Overview
You partner with AEs to demonstrate Harvey's AI capabilities to legal professionals and their IT/security teams. You customize demos for specific practice areas, explain how the AI works (without overpromising), and navigate technical evaluations. You're translating between sales' "this will transform your practice" and the reality of what the product can and can't do.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Pre-sales Solutions Engineer |
| Sales Motion | Balanced - supporting both inbound and outbound deals |
| Deal Complexity | Enterprise - technical evaluations, security reviews, integration questions |
| Sales Cycle | 4-9 months (you're involved from early demo through close) |
| Deal Size | Supporting deals $150K-500K+ ACV |
| Quota (est.) | Measured on deals closed with your support, not personal quota |
Company Context
Stage: Late-stage (1195 employees)
Size: 1195 employees
Growth: Scaling sales team, likely adding more SEs to support AE hiring
Market Position: Selling enterprise AI to conservative buyers means heavy technical scrutiny
GTM Reality
Pipeline Sources:
- You support AE-sourced opportunities across inbound, outbound, and referrals
- Not responsible for lead generation
SDR/AE Structure: You're paired with 3-5 AEs typically, supporting their deals from first demo through technical close
SE Support: You are the SE support
Competitive Landscape
Main Competitors: Traditional legal research tools (Westlaw, LexisNexis), competing legal AI platforms, customer's "build it internally" option
How They Differentiate: Purpose-built for legal vs generic AI, security model, specific integrations with legal workflows
Common Objections: "How do you prevent hallucinations?", "What happens to our client data?", "Can it handle [obscure legal scenario]?", "How does this integrate with our practice management system?"
Win Themes: Demonstrating accuracy on real legal queries, showing security architecture, proving ROI with time savings on specific tasks
What You'll Actually Do
Time Breakdown
Demo Prep (35%) | Live Demos (25%) | Technical Evaluations (20%) | Internal (20%)
Key Activities
- Demo preparation: For each new account, you spend 2-4 hours researching their practice areas, recent cases, and pain points. You customize demo scripts with relevant examples (litigation research for litigators, contract analysis for M&A teams). You test queries in advance so nothing breaks on the demo.
- Running demos: 60-90 minute video calls walking through Harvey's capabilities. You balance showing impressive AI results with being honest about limitations. Lawyers will ask edge case questions to test you. You demonstrate specific use cases: legal research, document review, drafting memos, etc.
- Answering technical questions: During and after demos, you field questions about the AI model, data handling, accuracy rates, and integrations. You need to explain how transformer models work without using jargon, and admit when something isn't possible yet.
- Supporting technical evaluations: Prospects often run POCs (Proof of Concepts) where they test Harvey on real work. You provide training, troubleshoot issues, and interpret results. POCs last 2-4 weeks and require daily check-ins.
- Security reviews: IT and security teams grill you on data residency, encryption, SOC 2 compliance, penetration testing. You coordinate with Harvey's security team to provide documentation and often join calls to answer questions live.
- Deal support: Late-stage deals may need you on calls to reassure stakeholders, address last-minute objections, or explain technical elements of the contract. You're on Slack with AEs constantly.
- Product feedback: You document feature requests and customer pain points for product team. You're the voice of "here's what we're losing deals over" and "here's what customers are confused by."
The Honest Reality
What's Hard
- Legal professionals are skeptical and detail-oriented. They'll ask about edge cases, hypotheticals, and corner scenarios. You need to be honest when the AI can't handle something, which sometimes hurts the deal.
- Demos need to be perfect. If the AI gives a bad result during a live demo, you lose credibility instantly. You spend hours testing queries in advance.
- You're managing expectations constantly. Sales may overpromise ("it can do anything"), and you're explaining what's realistic. This creates internal tension.
- Technical evaluations are high-stakes. Customers test Harvey on real work and compare to alternatives. If they find inaccuracies or limitations, the deal is in jeopardy.
- Security reviews are exhaustive. You'll field 50-100 questions about data handling, compliance, and infrastructure. Expect multiple calls with CISOs and IT teams.
- You're spread thin. If you support 4 AEs with 20 active opportunities each, you're juggling 80 accounts at various stages. You can't give every deal deep attention.
- Product gaps are painful. When a customer needs a feature you don't have, you're stuck explaining workarounds or promising future roadmap (which you can't commit to).
What Success Looks Like
- High win rate on deals you support (60%+ of opportunities close)
- Strong demo-to-next-stage conversion (75%+ of demos advance)
- POCs that convert to closed deals (70%+ success rate)
- Customers mention you positively in win/loss interviews
- Product team acts on your feature requests because your feedback is specific and frequent
Who You're Selling To
Primary Audiences:
- Partners and Associates (you demo actual legal use cases to them)
- Innovation Directors / COOs (they evaluate strategic fit)
- IT/Security teams (they vet your architecture and compliance)
- General Counsels and Deputy GCs (final decision makers who attend key demos)
What They Care About:
- Lawyers: Does this actually save time? Is it accurate? Will it make me look stupid if it gives wrong answers?
- Innovation/Ops: Can we roll this out across the firm? What's the change management effort?
- IT/Security: Is our data safe? Does this meet our compliance requirements? What's your uptime SLA?
- Executives: What's the ROI? Are peer firms using this? What's the risk if we don't adopt AI?
Requirements
- 3-5 years as a Solutions Engineer or Sales Engineer in B2B SaaS
- Technical background - comfortable explaining AI/ML concepts, APIs, data architecture (don't need to be a data scientist, but need technical credibility)
- Strong presentation skills - you're on video demos 10-15 hours/week
- Legal or professional services experience helpful (you'll learn legal workflows faster)
- Ability to handle objections and skepticism without getting defensive
- Customer-focused - you balance helping sales close with being honest about product limitations
- Willingness to travel occasionally for onsite demos at major accounts