The Situation
A leading company in the life science industry was confronting an uncomfortable truth about its core technology platform. The system processed hundreds of thousands of specimens annually, powered ordering and resulting workflows for thousands of clinicians, and served as the operational backbone of the business. But after years of incremental development, the platform had become a 3.6-million-line codebase spread across 204 projects, with 76% of the code still running on legacy frameworks including VB.NET WebForms and ASMX SOAP services.
The symptoms were impossible to ignore. The lab worklist page, used by every technician, pathologist, and supervisor, took nearly 23 seconds to load with over 20,000 daily hits. Specimen accessioning took 10+ seconds per save, with 150 to 372 redundant database calls on each transaction. Report generation required 30 to 50 seconds on a legacy engine. Users lost work when servers attempted to load balance. The login page returned errors under peak load. A 21-terabyte primary database was growing at 11 GB per day with no archiving strategy, while 27 TB of documents stored as database BLOBs inflated every backup cycle.
The engineering team had begun modernizing, and their strategic direction was sound. A next-generation platform and modular API layer had been successfully built for some capabilities. But the modernization was incomplete: workflow management was 100% legacy, resulting screens were 85%+ legacy, and deployments still caused 45 minutes of downtime on every monthly release. Test coverage sat at roughly 10% against a healthcare industry average of 40 to 60%. Leadership needed a clear-eyed assessment and an executable plan, not another strategy deck.
The Challenge
Our Approach
Aubrant Technical Advisory deployed a fundamentally different kind of assessment. Instead of interviewing stakeholders and producing a strategy deck, the team used AI to analyze every project, every file, and every pattern across the entire codebase, then validated findings against real production performance data and live discovery sessions with the engineering team. Every finding is traceable to specific files, line numbers, URIs, or session recordings. This is not traditional consulting. This is evidence-based, AI-powered technical due diligence.
The assessment produced 13 detailed analysis reports from the source code, analyzed 1,966 production performance traces from Dynatrace monitoring over 7 days, synthesized 5 live discovery sessions with engineering leadership, and benchmarked the platform against a modern life science platform reference architecture. From this evidence base, the team built a four-phase modernization approach spanning 18 to 24 months, each phase delivering visible business value while progressively retiring legacy code.
AI-Powered Source Code Analysis
3.6 million lines of code analyzed by AI across 204 projects and 6 solutions. The AI identified 5,865 instances of legacy data layer patterns, 1,382 VB.NET files requiring migration, 1,666 ASMX WebMethods to convert to REST endpoints, and 18,274 lines of untested critical-path code.
Production Performance Forensics
1,966 Dynatrace application traces analyzed across 100+ endpoints over 7 days. Findings were impact-scored and prioritized: worklist at 22.85s, specimen save at 10.31s, report signing at 75.47s. Real data from the live environment, not theoretical benchmarks.
Business Pain Point Discovery
5 live sessions with engineering leadership identified 21 pain points across ordering, client portal, operations, and patient experience. Each pain point was validated against source code and architecture, connecting business frustration to specific technical root causes.
Four-Phase Modernization Roadmap
Phase 1: Make it fast and stable (months 1 to 5). Phase 2: Fix the client experience (months 5 to 10). Phase 3: Modernize lab operations (months 10 to 18). Phase 4: Platform independence (months 18 to 24). Each phase delivers measurable outcomes, not just progress reports.
AI Acceleration Quantification
Identified approximately 43,000 developer-hours of savings using Aubrant Workbench, mapped to specific acceleration opportunities across the codebase. This is not theoretical: the same AI tools that performed the assessment accelerate the execution, compressing the transformation timeline by an estimated 40 to 60%.
What Made It Work
Evidence Over Opinion
Every finding is traceable to a specific file, line number, URI, or monitoring trace. When we say the worklist takes 22.85 seconds, we can show the exact Dynatrace trace. When we identify 5,865 data layer patterns to modernize, we can point to each instance in the codebase.
AI Did the Analysis in Days, Not Months
Traditional consulting assessments of this scope take 3 to 6 months. Aubrant delivered 13 detailed reports from 3.6M lines of code in weeks by applying AI to the source analysis itself. The same approach then accelerates the actual modernization work.
Validated the Team's Direction
The assessment confirmed that the engineering team's strategic direction (their next-gen platform and modular API) was sound. Instead of recommending a rewrite, Aubrant built a plan that completes and accelerates the modernization already in progress.
Connected Business Pain to Technical Root Cause
Each of the 21 business pain points was mapped to specific technical findings. Clinicians experiencing slow worklists? 20,000+ daily hits on a page with 22.85-second P95 latency. This precision builds executive confidence in prioritization decisions.
Phased Value, Not Deferred Value
Phase 1 delivers the highest-impact performance fixes first: the screens used most become noticeably faster, sessions stop dropping, reports generate in seconds. Trust is built before the heavier modernization begins.
Advisory That Leads to Execution
Aubrant does not deliver slides and walk away. The advisory defines the path, Studios teams execute, and Workbench accelerates with AI-powered code generation, test generation, and migration tools. One team, one accountability.
The Outcomes
Of source code analyzed by AI across 204 projects, producing 13 detailed technical reports with findings traceable to specific files and line numbers
Identified across 5 live discovery sessions, each mapped to specific technical root causes and prioritized against business impact
Of developer savings identified using Aubrant Workbench, mapped to specific acceleration opportunities across the codebase, compressing the modernization by an estimated 40 to 60%
Four-phase modernization roadmap with measurable milestones: worklist from 23s to under 1.5s, deployment downtime from 45 minutes to zero, and weekly release capability
Investment case with phased funding model, enabling leadership to commit resources with confidence and clear decision gates between phases
After just two weeks, Aubrant could point to specific files, line numbers, and patterns that no other firm could match, because AI analyzed every line, not a sample
Why Others Fail
The Takeaway
Legacy platform modernization in healthcare is not a technology project. It is a patient care project, a clinician experience project, and a business sustainability project that happens to require deep engineering. The organizations that will succeed are not the ones who commission the most comprehensive strategy deck. They are the ones who demand evidence: every line of code analyzed, every performance trace quantified, every business pain point connected to a technical root cause. When AI powers the assessment itself, the depth of insight that took months now takes weeks, and the same AI tools that revealed the truth become the accelerators that compress the solution. That is the difference between advisory that collects dust and advisory that becomes an executable plan.