# PRD: AI Features for [Product Name] Invoicing
**Status:** Draft v0.1 | **Owner:** [PM] | **Last updated:** [Date]
**Stakeholders:** Engineering, Design, Data/ML, Legal/Security, Support, Finance
> **Assumptions:** I don't know your product details, so I assumed a B2B invoicing app for small businesses and freelancers, where users create and send invoices, track payments, and chase late payers. Items in [brackets] need your input. If you're AP-focused (processing incoming bills), the priorities in Section 6 shift toward document extraction.
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## 1. Overview
Add AI capabilities to reduce the time users spend creating invoices and collecting payment, and to improve cash flow outcomes. The initial release focuses on **drafting** (invoices and reminders) and **prediction** (late-payment risk), with a human always approving before anything is sent or changed.
## 2. Problem Statement
- Creating invoices involves repetitive data entry: client details, line items, taxes, terms.
- Chasing late payments is uncomfortable and inconsistent. Users delay or skip follow-ups, which hurts cash flow.
- Users lack visibility into which invoices are likely to go unpaid and why.
- Competitors are shipping AI features, and it is becoming a table-stakes expectation.
Show the rest (188 more lines)
**Evidence to gather:** [support ticket themes, survey data, time-on-task analytics, churn interview quotes]
## 3. Goals and Non-Goals
### Goals
1. Cut median time to create and send an invoice by **40%** for AI-assisted users.
2. Reduce average days-to-payment by **10%** for users who adopt AI reminders.
3. Reach **30% monthly adoption** of at least one AI feature among active users within 6 months of GA.
4. Maintain user trust: **<2%** of AI outputs reported as harmful or incorrect after launch.
### Non-Goals (v1)
- Fully autonomous actions (sending invoices or payments with no user review)
- Tax or legal advice
- Automated accounting or bookkeeping reconciliation
- Training proprietary foundation models (we will use third-party or hosted models)
- Multi-language support beyond [English] at launch
## 4. Target Users
| Persona | Needs | AI value |
|---|---|---|
| **Freelancer / solo operator** | Speed, minimal admin | Create invoices from a sentence; automated polite follow-ups |
| **Small business owner** | Cash flow visibility | Late-payment risk flags, collections help |
| **Bookkeeper / finance admin** | Accuracy, auditability | Line-item suggestions, anomaly detection, audit trail |
## 5. User Stories
- As a freelancer, I type "Invoice Acme for 12 hours of design at $85/hr, net 15" and get a ready-to-review draft.
- As an owner, I get a suggested reminder email in my tone when an invoice goes overdue, and I can edit and send it in one click.
- As an owner, I see which open invoices are at high risk of paying late, and why.
- As a bookkeeper, I'm warned when a new invoice looks like a duplicate or has an unusual amount for that client.
- As any user, I can see what the AI changed, undo it, and turn AI features off.
## 6. Features and Prioritization
### P0: MVP
**F1. AI Invoice Drafting (natural language → draft invoice)**
- Input: free text, pasted email or thread, or voice (voice is P1).
- Output: a populated invoice (client, line items, quantities, rates, taxes, due date, notes).
- Matches existing clients and saved items; flags uncertain fields, such as ambiguous client or missing rate, for confirmation.
- Never auto-sends. The user must review and confirm.
**F2. AI Payment Reminder Drafting**
- Generates reminder emails by stage (upcoming, due today, 7/14/30 days overdue).
- Tone selector (friendly, neutral, firm) plus a learned user style [P1].
- Pulls in invoice details and payment link. The user approves each message, or opts into scheduled sends with pre-approved templates.
### P1: Fast Follow
**F3. Late-Payment Risk Scoring**
- Per-invoice risk (low/medium/high) based on client payment history, amount, terms, and seasonality.
- Shows top 2–3 human-readable contributing factors.
- Suggests actions, such as an earlier reminder or a deposit request.
**F4. Document Extraction (PDF/image → invoice or expense)**
- Upload a quote, PO, or receipt and pre-fill fields, with field-level confidence indicators.
**F5. Smart Line-Item and Category Suggestions**
- Autocomplete descriptions, rates, and tax codes from history.
### P2: Later
**F6. Financial Q&A ("Who owes me the most? What was revenue last quarter?")**, grounded in the user's data via structured queries rather than free-form generation.
**F7. Anomaly and Duplicate Detection**
**F8. Cash Flow Forecasting**
## 7. Functional Requirements
| ID | Requirement | Priority |
|---|---|---|
| FR-1 | The user can invoke AI drafting from the "New Invoice" screen and a global shortcut | P0 |
| FR-2 | All AI-populated fields are visually marked and editable | P0 |
| FR-3 | Low-confidence fields are highlighted and require confirmation before send | P0 |
| FR-4 | No AI-generated communication is sent without explicit user approval (unless the user opts into pre-approved automation) | P0 |
| FR-5 | The user can regenerate, edit, or discard any AI output | P0 |
| FR-6 | AI actions are logged (input, output, model version, user decision) and viewable in the audit trail | P0 |
| FR-7 | Account-level and user-level toggle to disable AI features | P0 |
| FR-8 | Thumbs up/down plus a reason on every AI output | P0 |
| FR-9 | Risk scores display explanations and are never the sole basis for automated action | P1 |
| FR-10 | Admin controls for roles allowed to use AI and approve sends | P1 |
## 8. AI-Specific Requirements
**Accuracy and quality**
- Numeric fields (amounts, quantities, taxes, dates) must be computed or validated deterministically in code. The LLM extracts intent, and the app does the math.
- Target for F1: **≥95%** field-level accuracy on the eval set for client, amount, and due date. **≥90%** of drafts are sent with ≤1 edit. [Calibrate after the baseline study.]
- Build an offline eval set of [300+] real, anonymized examples before launch, and run regression evals on every prompt or model change.
**Safety and guardrails**
- Structured output (JSON schema validation) for all invoice data.
- Prompt injection defenses for pasted emails and uploaded documents: treat content as data, not instructions, and restrict tool access.
- Reminder content filters: no threats, no false legal claims, no inappropriate escalation language.
- Refuse tax or legal advice, and route users to a disclaimer.
**Privacy and security**
- Customer financial data is not used to train third-party models (zero-retention or no-training agreements with providers).
- PII minimization: send only the fields needed for each call.
- Compliance review: [GDPR / CCPA / SOC 2 / PCI scope]. Data residency: [requirements].
- Opt-in for any use of customer data to improve our own models, with clear disclosure.
**Performance**
- F1 draft latency: p50 < 3s, p95 < 8s, with streaming or progress UI.
- Graceful degradation: if the AI service is down, manual flows are unaffected.
- Cost guardrail: target **<$[0.0X]** per AI-assisted invoice, with per-account rate limits.
**Transparency**
- Clear AI labeling, a "why this suggestion" explanation where relevant, and no hidden automation.
## 9. UX Principles
1. **Draft, don't decide.** AI proposes and the user confirms.
2. **Show your work.** Highlight what was inferred versus what was provided.
3. **Make correction easy.** Inline edits, one-click undo, and "try again."
4. **Earn trust gradually.** Start with suggestions, then offer opt-in automation after users have seen consistent quality.
5. **Don't block the manual path.** AI is an accelerator, not a gate.
Design deliverables: entry-point designs, empty and error states, low-confidence states, onboarding tooltip, and settings page.
## 10. Success Metrics
| Category | Metric | Target |
|---|---|---|
| Adoption | % of active users using ≥1 AI feature monthly | 30% at 6 months |
| Efficiency | Median time to create and send an invoice (AI vs. non-AI cohort) | −40% |
| Outcome | Average days-to-payment (reminder adopters vs. control) | −10% |
| Quality | Draft acceptance rate (sent with ≤1 edit) | ≥90% |
| Quality | Thumbs-down rate | <8% |
| Trust | AI-related support tickets per 1k users | Below baseline threshold [X] |
| Retention | 90-day retention (AI users vs. matched non-users) | +[X] pts |
| Business | Conversion to the paid tier / AI add-on attach rate | [X]% |
| Cost | AI cost per active user | < $[X]/month |
**Guardrail metrics:** error-correction rate on sent invoices, customer disputes tied to AI content, and unsubscribe/complaint rates on reminders.
## 11. Rollout Plan
1. **Phase 0, Discovery (2–3 wks):** user interviews, data audit, model/vendor evaluation, eval set creation, legal/security review.
2. **Phase 1, Internal alpha (3–4 wks):** employees and friendly accounts, F1 only.
3. **Phase 2, Closed beta (4–6 wks):** ~[5%] of users or opt-in waitlist, F1 + F2. Weekly eval and feedback review.
4. **Phase 3, GA (staged):** 25% → 50% → 100% with feature flags and kill switches. Add F3–F5 in subsequent releases.
**Launch criteria:** eval thresholds met, no P0/P1 security issues, support trained, and kill switch tested.
**Pricing:** [included in the existing plan / premium tier / usage-based add-on]. Decision needed before beta.
## 12. Risks and Mitigations
| Risk | Impact | Mitigation |
|---|---|---|
| Hallucinated amounts or wrong client | High (financial errors) | Deterministic validation, mandatory review, confidence flags |
| Inappropriate or aggressive reminder tone | Medium (client relationships) | Tone constraints, content filters, approval step |
| Data privacy concerns | High | No-training agreements, minimization, transparent settings |
| Prompt injection via pasted emails or documents | Medium–High | Input isolation, schema-constrained outputs, limited permissions |
| Low adoption due to distrust | Medium | Gradual autonomy, visible accuracy, easy undo |
| Model cost overruns | Medium | Caching, smaller models for simple tasks, rate limits, cost dashboards |
| Vendor dependency or outages | Medium | Abstraction layer, fallback model, graceful degradation |
| Regulatory exposure (e-invoicing, tax rules) | Medium | Keep AI out of compliance-critical logic, legal review |
## 13. Dependencies
- LLM provider(s) and contracts (zero-retention terms)
- Clean historical invoice and payment data for F3
- Email-sending infrastructure and deliverability for F2
- Analytics instrumentation for the metrics above
- Legal, security, and privacy review
## 14. Open Questions
1. Are we primarily AR (outgoing invoices), AP (incoming bills), or both?
2. What is our current data volume and quality for payment-history modeling?
3. Build vs. buy for document extraction (F4)?
4. Is the pricing model a bundled feature or a paid add-on?
5. Which regions and languages are in scope for v1?
6. What level of automation do users want, and which risks do they accept?
7. Do we need SOC 2 or other certification updates before launch?
## 15. Appendix (To Be Added)
- Competitive analysis
- User research summary
- Prompt and eval design doc
- Technical architecture diagram
- Detailed UX flows
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If you tell me whether you're AR or AP, your target customer, and your tech stack or data situation, I can tighten the scope, rewrite the priorities, and add specifics like detailed acceptance criteria or an eval plan.