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Chant
intent driven development

The Business Context

Acme SaaS Corp

Acme SaaS Corp runs a B2B project management platform. Key numbers:

MetricValue
Customers5,000
MRR$2.5M
Monthly churn8% (400 customers/month)
Revenue at risk$200K/month
Customer segmentsStartup, Mid-Market, Enterprise

The 8% churn rate is unsustainable. Each lost customer costs roughly $500/month in MRR, and acquiring a replacement costs 5x more than retention.

Q1 OKR

Objective: Improve customer retention

Key Result: Reduce monthly churn from 8% to 5%

Target delta:  -3 percentage points
Revenue saved: ~$75K/month at target
Timeline:      End of Q1 2026 (4 weeks)

KPIs Being Tracked

Sarah (VP Product) defines the key performance indicators that the Q1 OKR targets:

MetricBaselineTargetHow Measured
Monthly churn rate8%5%Billing system exports
30-day activation rate62%75%Product analytics
Support ticket volume340/week<250/weekZendesk
NPS score3240+Quarterly survey

Churn Breakdown (Current State)

Mike (Data Analyst) pulls initial numbers by segment:

SegmentCustomersChurn RateLost/Month
Startup (<50 seats)3,20011%352
Mid-Market (50-500)1,4004%56
Enterprise (500+)4001%4

The problem is concentrated in the Startup segment. Mid-Market and Enterprise are healthy.

Team Structure

The project follows chant’s orchestrator pattern:

  • Sarah creates specs, reviews findings, approves work
  • Mike gathers external data, creates context digests for chant
  • Chant agents analyze data (research specs) and implement fixes (code specs)
  • CI/CD runs daily activity reports and KPI tracking

Project Setup

# Initialize chant with enterprise features
chant init --agent claude

# Create context directory for KPI data
mkdir -p .chant/context/kpi-churn-q1

The .chant/context/ directory holds human-curated data that agents can reference during research and implementation.

What’s Next

With the OKR defined and project initialized, the team begins the four-phase workflow:

  1. Data Ingestion — Mike gathers metrics, support data, and survey results
  2. Research — Chant agent analyzes the data for churn drivers
  3. Approval — Sarah reviews and approves the analysis
  4. Implementation — Chant executes approved fixes in parallel