ELEVATE™ is our AI-native methodology for software and database modernization. It treats your legacy application as the specification for its successor, preserves the business logic built up over decades, and keeps human experts in charge of every release gate.
Billing, supply chain, regulatory tracking and pricing often run on applications that hold decades of business intelligence. Replacing them is risky. Keeping them indefinitely is a compounding risk of its own.
“Rewrite the old application using modern technology” sounds simple. Without current requirements, it becomes a manual exercise in rebuilding knowledge, and your own people pay for it.
Unlike code converters, ELEVATE™ analyzes everything together: documents, code, databases and integrations. From that it builds a permanent, technology-agnostic Enterprise Knowledge Model, a semantic layer that separates business capability from physical code.
Business rules are often split between application code and the database, for example in stored procedures. ELEVATE™ runs two workstreams together so nothing is lost between them.
Monoliths are parsed to find domain boundaries, then decomposed into modern microservices. Legacy languages become clean, secure code with built-in API frameworks and automatically compiled unit tests.
Relational structures are refactored for cloud-native performance. Stored procedures, views and triggers are translated into target SQL, and migrations are checked by multi-layer cryptographic reconciliation of counts, hashes and referential integrity.
AI-assisted discovery and automated validation bring speed, consistency and traceability.
| Dimension | Traditional manual modernization | d-insights ELEVATE™ |
|---|---|---|
| Landscape discovery | Predominantly manual analysis; code files read one by one; knowledge stays scattered and fragmented. | Automated, repository-scale AI analysis converts legacy files into a structured, unified Enterprise Knowledge Model. |
| SME time commitment | 30–40% of valuable daily operational hours spent in workshops. | Under 10%, restricted to quality and governance gates. |
| Database strategy | Database migrated flatly; stored logic and triggers handled manually. | Modernized in lockstep with application logic, with automated SQL translation and cryptographic data reconciliation. |
| Traceability | Requirements are disconnected from implementations, test scripts and target models. | A durable semantic layer gives an immutable audit trail from business requirements to generated code to test cases. |
| Testing and quality | Testing follows coding; documentation is written manually at the very end. | Testing and documentation are generated continuously, with automated feedback loops and build gates running in parallel. |
| Timeline and speed | Slower, linear and high-risk, frequently 18 to 36 months. | Timelines cut by 50% through AI-orchestrated engineering pipelines. |
Each phase ends at a gate you control. You see results on your own code before committing to the next step.
We analyze codebase complexity, dependencies, databases and AI automation potential to produce a modernization roadmap.
The ELEVATE™ pipeline runs on one representative module, showing rule extraction and automated code generation on your actual codebase.
The pipeline scales to the full application and database, with automated unit tests and continuous data reconciliation.
A customized factory inside your IT portfolio industrializes intake, builds reusable knowledge blocks and compresses future engineering time by 50%.
For new applications under tight timelines, ELEVATE™ treats structured requirements as executable context. It plans database entities, establishes REST API layers and outputs clean microservices, for a 50% overall timeline reduction. Developers move up to the role of system architecture gatekeepers.
Begin with an assessment. We will map your legacy estate, show where automation pays off most, and propose a pilot module to prove it.