Demo Call Prep Workflow

Every demo call ends with a customized proposal already in the prospect's inbox.

Updated 2026-04-18
Tycoon solution

AI Sales Rep + Tycoon Agent prep every demo call. Prospect research (company stage, team, tech stack, recent news), tailored demo agenda per use case, objection playbook pre-loaded, live notes during the call, same-day follow-up with custom proposal. You walk in knowing the prospect better than they expect and leave with the deal already moving.

How it runs

  1. Prospect research brief

    24 hours before the demo, AI Sales Rep pulls: company info (size, stage, funding, industry), attendees' LinkedIn profiles (role, tenure, past companies), company's recent news (press, product ships, hires), their tech stack (BuiltWith), and your historical interactions (past emails, site visits). Outputs a 1-page brief.

  2. Tailored demo agenda

    Based on the use case captured during discovery, AI Sales Rep drafts the specific demo flow: which features to show, which to skip, use-case-specific narratives, expected objections, relevant customer logos to name-drop. No generic 'here's everything our product does' demo.

  3. Objection playbook

    For each prospect, AI Sales Rep pre-loads likely objections from their stage, industry, and stated concerns: 'we'd need SOC 2' (here's the status), 'integration with Salesforce?' (here's the doc), 'pricing seems high' (here's the ROI calculator). You don't get caught off-guard.

  4. Live note-taking

    During the call (Zoom/Google Meet), AI Sales Rep transcribes (via Fireflies/Granola/Otter) and extracts: stated use cases, objections raised, decision criteria, competing tools mentioned, next-step commitments. Notes structured in real-time, not reconstructed from memory.

  5. Same-day follow-up draft

    Within 2 hours of call end, AI Sales Rep drafts follow-up: personal email recapping key points, proposal document with pricing tailored to their scale, links to referenced resources (ROI calc, security overview, integration docs), and a calendar link for next step.

  6. Proposal with pricing and ROI

    Proposal generated with their specific variables: seat count, feature selection, expected usage, annual discount, implementation timeline. ROI calculator pre-populated with their industry benchmarks. You review + approve in 10 minutes instead of building from scratch.

  7. Pipeline tracking and handoff

    Opportunity updated in HubSpot/Attio with full context. Stage advanced based on demo outcome (qualified, technical evaluation, procurement). Next-step tasks created with owners + deadlines. If they go dark, follow-up sequence kicks in automatically at day 5, 14, 30.

Who runs it

  • hire/ai-sales-rep
  • hire/ai-cmo

What you get

  • Every demo pre-researched, zero cold walk-ins
  • Demo conversion rate lifts 20-40% from tailored agendas
  • Same-day follow-up with custom proposal (not 3 days later)
  • Objections handled on the call, not in a follow-up email round
  • Pipeline stays in HubSpot current, not 'I'll update it Friday'
  • Founder prep time per demo drops from 30 min to 5 min of review
  • Win rate improves measurably from better prep + faster follow-up
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Prospect research feels invasive. Isn't there a creepy line?

The line is: public info vs private info. Public: company size, funding, press releases, LinkedIn profiles, tech stack visible via BuiltWith, recent product launches. Private: personal details, off-the-record discussions, non-public financials. AI Sales Rep pulls only public info — the same info a professional human researcher would find on Google + LinkedIn in 30 minutes. Most prospects expect this level of prep from a serious vendor; they notice when it's absent ('they didn't even know we'd just raised'). Creepy is when you reference something that had to come from a private source.

How is this different from Clay or Clearbit for prospect enrichment?

Clay and Clearbit are data providers — they give you fields about a company (size, revenue, tech stack). They don't synthesize into a brief, tailor the demo agenda, pre-load objections, or draft follow-up. Tycoon uses Clay/Clearbit as inputs and adds the orchestration layer that turns data into a workable demo brief. Think of it as: Clay = ingredients; Tycoon = the chef that prepares a meal.

Our sales cycles are complex — multi-stakeholder, 6-month enterprise cycles. Is one demo call prep enough?

No, and enterprise workflows have multiple demo stages. AI Sales Rep maps the account stakeholders (Champion, Economic Buyer, Technical Evaluator, Legal, IT) and runs demo prep for each stakeholder type: technical deep-dive for the evaluator, business-outcome focus for the buyer, integration-focused for IT. Between demos, it tracks the multi-thread: who's aligned, who's hesitant, what objections are blocking, what's the next gate. Enterprise deals close faster not because of better demos but because of better cross-stakeholder coordination.

What if the prospect goes off-script during the demo? Pre-planned agendas fall apart.

The agenda is a default, not a cage. Pre-prep makes off-script easier, not harder — you know the product well enough to pivot confidently. During the call, if the prospect asks about a feature you didn't plan to demo, AI Sales Rep pulls the right docs into your screen (via a bookmark bar or pre-loaded tabs) so you can show it without fumbling. Post-call notes capture the pivot so the follow-up reflects what actually mattered to the prospect, not what your pre-planned agenda said mattered.

Can this replace an SDR/BDR that qualifies inbound demo requests?

Partially. AI Sales Rep can handle qualification questions via chatbot/form: 'what's your team size?', 'what tools are you currently using?', 'what's the problem you're solving?'. For high-signal inbound (mid-market+ with clear use case), it books the demo directly. For lower-signal (freelancer curious about your product), it routes to self-serve onboarding + KB instead of a human demo. The SDR's job changes from 'qualify and schedule' to 'handle edge cases the AI can't'. Most teams find this reduces SDR headcount need by 50-70% or reallocates SDRs to higher-value outbound.

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