Sales Representative (Generalist)

Overview

You're selling Harvey's AI platform to law firms and in-house legal teams. Your buyers are partners at AmLaw 200 firms and General Counsels at mid-to-large enterprises. Most of your conversations are about data security, accuracy concerns, and how AI fits into existing workflows. Deals take months because legal buyers move slowly and every stakeholder has veto power.

Role Snapshot

Company Context

Stage: Late-stage venture (1,195 employees suggests Series C/D+)

Size: 1,195 employees

Growth: Hiring aggressively across the org - GTM team under 4-month-old leadership means rapid expansion and process building

Market Position: Category creator in legal AI - no direct competitors have their traction with top-tier firms yet, but incumbents like LexisNexis and new AI startups are circling

GTM Reality

Pipeline Sources:

• 30-40% Inbound - word of mouth from existing law firm clients, industry conferences, legal tech publications. Quality is high but volume is limited (small TAM).
• 50-60% Outbound - targeted account-based selling into specific firms. You're identifying innovation partners at each firm, then navigating their internal structure.
• 10-20% Referrals - existing clients introducing you to peer firms or former colleagues who moved in-house.

SDR/AE Structure: Likely dedicated BDRs for initial outreach, but AEs own the full relationship given deal complexity and small TAM.

SE Support: Almost certainly have Solutions Engineers or Legal specialists for demos and technical validation - the product is too complex and buyers too skeptical to go solo.

Competitive Landscape

Main Competitors: LexisNexis and Thomson Reuters (legacy legal research), CoCounsel (Thomson Reuters' AI offering), Casetext, plus internal "build it ourselves" initiatives at large firms.

How They Differentiate: Built specifically for legal workflows (not generic ChatGPT wrappers), enterprise-grade security that passes BigLaw compliance reviews, trained on legal-specific data.

Common Objections:

• "We can't put client data in an AI system" (data security/confidentiality)
• "AI makes mistakes - we can't risk that in legal work" (accuracy concerns)
• "Our lawyers won't use it" (change management)
• "We're building our own solution" (especially at large firms with tech budgets)

Win Themes: Speed to competent draft, ability to scale expertise across junior lawyers, competitive advantage in pitch responses and due diligence, firms your competitors are already using it.

What You'll Actually Do

Time Breakdown

Prospecting/Account Research (20%) | Active Deals (50%) | Internal/Strategic (30%)

Key Activities

• Stakeholder mapping at target firms: You research law firm structures, identify innovation committees, figure out who actually has budget authority versus who just has opinions. Every firm is organized differently.
• Running educational demos: You walk skeptical partners through how the product works, address "what if it hallucinates" questions, explain the security architecture. Demos often need 3-4 sessions with different stakeholder groups.
• Coordinating security reviews: You manage the procurement process - InfoSec questionnaires, vendor risk assessments, data processing agreements. Legal buyers have the longest, most detailed security reviews in B2B.
• Nurturing stalled deals: You follow up with champions who went quiet, navigate around partners who retired or left the firm mid-deal, wait for budget cycles to refresh when deals get pushed.

The Honest Reality

What's Hard

• Lawyers are the most skeptical buyers in B2B: Every claim you make gets interrogated. They're trained to find flaws in arguments. One partner saying "I don't trust AI" can kill a deal regardless of what other stakeholders think.
• Sales cycles are brutally long: 6-12 months is normal. Deals stall constantly - budget freezes, leadership changes, competing priorities. You'll have deals in your pipeline for quarters that barely move.
• The TAM is small: There are only ~500 AmLaw firms and a few thousand corporate legal departments that fit the ICP. You can't just "find more accounts" when deals slip - you're stuck waiting.
• Product is evolving fast: AI capabilities change monthly. What you demo today might work differently next quarter, which creates implementation challenges and buyer uncertainty.

What Success Looks Like

• Landing 3-4 new firm deployments per year (given cycle length and deal size)
• Getting past proof-of-concept into firm-wide rollout (where the big $ lives)
• Building champions who move to other firms and bring Harvey with them
• Creating case studies/references that open doors at peer firms

Who You're Selling To

Primary Buyers:

• Partners (especially practice group leaders, innovation committee members)
• General Counsel and Legal Ops leaders at corporations
• CIOs/CTOs at law firms (for technical validation)

What They Care About:

• Confidentiality/Security: Can they put client data in this? What's the data retention policy? Where are servers located?
• Accuracy: What happens when the AI is wrong? Can they trust the output?
• Adoption: Will their lawyers actually use it, or will it sit unused like previous tech investments?
• ROI: Can they bill clients more efficiently? Reduce associate hours? Win more competitive bids?
• Competitive positioning: Are their peer/competitor firms using this? Will they fall behind?

Requirements

• Experience selling B2B SaaS to enterprises with complex buying committees (legal, compliance, procurement)
• Comfort with long sales cycles and deals that stall frequently - you need patience
• Ability to translate technical AI concepts for non-technical buyers without oversimplifying
• Track record navigating risk-averse organizations and building consensus among skeptical stakeholders
• Understanding of professional services business models (billable hours, client confidentiality) helpful but not required
• Willingness to work in a fast-scaling environment where GTM processes are still being built