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Sales AI Agents.
Book 3x More Meetings.
Deployed in Days.
Intelligent SDR agents that research prospects, personalize outreach, and respond to leads in real-time—while you focus on closing.
Hi Sarah,
I noticed Acme recently migrated to Kubernetes based on your team's blog post last month—congrats on the successful rollout!
I'm curious: as you scale your microservices architecture, how are you handling observability across clusters? We've helped companies like Stripe and Figma cut their mean-time-to-resolution by 60% after similar migrations.
Would you be open to a 15-minute call to share what we've learned? I can work around your schedule.
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"If you're looking for a team that can support you, help scale your product, and be a true technical partner, slashdev.io is the way to go. Features that took months to finish are getting done in days. They have completely changed my business!"
SDR teams are maxed out
Manual prospecting and outreach can't scale with pipeline demands
Slow Response
42-hour average response time loses 78% of leads.
Research Time
SDRs spend 6+ hours daily on research vs selling.
Generic Outreach
Templated emails get ignored. Personalization fails.
Missed Follow-ups
Hot leads go cold when follow-ups are delayed.
AI SDRs that never sleep
Agents that handle the full sales development workflow autonomously
Prospect Research
AI agents scrape LinkedIn, company websites, and news to build rich prospect profiles before outreach.
Personalized Messaging
Generate hyper-personalized emails that reference specific company initiatives, tech stack, or recent news.
Instant Response
Reply to inbound leads in seconds, not hours—qualifying and routing while prospects are still engaged.
Meeting Scheduling
Automatically handle back-and-forth scheduling, timezone conversion, and calendar invites.
Multi-Channel Outreach
Coordinate sequences across email, LinkedIn, and other channels with intelligent follow-up timing.
Performance Analytics
Track open rates, reply rates, and meeting conversions to continuously improve messaging.
From lead to meeting
An autonomous workflow that books qualified meetings
Lead Enrichment
When a new lead enters your CRM, the agent immediately researches the prospect across multiple data sources.
// New lead webhook trigger
on_lead_created(lead) {
// Enrich from multiple sources
linkedin = scrape_linkedin(lead.email)
company = get_company_data(lead.domain)
news = search_recent_news(lead.company)
tech_stack = detect_technologies(lead.domain)
// Build prospect profile
profile = {
role: linkedin.title,
tenure: linkedin.start_date,
company_size: company.employees,
funding: company.funding_stage,
tech_stack: tech_stack,
recent_news: news[0:3],
mutual_connections: linkedin.shared
}
}Personalized Outreach
Using the enriched profile, the agent crafts a highly personalized email that references specific details about the prospect.
// Generate personalized email
prompt = """
Prospect: {profile}
Product: {our_product}
Similar customers: {case_studies}
Write a cold email that:
- References their recent news/initiative
- Connects to a specific pain point
- Mentions a relevant case study
- Asks one clear question
- Is under 150 words
"""
email = llm.generate(prompt)
send_email(lead.email, email)
schedule_followup(lead, days=3)Intelligent Follow-Up
The agent monitors for replies and engagement, automatically sending contextual follow-ups or adjusting the sequence.
// Monitor engagement
on_email_opened(lead) {
update_engagement_score(lead, +10)
}
on_no_reply(lead, days=3) {
// Generate contextual follow-up
followup = generate_followup({
previous_email: last_email,
new_context: get_recent_updates(lead),
attempt: sequence_step
})
send_email(lead.email, followup)
}
on_reply(lead, message) {
// Classify intent
intent = classify(message)
if intent == "interested":
route_to_scheduler(lead)
elif intent == "objection":
generate_response(message, objection_handlers)
}Meeting Booking
When a prospect expresses interest, the agent handles all scheduling logistics and confirms the meeting.
// Handle scheduling
on_intent_meeting(lead, message) {
// Parse availability from message
proposed_times = extract_times(message)
// Check AE calendar
available_slots = get_calendar_slots(
ae_calendar,
timezone=lead.timezone
)
// Find match or propose alternatives
if overlap(proposed_times, available_slots):
confirm_time(lead, overlap[0])
else:
suggest_times(lead, available_slots[0:3])
// On confirmation
create_calendar_event({
attendees: [lead.email, ae.email],
title: "Intro Call: {lead.company}",
description: generate_briefing(lead.profile)
})
notify_ae(lead, "Meeting booked!")
}Agent architecture
How we build AI SDRs that convert
sdr_agent:
name: "AI SDR"
enrichment: [linkedin, clearbit, apollo, zoominfo]
models: { email: "claude-opus", intent: "haiku" }
personalization:
required: [company_ref, pain_point, social_proof]
max_length: 150_words
sequence: [email, linkedin, followup, message, breakup]
intent_routing: { interested: scheduler, objection: handler }
integrations: [salesforce, google_calendar, gmail]Multi-Source Enrichment
Combine data from 6+ providers to build comprehensive prospect profiles that enable truly personalized outreach.
Intent Classification
Real-time classification of reply intent allows the agent to route appropriately—interested leads to scheduling, objections to handlers.
Multi-Channel Sequences
Coordinate touchpoints across email and LinkedIn with intelligent timing based on engagement signals.
CRM Integration
Bi-directional sync with Salesforce/HubSpot ensures all activity is logged and leads are properly qualified.
Impact on sales teams
Metrics from clients using our AI SDR agents