what had been done. Slack notifications fired exactly once per MQL, ever, so anything missed in the moment effectively disappeared. And there was no segmentation: open-opportunity MQLs, where the AE was already running a deal cycle with the prospect, were not flagged. SDRs were at risk of actioning those MQLs without realizing it, which could break the existing process.

MQ Hunter turns the same workflow into a one-click action queue. Each MQL shows up with the context already pulled in (current segment, industry, campaign, the event or webinar that triggered the lead) and a pre-populated email and LinkedIn message drafted and ready for send.

The throughput change is the headline. Where a ramped SDR previously moved through 10 MQLs per hour, the same SDR can now move through 100. Each draft has already been scored against an internal personalization rubric, and only the messages that pass a benchmark land in the queue. The result is faster output that is also more personalized and more professional than what the team was producing manually.

MQ Hunter sorts MQLs by persona so the personalization bar matches the audience. Persona A is director-plus contacts at the top of the funnel, where messaging has to feel hand-written or it reads as spam. Persona B is middle managers, where personalization still matters but the bar is lower. Persona C is analysts, where the priority shifts to leading with the facts and the value of the meeting.

The same logic applies across the rest of the filters. SDRs can sort by persona, by personalization score, or by individual AE. If Brock has a recent triggering event worth chasing, the queue can be filtered down to just his MQLs in one click.

The most important filter in MQ Hunter is the one for open-opportunity MQLs. In the live demo, 34 of the 166 MQLs in the queue belonged to opportunities the AE was already working. Those should not be touched the way a normal MQL is, so MQ Hunter flags them prominently and sorts them to the bottom of the queue. After the normal MQLs are cleared, SDRs work the open-opportunity ones with a different approach, usually syncing with the AE first to learn where the deal cycle stands.

The queue also moves cleanly through three stages. A checkbox advances an MQL from Stage 1 (email and LinkedIn) to Stage 2 (follow-up), and from Stage 2 into a library of everything that has been actioned. The library is what makes the system referenceable in conversations with AEs and other internal partners, so the team is not relying on memory to retrace what has already been done with a given contact.

MQ Hunter is in early access with two SDRs. In the first week, those two reps booked four net new business meetings off the tool, which means four new deal cycles opened with high-value contacts at high-value companies.

The improvement is not just speed. Better messaging, better visibility into where each MQL stands, and a clearer signal on which MQLs need a different approach all show up in the same workflow. Manual cross-tool research becomes a queue, one-off Slack pings become a persistent action view, and risky MQLs get the treatment they actually need.