Systems we've built

The sales team had no systematic way to identify warm prospects. Outbound was cold-list-based, and reps were spending hours manually scanning LinkedIn for signals — job changes, company news, hiring patterns — with no consistency and no way to scale.

Built an automated LinkedIn signal pipeline that monitors target accounts for buying signals — role changes, company growth, content engagement — enriches matched contacts with firmographic data, scores them against the client's ICP, and routes qualified leads directly into the outreach sequence.

The team went from zero systematic warm prospecting to a steady stream of signal-sourced leads with no manual research required.

Dormant pipeline was invisible. Demo no-shows, ghosted follow-ups, and stalled opportunities sat in the CRM with no systematic way to resurface them. Reps moved on; revenue stayed on the table.

Built a re-engagement engine that identifies ghosted leads based on activity gaps, monitors for re-engagement signals (new stakeholder activity, company news, renewed website visits), and triggers personalized multi-threaded outreach sequences with Slack-based approval for gift sends.

Recovered dormant pipeline that had been written off. Leads that had gone dark for weeks re-entered active conversations without reps doing any manual follow-up.

When champions left for new companies, the team had no way to know — let alone act on it. Former buyers who already understood the product were starting fresh at new organizations, and nobody was reaching out.

Built an automated monitoring system that tracks job changes for key contacts — champions, decision-makers, power users — and triggers personalized outreach when they land at a new company. The system enriches the new company against ICP criteria before routing, so reps only get notified for high-fit moves.

Created a steady stream of warm introductions into new accounts via people who already know and trust the product. The warmest possible outbound channel, fully automated.

The sales team needed to prospect at the facility level — individual plants and production sites — but had no systematic way to identify decision-makers at each location. Contact data was scattered, incomplete, and not segmented by facility type or role.

Built a contact enrichment and segmentation pipeline that identified plant-director-level contacts across target manufacturers, enriched them with facility-level data (location, production type, headcount), and organized them into prospecting segments by manufacturer and role.

Delivered a structured, segmented contact database that the team could immediately load into outreach sequences — organized by manufacturer, facility, and decision-maker role.

Sales leadership had no reliable way to stay aligned on deal progress without sitting through long pipeline reviews. Call transcripts went unread, and SEs were manually summarizing every conversation. Critical context was getting lost between meetings.

Built an automated digest system that ingests call transcripts, runs AI summarization to extract key themes, objections, next steps, and competitive mentions, and delivers a structured summary to Slack. The digest runs after every recorded call — no manual input required.

Team alignment without manual notes. Leadership gets deal context in real time, SEs stop writing summaries, and nothing falls through the cracks between pipeline reviews.

Competitive battlecards were outdated PDFs that nobody used. Account-based campaigns took weeks to build because every piece — messaging, targeting criteria, competitive positioning — was created from scratch each time.

Built an AI-powered generation system that pulls from competitive intelligence sources, CRM data, and product documentation to produce up-to-date battlecards and account-specific campaign briefs on demand. Reps request a battlecard or ABM brief; the system delivers it in minutes.

Faster competitive and account-based GTM execution. Reps stopped using stale competitive docs and started using generated battlecards that reflect current market positioning.

Enterprise AEs were spending 5+ hours per week manually reading earnings call transcripts to identify buying signals for target accounts. By the time a rep surfaced relevant intelligence — budget shifts, strategic priorities, competitive mentions — competitors had already booked the meeting.

Built an AI-powered earnings call analysis tool that ingests public earnings transcripts, extracts buying signals (budget commentary, technology mentions, strategic shifts, competitive displacement indicators), scores them against the client's ICP, and delivers sales-ready briefings to the assigned rep 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 days to hours.

The team was finding government RFPs after they were already published — too late to influence requirements or build relationships. By the time the formal process started, incumbents had already shaped the evaluation criteria.

Built a pre-RFP signal pipeline that monitors government procurement sources, budget documents, and agency hiring patterns for early indicators of upcoming opportunities. Each signal is AI-scored for ICP fit and routed to the right rep with context on timing, agency, and estimated deal size.

The team gained early visibility into high-value government opportunities weeks or months before formal RFPs dropped, giving them time to build relationships and position ahead of competitors.

The sales team was pitching efficiency improvements to plant managers but had no way to quantify the value in the prospect's own terms. ROI conversations were hand-wavy and unconvincing — no concrete numbers tied to the prospect's specific operation.

Built an interactive calculator that lets prospects input their own OEE (Overall Equipment Effectiveness) metrics, labor costs, shift patterns, and downtime data to model the financial impact of the client's solution. The output is a shareable business case with specific dollar savings.

Reps started leading discovery calls with a value quantification tool instead of a slide deck. Prospects could see the ROI in their own numbers, making internal champion-building significantly easier.

The sales team had no visual way to see where their customers and prospects were located relative to each other. Territory planning was spreadsheet-based, whitespace identification was guesswork, and nobody could answer 'where should we focus next?' with data.

Built a geographic mapping tool that plots all customer and prospect facilities, overlays lookalike-radius analysis to identify whitespace opportunities, and segments by account status, facility type, and deal stage. The map updates automatically from CRM data.

Territory planning shifted from gut feel to data-driven whitespace analysis. The team could visually identify clusters of prospects near existing customers and prioritize territories with the highest density of ICP-fit facilities.

Sales management had no visibility into who was prospecting where, how often, or with what cadence. Activity data existed in Salesforce and the sequencing tool, but it wasn't aggregated into anything actionable. Coaching conversations were based on gut feel, not data.

Built a Salesforce-native prospecting activity dashboard that aggregates outreach data across reps — calls, emails, sequences, LinkedIn touches — and surfaces activity patterns, coverage gaps, and cadence consistency in a single view.

Sales managers gained real-time transparency into prospecting behavior. Coaching shifted from 'are you prospecting enough?' to specific, data-backed conversations about coverage, cadence, and targeting.