Early GTM Hire (AE/BDR)

Overview

You're the first dedicated GTM hire at a 6-person startup selling AI data governance infrastructure to enterprises. The founders (Stanford CS dropout + co-founder) landed Disney and a few other enterprise customers, but there's no playbook yet. You'll be doing everything: finding prospects, running demos, closing deals, and probably helping with implementation. This is a "figure it out as you go" role at the earliest possible stage.

Role Snapshot

Company Context

Stage: Seed (just raised millions from Susa Ventures and Wischoff Ventures)

Size: 6 employees total

Growth: Scaling "fast across engineering and GTM teams" - you'd likely be GTM hire #1 or #2

Market Position: Category creator in AI data governance - no established competitors mentioned, which means heavy buyer education

GTM Reality

Pipeline Sources:

• 10% Inbound - mostly founder network and LinkedIn post reach
• 90% Outbound - you'll be building lists of enterprise companies deploying AI, doing cold outreach to legal/compliance/data teams
• 0% Partners/Referrals - too early for meaningful channel

SDR/AE Structure: You are both. No one else is doing this yet.

SE Support: Founders will do technical deep-dives, but you'll need to learn the product well enough to run initial demos yourself.

Competitive Landscape

Main Competitors: Data governance platforms (Collibra, Alation), data quality tools (Monte Carlo, Bigeye), compliance software - but no direct comp in "AI trust layer" yet

How They Differentiate: First-mover in AI-specific data governance - not just cataloging data but making it AI-ready and trustworthy

Common Objections: "We already have a data governance tool", "Our data is fine", "Is this really necessary?", "Can't our data team handle this?"

Win Themes: Risk mitigation (legal exposure from bad AI outputs), speed to AI deployment, Disney as reference customer

What You'll Actually Do

Time Breakdown

Prospecting (40%) | Active Deals (30%) | Product Learning (15%) | Internal (15%)

Key Activities

• Cold Outbound: Build lists of enterprises deploying AI tools (Fortune 500, fast-growing tech companies). Cold email and LinkedIn to Chief Data Officers, VPs of AI, Heads of Legal/Compliance. Most won't respond. You're trying to get 5-10 initial conversations per week.
• Running Discovery Calls: Figure out if they're even thinking about AI data governance yet. Most aren't. You'll spend a lot of time educating on why this matters before you can even position the product.
• Product Demos: Walk through how Human Delta scans their knowledge bases, identifies contradictions/outdated info, and creates audit trails. Founders will help on technical questions, but you need to handle the first call yourself.
• Building Sales Collateral: There's probably no pitch deck, case studies, or ROI calculator yet. You'll create these as you go based on what's resonating in conversations.
• Managing Trials/POCs: Enterprise deals require proof. You'll coordinate technical pilots with their data teams, chase for feedback, handle objections when things don't work perfectly.
• Closing and Negotiation: Work through procurement, legal reviews, security questionnaires. First-time vendor at most of these companies means extra scrutiny.
• Weekly Syncs with Founders: Report on what's working, what objections you're hearing, what messaging lands. You're building the playbook together.

The Honest Reality

What's Hard

• You're selling something most companies don't know they need yet. Lots of calls explaining "what is AI data governance" before you even get to your product.
• No established process. You'll try things that don't work. No one can tell you "here's how we do it" because this is the first time.
• Long enterprise cycles with limited leverage. You can't move 10 deals at once when you're also the one building the presentation and writing the proposals.
• Founder-led sales worked (Disney deal), but that doesn't mean your outbound will. You're figuring out if this is repeatable without the Stanford/founder halo.
• When technical questions come up mid-deal, you're waiting on founders who are also writing code. Slow responses can kill momentum.

What Success Looks Like

• 3-5 new enterprise customers signed in your first year
• A repeatable outbound process documented: ICP, messaging, objection handling, demo flow
• Built enough pipeline that they can justify hiring a second GTM person under you

Who You're Selling To

Primary Buyers:

• Chief Data Officers / VPs of Data at enterprises deploying AI
• Heads of AI/ML teams who've hit data quality issues in production
• Legal/Compliance leaders worried about AI liability

What They Care About:

• Risk mitigation: Can we trust our AI not to give wrong/dangerous answers?
• Speed: How fast can we safely deploy AI tools without data cleanup blocking us?
• Audit trail: If something goes wrong, can we show we did due diligence?
• ROI: What's this cost vs the risk of bad AI outputs or delayed AI projects?

Requirements

• 1-3 years in B2B sales, ideally selling to enterprise data/AI/engineering teams
• Comfortable with ambiguity and building from zero - no one will hand you a script
• Technical enough to learn the product deeply and speak credibly to data teams (not engineer-level, but not afraid of technical concepts)
• Self-starter who can manage your own pipeline, create your own collateral, and figure out what works without constant direction
• Willing to do unglamorous work: list building, cold calling, admin, whatever it takes
• SF-based or willing to relocate (6-person team, lots of in-person collaboration likely)