Stacked GTM

GTM Council and Frontlines.io
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Deep-dive into how AI is impacting GTM - Each series of 5-7 episodes explores one area from the perspective of top practitioners and vendors.  Presented by the GTM Council - the exclusive community for operational GTM leaders.

  1. 3d ago

    GTM Engineer: Jai @ Deepline

    A mining safety company needed to find prospects, and no pre-built tool existed for that niche. Jai Toor at Deepline pointed a coding agent at the problem. It found a Reddit ML model, combined it with hiring signals, and delivered qualified contacts in under 30 minutes. Jai walks through Deepline's developer-first approach to GTM data infrastructure, from waterfall enrichment to deploying deterministic code-based workflows. He shares how close-lost analysis surfaces niche buying signals specific to each company. Topics discussed: Building code-native GTM infrastructure designed for coding agentsUsing waterfall enrichment across multiple data providers programmaticallyDiscovering niche buying signals through close-lost regression analysisDeploying deterministic plays from iterative agent testingAdopting pay-as-you-go pricing inspired by OpenRouter's modelCentralizing data while decentralizing agent-based rep interfacesGenerating org charts from multiple data sources at $7 eachPredicting cold call answer rates with ML-driven scoring models

    GTM Engineer: Jai @ Deepline

  2. Jul 21

    Agentic Sales: Austin @ Unify

    Austin Hughes, Co-Founder and CEO of Unify, joined Noah and Andy to make the case that offer based outbound is quietly beating the classic ask for a meeting. Austin walks through why Unify skipped Inbound entirely, how offer based outbound is beating classic meeting asks, and why he still believes reps need to own their own prospecting decisions even as agents take over the grunt work.   Topics discussed: - Building offer based outbound instead of meeting asks - Why Unify skipped Inbound as a use case entirely - Letting reps buy in individually before Rev Ops gets involved - Owning a company wide context graph in the data warehouse - Why AE demand surprised Unify more than SDR demand - Why teams now want predictability and cost over frontier models - Advice for hiring the first two outbound reps - Why rep agency still matters as automation increases

    Agentic Sales: Austin @ Unify

  3. Jul 14

    GTM Engineer: Gina @ Dust

    Dust keeps coming up across the GTM engineering community, and this episode explains why. Gina Kabasakalis, Founding US GTM at Dust, has a clear-eyed view of where AI adoption actually breaks down: not in the build, but in the handoff from one technical person to an entire team. She gets into the governance and permission infrastructure that makes multiplayer AI viable, with a concrete example of how RevOps encoding the right Salesforce schema field into a shared agent saves every sales manager from carrying that knowledge individually. She also covers what companies consistently get wrong about ROI measurement, and a new Dust feature called Pods that puts humans and agents in a shared workspace. If you're past the single-agent stage and trying to figure out how to scale this across an org, this one is worth your time. Topics discussed: Why RevOps and IT/CISOs are the two buyer profiles gravitating toward Dust Multi-model routing: matching model complexity and cost to the task rather than defaulting to the highest-reasoning model for everything Encoding institutional knowledge at the system level so ICs stop carrying it individually Piloting vs. rolling out: validating long-term fit vs. the executive air cover and embedded AI ops function a real rollout requires Why time savings is the wrong AI ROI frame and what the CRM hygiene argument actually proves Pods: a shared workspace for humans and agents, with use cases from new hire onboarding to funding round comms Four-component agent framework: trigger event, data sources, recipient and destination, output transformation Agent sprawl: when to consolidate duplicate skills and when distributed creativity is the point

    GTM Engineer: Gina @ Dust

  4. Jul 7

    GTM Engineer: Joe @ Primary Ventures

    Everyone wants to run GenAI workflows. The problem is they're running them on commoditized data, team sizes, job titles, basic firmographics  and wondering why nothing converts. Joe Lehr, Director of GTM Engineering & Innovation at Primary Ventures, works across a portfolio of companies from pre-seed through Series D, backed by a $625M fund five, and he sees this mistake at nearly every stage. Joe gets specific about how he builds out GTM infrastructure from scratch: which signals actually move deals, how Parallel.ai powers Clay's CLAGIN for enrichment at scale, and what it actually takes to move a sales team from single-player AI experiments to a shared multiplayer system built on Supabase and MCP. He also pushes back on a few widely held beliefs, including whether deep hyper-personalization still works, why your first GTM hire should not be a 23-year-old, and why CS may be the most under-automated function in the stack. Topics Discussed: Non-commoditized signal mapping before any AI motion is built Why creativity, not tooling, is the actual constraint at early-stage companies The right first GTM hire: what to look for and what to avoid How Parallel.ai powers Clay's CLAGIN for signal enrichment at scale Mid-funnel data capture and why multi-opportunity edge cases break most tools Build vs. buy: taking vendor sales calls before deciding what to build Going multiplayer with Supabase, MCP, and structured account objects Why deep hyper-personalization is losing ground to smarter segmentation CS as the next frontier for agentic automation, expansion, and churn scoring Observability gaps when vibe-coded internal tools skip engineering involvement

    GTM Engineer: Joe @ Primary Ventures

  5. Jun 30

    GTM Engineer: James @ Profound

    James Underhill, Head of GTM Ops at Profound, runs the ops and systems function through one of the more extreme growth curves you'll hear about: five times ARR in nine months, headcount from sixty to two hundred, and an AE team scaling from thirty toward a hundred by year end. His operating principle is simple: buy infrastructure, build applications. That single bias shapes how his team is staffed, what they buy, what they build, and where they draw the line on both. He built a deal desk bot in under twenty minutes on Dust without writing a line of code.  His team deflected 70% of support inquiries with an internal triage agent. And he replaced the traditional business partner role entirely by giving the field direct, semantic access to their own data. Topics Discussed Why calibrating to the first derivative of growth matters more than current headcount Using Dust as a low-code agent platform across GTM, CS, and internal ops Deflecting 70% of support inquiries with a confidence-gated triage agent Buy infrastructure, build application as a disciplined team operating principle Why GTM engineering at this level requires actual software engineers, not just technical curiosity GitHub fluency as the new hiring proxy for this function Snowflake plus a semantic layer as the foundation for real-time, conversational data querying Centralizing Claude Code skills in Notion for rep-accessible, field-ready workflows The hidden maintenance cost of vibe-coded tools and why it compounds fast Replacing business partner roles with self-serve data access Where Gong's call intelligence falls short and why that becomes a build opportunity How to decide what to build versus buy when your team could technically do both

    GTM Engineer: James @ Profound

  6. Jun 23

    Agentic Sales: Seth @ Sandler

    Seth Marrs spent six years at Forrester as their lead sales AI and technology analyst, which means he evaluated every major wave of GTM tech and has the receipts to back his calls. Now at Sandler as Chief Strategy Officer, he's seeing the practitioner side up close. His read on the agentic SDR market is blunt and sits well outside the vendor narrative, and he supports it with something most guests can't: actual data on what happens to sellers when you train them, test them, then watch them on live calls the next day. He also gets specific on the parts most people gloss over. Why removing the seller from data capture is the first move that makes everything downstream possible. Why "next best action" is a flawed idea that quietly argues against needing sellers at all. And why the agentic opportunity in sales may sit with the SE and the human selling skill, not the SDR everyone is trying to automate away. Topics discussed: Why "fire your BDRs and automate with AI" is the vendor red flag to watch for The recording-permission gap that breaks the data flywheel on outbound Keeping BDR research depth while moving from 10 calls to 50 Sales motion as the deciding factor: high-velocity vs. long-cycle BDR economics Removing the seller from data capture entirely to build a reliable dataset Compete score: revenue per rep normalized across net-new, farmer, and CSM roles The 40 to 80 to 40 adoption collapse measured on live calls, not surveys Certifying sellers on what they actually say to customers, not test completion Why top-three suggested actions beat a single prescribed next best action The agentic SE: training a bot on the technical layer while humans own the conversation Extracting intelligence from the bottom of the funnel where capture doesn't exist Buy the infrastructure, build the AI layer on top: the maintenance trap with vibe-coded tools

    Agentic Sales: Seth @ Sandler

  7. Jun 16

    GTM Engineer: Ryan CRO @ Quotapath

    Ryan Milligan started at Quotapath as Director of RevOps, spent four and a half years building the data and systems foundation, and is now CRO. His team has run at 100% blended quota attainment in 8 of the past 10 quarters, never below 90%, and has grown closed ARR per rep 1.7x in 18 months. The GTM engineering team doing most of the building is two people. In this episode, Ryan gets specific about how the whole system works: the data architecture decision he made on day one that still underpins everything, how he splits Dust and Claude into distinct roles across the sales cycle, and why he thinks the current wave of everyone building their own tools is a bubble with a painful correction coming. He also makes a sharp case that comp plan design is one of the highest-leverage tools a CRO has for changing the mix of revenue being closed, not just paying people. Topics discussed: Processing data in the warehouse nightly and reverse ETL-ing into CRM so both always speak the same language The build vs. buy litmus test: uniquely bespoke and relatively fixed vs. everything else Rep-built V1 prototypes handed to RevOps for productionizing and org-wide rollout Dust as system of record, Claude as system of action, and how they split across the sales cycle Thursday multi-thread standup: every rep required to arrive with all multi-threads queued for active opps The "what would it take to close twice as many deals" framework for identifying which agents to build next Warm outbound architecture using Clay, Unify, and product interaction data as intent signals Comp plan design as a lever for changing the shape of revenue reps close, not just incentivizing volume Why data architecture is the only real defense against confident AI hallucination GTM engineer defined as the owner of the full prospect-to-renewal lifecycle Listen to more episodes:  Apple  Spotify  YouTube

    GTM Engineer: Ryan CRO @ Quotapath

  8. Jun 9

    Agentic Sales: Mark @ Canibuild

    Mark Deacon, CRO of CaniBuild, has gone further than most leaders talking about agentic GTM. He's actually built it, measured it, and has the numbers to back it up: a 400% improvement in revenue per human headcount and a demo-to-close rate now sitting above 60%, more than double what it was before AI. What separates this conversation is the operational depth. Mark walks through the exact sequencing logic behind their AI SDR workflow, the buy vs. build decision criteria he applies to every tool, how he onboards and governs AI agents the same way you would a new hire, and the centralized AI operating system he built from scratch to keep an 80-person company running with consistent governance across the stack. Topics Discussed: 400% revenue per headcount improvement and 60%+ demo-to-close rate after AI deployment SMS-first sequencing strategy that increased AI SDR pickup rates through A/B testing ICP based routing logic that books demos directly into the right rep's calendar Buy vs. build decision framework based on uptime requirements and maintenance cost Two-to-three month AI agent onboarding process before handoff to the business owner Slack-native AI chief of staff architecture that routes tasks across a team of specialized agents One-script Claude Code config deployment for consistent governance across all team members AI-first vs. AI-only operating model and why the 80/20 split on support tickets matters Listen to more episodes:  Apple  Spotify  YouTube

    Agentic Sales: Mark @ Canibuild
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Deep-dive into how AI is impacting GTM - Each series of 5-7 episodes explores one area from the perspective of top practitioners and vendors.  Presented by the GTM Council - the exclusive community for operational GTM leaders.

Information

  • Creator
    GTM Council and Frontlines.io
  • Years Active
    2026
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © GTM Council and Frontlines.io
  • Show Website
    Stacked GTM

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