Case Study
Account Expansion Analyst
AI employee for post-sale growth in B2B SaaS
Why this problem
Account Managers in B2B SaaS spend most of their time preparing for expansion and renewals, not actually selling. Critical signals live across CRMs, usage dashboards, support tools, and internal docs, making it hard to identify which accounts are ready to expand and why.
For a mid-market SaaS company, this results in millions of dollars in missed expansion revenue every year, even when customers are actively showing buying intent.
This problem is high-frequency, high-stakes, and perfectly suited for an AI agent that can reason across systems and act autonomously.
The gap
Today there is no system that:
- –Unifies expansion signals across data sources
- –Explains why an account is ready to expand
- –Generates tailored pitches and assets instantly
- –Continuously monitors accounts without manual work
As a result:
- –60-70% of expansion-ready accounts go unidentified
- –AEs spend 5-8 hours per account on research and prep
- –Upsell pitches are generic and poorly timed
The solution
Account Expansion Analyst (AEA) is an AI employee that acts as a dedicated expansion analyst and strategist for every Account Manager.
It continuously analyzes accounts, surfaces expansion opportunities with clear reasoning, and generates ready-to-use sales assets in seconds.
What the agent does
AEA is responsible for:
- –Identifying expansion-ready accounts and explaining the reasoning
- –Recommending the right products, tiers, or seat expansions
- –Generating personalized pitch decks, emails, and talking points
- –Aggregating CRM, usage, support, and activity data into one view
- –Monitoring accounts continuously and updating recommendations
End-to-end workflow
The Account Manager opens their portfolio
AEA instantly ranks accounts by expansion potential and churn risk, saving hours of manual analysis.
The Account Manager explores an account
AEA presents a unified view of usage trends, support tickets, adoption patterns, and historical context.
AEA identifies opportunities
It highlights insights like feature adoption spikes or plan limits reached, along with confidence scores and data sources.
The Account Manager selects an expansion path
Options include adding seats, upgrading plans, or cross-selling products.
AEA generates assets
Personalized pitch deck, outreach email, talking points, and a CRM opportunity with estimated ARR are created instantly.
Outreach and follow-up
AEA continues monitoring signals and updates recommendations automatically.
This workflow saves approximately 10-15 hours per Account Manager per week.
Agent architecture
This is an agentic AI system that:
- →Understands the user request
- →Plans a multi-step workflow
- →Calls tools to fetch CRM, usage, ticket, and activity data
- →Synthesizes signals into expansion insights
- →Generates assets and streams outputs to the UI in real-time
Prototype implementation
The prototype uses a real database and live reasoning.
Tech stack:
Frontend
Next.js for the frontend
Database
Supabase as the database with five tables: accounts, usage events, support tickets, user activity, opportunities
AI Layer
Claude Sonnet for reasoning, tool calling, and streaming responses
All insights shown in the UI are generated from synthetic but structured data, designed to mimic real enterprise workflows.
Validation approach
Feasibility
Seeded the database with sample accounts and tested end-to-end prompts like "Show top expansion opportunities" and "Analyze Acme Corp." Goal was to validate reasoning, tool calling, and output quality.
Desirability
Shared generated insights and pitch snippets with Account Managers and asked whether this would meaningfully reduce their workload. Target signal was usefulness rated 8/10 or higher.
Usability
Compared time taken, confidence, and quality of decisions before and after using the prototype.
Measuring success
Success would be measured across:
Risks and mitigation
Hallucinations
Mitigated through evidence-based generation and human approval loops.
Data quality issues
Handled via schema validation, monitoring, and fallback logic.
Workflow resistance
Addressed through transparent explanations and visible data sources.
Over-reliance on AI
Reduced using confidence scores and uncertainty indicators.