Product Requirements Document

SalesStack AI Product Requirements

Comprehensive product specification for AI-powered sales intelligence platform targeting enterprise sales teams

Status: Approved
Last Updated: November 2025

1. Executive Summary

Product Vision

Product: SalesStack AI is an AI-powered sales intelligence platform that automates deal prediction and pipeline management for enterprise sales teams targeting $50M+ ARR organizations.

Target User: Enterprise sales operations managers, VPs of Sales, and sales representatives at companies with complex B2B sales cycles who need data-driven pipeline insights and deal forecasting.

Key Differentiator: Seamless CRM integration with real-time AI analysis of every email, call, and meeting to surface buying signals and predict deal outcomes with higher accuracy than traditional forecasting methods.

Success Definition: 68% improvement in sales productivity, 90%+ deal prediction accuracy, and 30% reduction in time spent on CRM updates within first 6 months of deployment.

Strategic Alignment

Business Objectives
  • Capture $3.8B sales intelligence market
  • Achieve $10M-$100M ARR within 3 years
  • Establish category leadership in AI-powered forecasting
  • Build recurring revenue model with 95%+ retention
User Problems Solved
  • Manual CRM data entry consuming 4-6 hours daily
  • Inaccurate pipeline forecasts (avg 40% variance)
  • Missed buying signals in communications
  • Poor visibility into deal health and risk factors
Market Opportunity
  • $3.8B projected market by 2025
  • 9/10 pain score with acute, frequent issues
  • 80% of enterprise sales teams adopting AI
  • First-mover advantage in seamless integration
Competitive Advantage
  • Plug-and-play CRM integration (no custom dev)
  • Real-time AI coaching and insights
  • Enterprise-grade security and compliance
  • 1,000 reseller network for rapid distribution

Resource Requirements

3 Months
MVP Development Timeline
Dec 2025 - Mar 2026
$500K
Initial Budget (6 months)
Seed funding requirement
8-12
Team Members
Engineering, Sales, Support

2. Problem Statement & Opportunity

Problem Definition

Enterprise sales teams are drowning in CRM busy work while missing critical deals. Sales representatives spend 4-6 hours daily updating Salesforce instead of selling, and pipeline forecasts remain built on gut feelings with an average variance of 40% from actual results. Communication tools capture thousands of buying signals in emails, calls, and meetings, but humans cannot process this data at scale, leading to missed opportunities and poor deal prioritization.

Quantified Impact:
  • 30-40% of sales time wasted on administrative tasks instead of selling activities
  • $1.2 trillion in potential global productivity gains from AI-powered sales tools
  • 40% forecast variance leads to poor resource allocation and missed revenue targets
  • 60% of deals stall due to lack of engagement insights and buying signal detection
Evidence Supporting Problem:
  • 393K members in r/sales actively discussing tool ineffectiveness and integration issues
  • Strong engagement across LinkedIn, YouTube, and sales communities seeking better solutions
  • 80% of sales teams already integrating AI, demonstrating validated market need
  • Compliance challenges and integration complexity rated 9/10 pain score by target users

Opportunity Analysis

Market Size & Growth
  • $3.8B projected market size by 2025
  • 19% annual growth in LinkedIn sales AI discussions
  • 583K average YouTube views for sales tool content
  • Growing enterprise adoption with mature API ecosystems
Revenue Opportunity
  • B2B SaaS: $200-$3,000/month per user
  • Enterprise contracts: $25K-$100K annually
  • $10M-$100M ARR potential within 3 years
  • $4,800 LTV with 9.6-16:1 LTV:CAC ratio

Success Criteria

Primary Success Metrics
  • 90%+ deal prediction accuracy
  • 68% improvement in sales productivity
  • 30% reduction in CRM update time
  • 95%+ customer retention rate
Business Outcomes
  • 500 paying users within 6 months
  • 2 major CRM platform integrations
  • $1M ARR by end of year 1
  • 10% market penetration in target segment

3. User Requirements & Stories

Primary User Personas

Sarah Chen
VP of Sales
Oversees 50+ rep team, frustrated by inaccurate forecasts and lack of pipeline visibility. Needs real-time insights to make strategic decisions.
Goal: Accurate forecasting and team performance visibility
Marcus Rodriguez
Sales Ops Manager
Manages CRM data quality and reporting. Tired of explaining forecast variances and chasing reps for updates.
Goal: Automated data capture and reliable reporting
Jennifer Kim
Enterprise AE
Handles complex 6-12 month sales cycles. Wants to focus on selling, not admin work and guessing next steps.
Goal: More selling time and better deal guidance

Core User Stories

Epic: Automated Deal Intelligence
As a sales rep, I want the system to automatically analyze my emails and meetings so that I can identify buying signals without manual review.
Acceptance Criteria:
  • System analyzes 100% of connected email and calendar data
  • Buying signals surfaced within 5 minutes of communication
  • 90%+ accuracy in signal classification (budget, authority, need, timing)
  • Dashboard displays actionable insights with recommended next steps
  • Mobile notifications for high-priority signals
Epic: CRM Integration & Automation
As a sales ops manager, I want seamless Salesforce integration so that deal data syncs automatically without manual entry.
Acceptance Criteria:
  • One-click OAuth connection to Salesforce
  • Bi-directional sync within 2 minutes of changes
  • Automatic field mapping with smart suggestions
  • 30%+ reduction in manual CRM update time
  • Conflict resolution UI for data discrepancies
Epic: Predictive Deal Scoring
As a VP of Sales, I want AI-powered deal health scores so that I can accurately forecast revenue and allocate resources.
Acceptance Criteria:
  • Deal health score (0-100) updates in real-time based on activity
  • 90%+ accuracy in predicting close probability and timing
  • Risk factors clearly identified with mitigation suggestions
  • Historical data shows improved forecast accuracy vs baseline
  • Export functionality for board/exec reporting

4. Functional Requirements

Core Features (Must Have - MVP)

Salesforce CRM Integration

OAuth-based connection with bi-directional sync of contacts, accounts, opportunities, and activities. Automatic field mapping and conflict resolution. Real-time sync within 2 minutes.

Priority: P0 (Launch Blocker) | Effort: 4 weeks
Email & Calendar Analysis

Integration with Gmail and Outlook to analyze communication patterns, extract buying signals (BANT indicators), sentiment analysis, and engagement tracking. NLP-powered signal detection with 90%+ accuracy.

Priority: P0 (Launch Blocker) | Effort: 6 weeks
Deal Health Dashboard

Real-time visualization of pipeline health with AI-generated deal scores (0-100), risk indicators, recommended next actions, and forecast accuracy tracking. Mobile-responsive with export capabilities.

Priority: P0 (Launch Blocker) | Effort: 3 weeks
Predictive Forecasting Engine

Machine learning models trained on historical deal data to predict close probability, expected close date, and deal value. Continuous learning from actual outcomes to improve accuracy over time.

Priority: P0 (Launch Blocker) | Effort: 5 weeks
Enterprise Security & Compliance

SOC 2 Type II compliant infrastructure, end-to-end encryption, role-based access control (RBAC), audit logging, GDPR compliance features, and SSO integration (SAML/OAuth).

Priority: P0 (Launch Blocker) | Effort: 4 weeks

Secondary Features (Post-MVP)

HubSpot & Microsoft Dynamics Integration
Q2 2026 | Effort: 3 weeks each
Voice Call Analysis (Gong/Chorus)
Q2 2026 | Effort: 4 weeks
Competitive Intelligence Integration
Q3 2026 | Effort: 3 weeks
Advanced Team Collaboration Tools
Q3 2026 | Effort: 4 weeks

5. Technical Requirements

Architecture Specifications

System Architecture
  • Microservices architecture on AWS/Azure
  • React.js frontend with Next.js framework
  • Node.js/Python backend services
  • PostgreSQL for relational data
  • Redis for caching and real-time features
  • Kubernetes for container orchestration
Integration Points
  • Salesforce REST/SOAP APIs
  • Gmail/Outlook Graph APIs
  • GPT-4 for NLP and signal detection
  • Auth0/Okta for SSO
  • Stripe for payment processing
  • Segment for analytics

Performance Specifications

<2s
Page Load Time
99.9%
Uptime SLA
100K+
Concurrent Users
<5min
CRM Sync Latency

Security & Compliance

Data Protection
  • AES-256 encryption at rest
  • TLS 1.3 for data in transit
  • Regular penetration testing
  • Automated vulnerability scanning
Compliance
  • SOC 2 Type II certification
  • GDPR compliance
  • CCPA compliance
  • ISO 27001 roadmap
Access Control
  • Role-based access control (RBAC)
  • Multi-factor authentication (MFA)
  • SSO integration (SAML/OAuth)
  • Comprehensive audit logging

6. Success Metrics & Analytics

Key Performance Indicators

Product Metrics
Deal Prediction Accuracy
90%+
Target vs actual close outcomes
Time Saved on CRM Updates
30%+
Reduction in manual data entry time
Sales Productivity Improvement
68%+
Increase in selling time vs admin
Business Metrics
Customer Retention Rate
95%+
Annual retention target
Net Revenue Retention (NRR)
120%+
Including expansion revenue
LTV:CAC Ratio
9.6:1
Blended CAC at scale

Analytics Implementation

Event Tracking Requirements
  • User sign-up and onboarding completion
  • CRM connection success/failure rates
  • Deal health score calculations
  • Dashboard views and interaction patterns
  • Feature adoption and usage frequency
  • Email/calendar analysis triggers
  • Prediction accuracy vs actual outcomes
  • User satisfaction (NPS) surveys
  • Support ticket creation and resolution
  • Churn indicators and warning signals

7. Implementation Plan

3-Month MVP Development Timeline

Phase 1: Foundation (Weeks 1-4)
Dec 2025
  • Infrastructure setup (AWS/Azure, CI/CD, monitoring)
  • Core authentication and user management
  • Database schema design and implementation
  • Salesforce OAuth integration foundation
  • Basic frontend scaffolding and design system
Phase 2: Core Features (Weeks 5-9)
Jan 2026
  • Complete Salesforce bi-directional sync
  • Email/calendar integration (Gmail & Outlook)
  • NLP engine for buying signal detection
  • Deal health scoring algorithm v1
  • Dashboard UI with real-time updates
Phase 3: Polish & Launch (Weeks 10-12)
Feb-Mar 2026
  • Predictive forecasting model training
  • Security audit and SOC 2 preparation
  • Beta testing with 5-10 design partners
  • Performance optimization and bug fixes
  • Documentation, onboarding, and support setup
  • MVP Launch on March 1, 2026

Resource Allocation

Engineering
4 Full-stack developers, 1 ML engineer, 1 DevOps engineer
Product & Design
1 Product Manager, 1 UX/UI Designer
Go-to-Market
2 Sales, 1 Marketing, 1 Customer Success
Budget
$500K for first 6 months (includes runway)

8. Risk Assessment & Mitigation

Technical Risks

CRM Integration Complexity
Medium Risk

Salesforce API rate limits and data mapping complexity may cause delays

Mitigation: Partner with Salesforce ISV program, implement robust caching and queue systems, allocate 4 weeks with buffer
AI Model Accuracy
Medium Risk

Insufficient training data may result in lower-than-target prediction accuracy

Mitigation: Start with GPT-4 for NLP (proven accuracy), collect labeled data from beta partners, iterate on custom models post-launch

Business Risks

Market Competition
Low-Medium Risk

Established players (Gong, Clari) may launch competing features

Mitigation: Focus on seamless integration advantage, leverage 1,000 reseller network for rapid distribution, emphasize superior UX
Customer Adoption
Low Risk

Sales teams may resist adopting new tools or changing workflows

Mitigation: 9/10 validated pain score, design for minimal workflow disruption, emphasize time savings, strong onboarding and change management

Complete Product Specification

This comprehensive PRD demonstrates our systematic approach to product development with clear requirements, success metrics, and execution plans that minimize risk and maximize investor confidence.

Back to OverviewView Technology Stack