Sail JADA — Engineering Log

Technical work on sailjada.com and the JADA booking systems

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Building a Multi-Domain Executive Intelligence System: Automated Reporting, Lambda Orchestration, and Cross-Entity Visibility

Over the past development session, we built and deployed a comprehensive executive reporting infrastructure that synthesizes operational, financial, technical, and strategic data across four distinct business entities (JADA, QueenofSanDiego, QuickDumpNow, DangerousCentaur) plus three ancillary properties. This post covers the architecture, deployment pipeline, and key technical decisions that enable C-suite stakeholders to see the entire portfolio at once.

What Was Built

The core deliverable is a Python-based reporting engine that generates five specialized executive reports, each written from a distinct stakeholder perspective:

  • CEO Report: Asset inventory, critical shortfalls, missing KPIs, 30-day prioritized agenda
  • CTO Report: Stack-by-stack security audit, cost analysis, UX gaps, dev cycle improvements, 10 prioritized engineering actions
  • Accounting Report: Revenue recognition audit, chart of accounts, expense categorization, 4-milestone roadmap to profitability
  • CMO Report: Channel-by-channel visibility matrix, OTA sequencing strategy, blast email modeling, 30/60/90-day milestones
  • CFO Report: Burn rate modeling, capital deployment framework, break-even analysis, monthly targets through Q4 2026

Three additional reports (3028 51st St Rental, Expert Yacht Delivery billing audit, DC Client Portfolio audit) extend visibility to the full property portfolio.

Technical Architecture

Report Generation Engine

The entry point is /Users/cb/Documents/repos/tools/send_exec_reports.py, a Python script that orchestrates report generation and email delivery:

# Pseudocode structure
import boto3
import json
from datetime import datetime

ses_client = boto3.client('ses', region_name='us-west-2')

def generate_ceo_report(entities):
    # Synthesize asset inventory from all 4 entities
    # Cross-reference with handoff docs
    # Generate shortfall analysis
    # Calculate missing KPIs
    return formatted_report

def send_via_ses(recipients, subject, body):
    # Use SES for reliable delivery
    # BCC admin@queenofsandiego.com for audit trail
    # Handle bounce/complaint feedback
    ses_client.send_email(
        Source='admin@queenofsandiego.com',
        Destination={'ToAddresses': recipients, 'BccAddresses': [...]},
        Message={...}
    )

The script reads SES configuration from repos.env (verified sender address, region settings) and iterates through each report template, injecting entity-specific data from markdown handoff documents stored in /Users/cb/Documents/repos/agent_handoffs/projects/.

Data Sources & Integration Points

Reports pull from multiple sources without a centralized database:

  • Handoff Documents: shipcaptaincrew.md and related project files contain operational context, status, and known gaps
  • AWS Infrastructure: Lambda functions, S3 buckets, CloudFront distributions audited via direct inspection
  • Email Logs: SES receipt rules and bounce feedback for channel analysis
  • Code Analysis: Repository structure, deployment patterns, tech stack enumeration

This approach—building reports from dispersed sources—revealed a critical gap: no unified observability or data warehouse. The accounting report specifically identifies this as a Q1 2027 blocker.

Deployment & Infrastructure Changes

SES Configuration

All reports route through Amazon SES in us-west-2. The verified sender is admin@queenofsandiego.com (already validated in SES console). Each report includes a BCC to the same address for audit trail compliance.

Why SES over alternatives: Low cost (~$0.10 per 1,000 emails), reliable delivery tracking, built-in bounce/complaint feedback loop, and integration with SNS for automated error handling on production deployments.

Lambda & Frontend Deployments

During this session, we also made multiple iterations to the Ship Captain Crew tool (the charter booking interface):

  • Lambda function: /Users/cb/Documents/repos/sites/queenofsandiego.com/tools/shipcaptaincrew/lambda_function.py — event creation, JWT auth, waiver tracking, role management, magic link generation
  • Frontend: .../shipcaptaincrew/frontend/index.html — DOM updates for event timing panels, checklist UI, claim/release role interactions
  • Deployment target: Lambda deployed to AWS; frontend deployed to S3 with CloudFront invalidation

Pre-deployment, we syntax-check the Lambda function and verify all environment variables (JWT_SECRET, DynamoDB table names, SES config). Post-deployment, we test the magic link auth endpoint and verify the event/checklist retrieval logic.

Key decision: We maintained a staging workflow (syntax check → deploy → test) even for this internal tool. This prevents typos in JWT handling or DynamoDB queries from breaking the production user experience.

Why This Matters: The Intelligence Gap

Before this session, the CEO, CTO, CFO, and CMO had fragmented views:

  • CEO saw individual project handoffs but not the cross-entity P&L or capital allocation story
  • CTO audited code in isolation, missing security patterns repeated across domains
  • CFO had no revenue tracking system or expense categorization—only bank statements
  • CMO had channel plans but no unified acquisition cost modeling

The new reporting system creates a single source of truth, generated on-demand, that shows interdependencies and gaps. For example:

  • CEO learns that QDN's broken funnel (no cart, no checkout) directly impacts CFO's revenue target
  • CTO sees that hardcoded Stripe keys in code repos pose security risk across multiple domains
  • Accounting uncovers that DC has no billing model, so revenue can't be recognized
  • CMO sees that the 3,676-person email list is untapped, with potential $10K–50K concert booking pipeline

Key Technical Decisions

1. Python for Report Generation, Not a Data Warehouse
Rather than building a database-backed reporting system (expensive, slow to implement), we chose lightweight Python scripts that parse markdown handoffs, inspect code repositories, and synthesize output. This lets executives see decisions and gaps in a day rather than waiting for infrastructure.

2. SES for Delivery, Not Internal Dashboards
Email reports are asynchronous and work offline. Executives can forward them, annotate them, and reference them in Slack threads. A web dashboard would require auth, hosting, and maintenance—email scales without that overhead.

3. Role-Based Report Tailoring, Not One-Size-Fits-All
Each report (CEO, CTO, CMO, CFO, Accounting) emphasizes different metrics and decision points. The CTO doesn't need burn rate; the CFO doesn't need cloud