Systems we've built

The sales team had no systematic way to identify warm prospects. Outbound was cold-list-based with a 1.8% reply rate. Reps spent 6-8 hours per week manually scanning LinkedIn for signals — job changes, company news, hiring patterns — with no consistency, no prioritization, and no way to scale beyond what one person could monitor in a browser tab.

Built an automated LinkedIn signal pipeline using Clay and Sales Navigator that monitors 2,000+ target accounts for buying signals — role changes into decision-maker positions, company growth indicators, hiring surges in relevant departments, and content engagement patterns. Each signal is enriched with firmographic data, scored against the client's ICP, and qualified signals are routed directly into personalized outreach sequences within 24 hours of detection.

The team went from zero systematic warm prospecting to a steady stream of signal-sourced leads. Reply rates on signal-triggered outreach averaged 14% — 7x higher than their previous cold lists. Pipeline generated from signal-sourced outbound reached $2.1M in the first quarter.

Dormant pipeline was invisible. Over 200 opportunities had gone dark — demo no-shows, ghosted follow-ups, stalled evaluations — sitting in the CRM with no systematic way to resurface them. Reps moved on to new pipeline; $3.4M in previously qualified revenue sat untouched.

Built a re-engagement engine that identifies ghosted leads based on activity gaps (14+ days no response after a demo, 30+ days stalled in evaluation), monitors for re-engagement signals (new stakeholder activity at the account, renewed website visits, company news), and triggers personalized multi-threaded outreach sequences. Includes Slack-based approval workflow for gift sends to high-value stalled deals.

Recovered $890K in pipeline from leads that had been written off. 23% of re-engaged leads booked a follow-up meeting without any manual rep effort. The system now runs continuously, automatically surfacing ghosted leads the moment a re-engagement signal appears.

When champions left for new companies, the team had no way to know — let alone act on it. Former buyers who already understood and trusted the product were starting at new organizations with fresh budget and a mandate to build. Competitors were getting there first because nobody was monitoring these moves systematically.

Built an automated monitoring system that tracks job changes for 500+ tagged contacts — champions, decision-makers, power users, and advocates — across LinkedIn and enrichment sources. When a tracked contact moves, the system enriches the new company against ICP criteria (size, industry, tech stack, funding stage) and only triggers outreach for high-fit moves. Outreach is personalized with shared history context and routed to the original account owner.

Created the team's highest-converting outbound channel. Champion-sourced outreach converts to meetings at 31% — compared to 4% for standard cold outbound. The system runs continuously and has surfaced warm introductions into 12 net-new accounts per month that would have been missed entirely.

The sales team needed to prospect at the facility level — individual plants and production sites — but corporate-level CRM data was useless for reaching plant directors and operations managers. Contact data was scattered across LinkedIn, industry databases, and spreadsheets. Building a prospecting list for one manufacturer took a rep 3-4 hours of manual research.

Built a contact enrichment and segmentation pipeline using Clay that identified plant-director-level contacts across 42 target manufacturers. Each contact was enriched with facility-level data — plant location, production type, estimated headcount, and technology stack — then organized into prospecting segments by manufacturer, facility type, and decision-maker role. The output feeds directly into Salesforce with territory-based routing.

Delivered a structured, segmented contact database of 430+ verified plant-level decision-makers that the team loaded directly into outreach sequences. What previously took 3-4 hours per manufacturer now happens automatically. The team's addressable market visibility increased from ~15% to 85% coverage of target facilities.

Enterprise AEs were spending 5+ hours per week manually reading earnings call transcripts to identify buying signals for 200+ target accounts. By the time a rep surfaced relevant intelligence — budget commentary, strategic shifts, competitive displacement mentions — the information was days old. Competitors with faster systems were booking meetings on the same signals first.

Built an AI-powered earnings call analysis system that ingests public earnings transcripts within hours of filing, extracts buying signals (budget allocation changes, technology investment mentions, efficiency mandates, competitive displacement indicators, leadership commentary on strategic priorities), scores each signal against the client's ICP and deal context, and delivers sales-ready briefings to the assigned rep via Slack and email within 24 hours of the call.

Reps went from spending hours reading transcripts to receiving prioritized, actionable intelligence automatically. Signal-to-outreach turnaround dropped from 4-5 days to under 24 hours. Meeting book rate on earnings-signal-triggered outreach was 3.2x higher than the team's standard cold outbound.

Sales leadership had no reliable way to stay aligned on deal progress without sitting through 90-minute pipeline reviews. Call transcripts from Gong went unread. SEs were spending 2-3 hours per week manually summarizing conversations. Critical context — objections raised, competitors mentioned, next steps promised — was getting lost between meetings.

Built an automated digest system that ingests call transcripts after every recorded conversation, runs AI summarization to extract key themes (pain points confirmed, objections raised, competitors mentioned, next steps committed, stakeholders identified), and delivers a structured summary to a dedicated Slack channel. Each digest links to the full recording and tags the relevant deal in Salesforce.

Team alignment without manual notes. Leadership gets deal context in real time. SEs recovered 2-3 hours per week. Pipeline reviews shortened from 90 minutes to 35 because everyone arrives informed. Nothing falls through the cracks between conversations.

Competitive battlecards were outdated PDFs in a Google Drive folder that nobody opened. When reps encountered a competitor in a deal, they either winged it or pinged the product marketing Slack channel and waited hours for a response. Account-based campaigns took 2-3 weeks to build because messaging, targeting criteria, and competitive positioning were created from scratch every time.

Built an AI-powered generation system that pulls from live competitive intelligence sources (G2 reviews, competitor changelog, pricing pages, job postings), CRM win/loss data, and internal product documentation to produce up-to-date battlecards on demand. Reps request a battlecard via Slack command; the system returns a structured competitive brief in under 3 minutes with current positioning, objection handling, and landmine questions.

Competitive win rate improved as reps entered deals prepared instead of reactive. Battlecard usage went from near-zero to 70%+ of competitive deals. ABM campaign creation dropped from weeks to hours because messaging frameworks are pre-generated and refreshed automatically.

Sales management had zero visibility into prospecting behavior across 18 reps. Activity data was fragmented across Salesforce, Outreach, and LinkedIn — never aggregated. Managers couldn't identify who was under-prospecting, which territories had coverage gaps, or whether reps were following cadence. Coaching was based on gut feel and quarterly results, not leading indicators.

Built a Salesforce-native prospecting activity dashboard that aggregates outreach data across all reps and channels — calls logged, emails sent, sequences active, LinkedIn touches, meetings booked. Surfaces real-time patterns: activity consistency (daily/weekly cadence), territory coverage gaps (accounts not touched in 30+ days), and channel mix. Includes manager alerts when a rep's activity drops below threshold for 3+ consecutive days.

Coaching shifted from 'are you prospecting enough?' to specific, data-backed conversations: 'You're not touching accounts in the Northeast territory' or 'Your call-to-meeting ratio dropped this week — let's review messaging.' Ramp time for new hires dropped because managers could identify skill gaps from activity patterns in week 2, not month 3.