FOUNDATION
Systems Recovery
Scoped after Discovery Call • 4-8 weeksBefore automation or AI can work reliably, core systems must be clean, consistent, and owned.
This service focuses on repairing the foundation everything else depends on.
Automation multiplies whatever it touches. Including your problems.
Before you can automate reliably, your systems need to behave consistently. Most operational failures aren't caused by bad automations—they're caused by unstable foundations that automation then amplifies.
Systems Recovery & Operational Foundation is about repairing the infrastructure everything else depends on: data integrity, system ownership, lifecycle definitions, and source-of-truth clarity.
This work is unsexy. It doesn't demo well. But it's the difference between automations that work for months and automations that work for years.
Why This Matters
- -Dirty data doesn't get cleaner when you automate it—it spreads faster
- -Undefined ownership means no one catches problems until they're crises
- -Conflicting automations create chaos that compounds over time
- -Without clear sources of truth, every integration becomes a liability
Common Symptoms
Signs you might need this service
What we do
- —Data normalization and cleanup
- —Pipeline and lifecycle redesign
- —Source-of-truth enforcement
- —Removal of duplicate or dead automations
- —Clarification of system ownership
What you get
- —Clean, reliable data foundations
- —Consistent system behavior
- —Fewer silent failures
- —Readiness for more advanced systems
How It Works
System Inventory
We catalog every tool, database, integration, and automation in your operational stack.
Data Quality Assessment
We analyze data integrity across systems—duplicates, conflicts, missing fields, and decay patterns.
Ownership Mapping
We define who owns each system, each data type, and each critical process.
Lifecycle Redesign
We standardize status definitions, stage progressions, and state transitions.
Data Normalization
We clean historical data, merge duplicates, and establish data hygiene rules.
Source of Truth Enforcement
We configure systems so truth flows one direction, eliminating sync conflicts.
The Transformation
Before
- -Data exists in multiple conflicting places
- -No clear system ownership
- -Status fields mean different things
- -Fear of changing anything
- -Automations that fight each other
After
- +Single source of truth per data type
- +Clear ownership and accountability
- +Standardized lifecycles and statuses
- +Confidence to iterate and improve
- +Automations that reinforce each other
In Practice
Case Example
Real-World Application
Scenario
Property Management Company
The Problem
A 200-unit property manager had leads in three systems: their CRM, a marketing platform, and a spreadsheet the leasing team maintained. Each showed different numbers. Automation attempts failed because there was no single truth.
The Solution
We established the CRM as the canonical source, built one-way sync rules, cleaned 18 months of duplicate and conflicting records, and defined clear ownership: marketing owns lead creation, leasing owns qualification, CRM owns the record.
The Outcome
Lead response time dropped from 4 hours to 12 minutes. More importantly, the team trusts their data for the first time in years.
Typical Results
15-40%
Duplicate Records Resolved
Of total database
8-15
Conflicting Automations Removed
Per engagement
60-85%
Data Accuracy Improvement
After normalization
Is This Right For You?
Ideal For
- +Companies with "messy" CRM or operational data
- +Teams that have accumulated technical debt from past tools
- +Organizations preparing for automation or AI deployment
- +Businesses that have grown faster than their systems
Not Ideal For
- -Startups with clean-slate systems
- -Companies wanting automation without fixing underlying issues
- -Organizations unwilling to invest in foundational work
Engagement Type
Build
Ready to get started?
Start with clarity. No commitment required.