Smartling's BDRs Send 10x More Personalized Emails After Deploying Apollo AI Research Automation

Background

Smartling's BDR team was spending the majority of its working time on manual prospect research, limiting the number of personalized outreach emails that could be sent. As the company sought to expand targeting beyond its established tech-company base into less familiar verticals — government, life sciences — the research burden intensified. Grace Feeney needed a way to give junior BDRs access to the research quality that usually took years of vertical expertise to develop.

What Was Implemented

• Deployment of Apollo AI Power-ups, a feature enabling prompt-driven AI research that runs automatically across Apollo's database, LinkedIn, and web sources
• Contact-level qualifying prompt: automated query identifying whether each prospect had professional experience with multilingual website translation, surfacing the result directly in the CRM alongside the contact record
• Account-level qualifying prompt: automated scan of each target company's website to detect presence (or absence) of language toggle and identify gaps in existing translations — producing a specific, personalized problem statement for BDR outreach
• Integration of Power-up outputs directly into Apollo outbound sequences, enabling reps to insert AI-researched context into personalized email copy at scale
• BDR enablement: structured access to Power-ups as a skill-building tool for junior reps learning new market segments

Results

Smartling's BDRs were able to send 10x more personalized emails than before, with Grace Feeney reporting that "productivity and growth has skyrocketed." The quality of personalization did not decline — reps shifted from generic hooks to specific, company-relevant observations surfaced by AI research. No pipeline value, conversion rate, or revenue metric was reported in the primary source. The 10x figure is self-reported by the sales leader in a vendor-commissioned case study.

Lessons

• AI research automation is most powerful when targeted at the highest-friction step in the sales workflow — in this case, the manual research that constrained BDR output
• Prompt engineering for sales qualification requires careful scoping: effective prompts return a single, actionable signal (e.g., "does this prospect have translation experience?") rather than general summaries
• AI-surfaced, company-specific context (e.g., a detected gap in a company's multilingual website) is more effective for opening conversations than generic demographic personalization
• Expanding into new market verticals becomes feasible when AI can compress the learning curve for junior reps who lack domain expertise
• Productivity gains in email volume are a necessary but insufficient metric — pipeline and conversion impact should be tracked to confirm revenue value