Using The Lifter to Audit Hybrid Systems and Validate Legacy Risk for a Large IT Enterprise
Deploying Legacy Lifter, a foundational component of The Lifter solution suite, to
support a top-tier IT enterprise in validating legacy risk and readiness before
de-risking platform modernization.
Customer Overview
A leading North American IT organization was laying the strategic groundwork to
modernize its most critical business systems.
Its environment spanned Informix 4GL, Java, PL/SQL, and Oracle Forms,
accumulated over decades and extended with deep, client-specific customizations.
These systems continued to power critical operations, but very few people could
confidently explain how everything fit together.
Leadership faced a familiar but high-stakes dilemma:
Before committing to timelines, budgets, or architecture decisions, the
organization needed assurance:
Engaging Indium as a strategic partner, they deployed The Lifter, specifically
leveraging the Legacy Lifter and Data Lifter modules, to resolve these critical
uncertainties prior to initiating transformation.
Modernization was inevitable, but proceeding without proof would create
unacceptable operational and compliance risk.
What exactly exists today?
Where are the real dependencies?
How much logic is customized vs standard?
Can AI-driven discovery be trusted at enterprise scale without a clear
understanding of the legacy?
gaging Indium as a strategic partner, they deployed The Lifter, specifically
leveraging the Legacy Lifter and Data Lifter modules, to resolve these critical
uncertainties prior to initiating transformation.
Deep Visibility through Code Intelligence
Legacy Lifter analyzed legacy systems across:
Informix 4GL
Java
PL/SQL
Oracle Forms
By working directly from source code and supporting
artifacts, insight was derived from facts, not assumptions or
interviews alone
Custom Logic Identification
A critical requirement was separating:
What came out of the box
What had been customized for specific business needs
The Lifter surfaced embedded custom logic and
client-specific behavior, allowing leaders to understand
where modernization risk actually lived.
Dependency, Flow Mapping & High-Risk Zones
We began by meticulously understanding the existing data flows ("AS IS") from the policy issuance and agency systems into the designated data platform (data warehouse or data lake).
This comprehensive mapping exercise ensured a seamless data integration process.
Human-Validated Confidence
AI-driven insights were reviewed with domain experts through a
human-in-the-loop validation model.
This ensured outputs were:
Business-accurate
Context-aware
Suitable for executive decision-making
What Was Delivered by The Lifter
engagement produced modernization assurance artifacts, including:
Feature and functionality inventories.
Business rule dictionaries extracted from live systems.
Cross-platform process and dependency maps.
Clear visibility into custom vs standard logic.
Structured documentation aligned to transformation planning.
e first time, leadership had evidence-backed clarity on
what modernization would truly involve.
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.