Company Context

LedgerLine is a Series B fintech SaaS company (raised $95M) that sells invoicing + payments software to small businesses and freelancers in the US, UK, and Canada. LedgerLine has 2.4M monthly active businesses, processes $18B in annual invoice volume, and monetizes via a freemium plan plus a paid tier ($20–$60/month) and payment processing fees (2.9% + $0.30).

LedgerLine competes with QuickBooks, FreshBooks, and Stripe Invoicing. The company’s differentiation is speed: “get paid faster” through simple invoicing, automated reminders, and integrated card/ACH payments.

The CEO wants LedgerLine to be perceived as an “AI-first back office” product, but leadership is wary of shipping AI features that create trust issues or compliance risk.

User / Market Scenario

LedgerLine’s core users are time-constrained operators who send invoices frequently but inconsistently. Recent research (N=38 interviews + 1,200 survey responses) surfaced three primary personas:

Competitive notes:

• QuickBooks is strong on accounting integrations and reporting, but users complain it’s “heavy” for simple invoicing.
• FreshBooks is loved for time tracking + invoicing, but has weaker payments UX.
• Stripe Invoicing is strong for developer-led businesses, but less friendly for non-technical users.

Problem / Opportunity

LedgerLine’s North Star is “weekly paid invoices per active business.” However, the invoicing funnel shows a clear bottleneck:

• Invoice draft started → invoice sent conversion is 61% (meaning 39% of started drafts never get sent).
• Median time-to-send from draft start is 11.5 minutes on web and 18.2 minutes on mobile.
• Support tickets related to invoice mistakes (wrong line items, incorrect tax, wrong client) have increased 22% QoQ.

A product discovery team proposes an AI Invoicing Assistant that can:

1. generate invoice line items from a short prompt (e.g., “Logo design, 3 revisions, due net 15”),
2. suggest taxes/terms based on prior behavior, and
3. detect likely mistakes before sending.

Leadership is not convinced users will trust it, and engineering warns that a fully integrated AI experience could take 4–6 months.

Your Task (what you will be assessed on)

Design a rapid experimentation and prototyping plan to validate whether an AI Invoicing Assistant is worth building.

1. Clarify the user problem and success definition

   • What job(s) are users hiring LedgerLine to do in this invoicing moment?
   • What would “better” look like for each persona?

2. Propose 3–5 experiments/prototypes that can be run in 6 weeks

   • Include at least one low-fidelity prototype (e.g., clickable Figma, concierge, Wizard-of-Oz) and one in-product experiment.
   • For each experiment: hypothesis, target segment, method, what you’ll measure, and the decision rule.

3. Prioritize the experiments

   • Use a prioritization approach (e.g., RICE) and explain trade-offs.

4. Define key risks and mitigations

   • Trust/safety, compliance, hallucinations, and “automation surprises.”
   • How will you ensure experiments don’t damage user trust or increase invoice errors?

5. Recommend an MVP direction

   • Based on the likely outcomes, what MVP would you build first, and what would you explicitly not build yet?

Constraints

• Timeline: 6 weeks to reach a go/no-go decision; 10 weeks total to ship an MVP if approved.
• Team: 1 PM (you), 1 designer, 4 engineers (2 web, 1 mobile, 1 backend), shared data analyst (25% time).
• Compliance: Must meet SOC 2 controls; cannot store raw invoice text with PII in third-party tools without legal approval.
• Brand risk: Any experiment must keep invoice error rate from rising more than +0.3 percentage points.
• Technical: No existing ML platform; you can call an LLM via an approved vendor, but latency must stay under 800ms p95 for any user-facing generation.

---

Interviewer Notes

Assume you have access to event instrumentation for the invoice funnel, the ability to run A/B tests on web, and limited ability to test on mobile (feature flags exist but release cadence is slower). You can recruit users for moderated sessions within 5 business days via an existing research panel.