Setup
Define the source boundary, ingest the supplied assets and establish what the analysis can inspect.
Deterministic boundaryApplication & data modernization
Understand the source.
Decide what replaces it.
modernAIze reconstructs supported legacy applications and data systems into a semantic model your team can inspect. It records the approved target design, generates the target project and keeps the result connected to the available evidence.
Source-to-target contract
Source evidenceDeterministic parsingStructure, relationships and explicit gaps
Trazadera modernAIzeSemantic modelAI enrichment with human review
Approved targetBlueprint and projectGenerated output with validation criteria
A governed path
Evidence first. Generation last.
The work moves through four connected stages. Automation builds and tests the model; the people responsible for the system review its meaning and approve the target before generation.
Understand
Move from system briefing to regions and exact source-backed detail, with confidence and missing knowledge visible.
Team reviewOptimize
Compare target structures, apply the relevant blueprint and approve the intended architecture.
Team approvalModernaize
Generate the target project and inspect its files, execution record and available quality evidence.
Reviewable outputNorthstar University
One estate, from source to output.
This synthetic Qlik estate follows the same project through Setup, three levels of understanding, an approved target architecture and the resulting dbt project. Move through the pages to see how the available source becomes a reviewable target.
Setup
Establish the estate boundary.
Northstar begins with two supplied Qlik scripts. Setup records the source platform, the files received and their readiness for analysis.
Understand · Briefing
Explain the estate as a system.
The first view gives the project a business context and reduces the source into a readable flow from inputs through transformations to reporting targets.
Understand · Landscape
See where the work is concentrated.
The region landscape groups the reconstructed estate and compares areas by size, complexity and available evidence before anyone chooses where to investigate.
Understand · Detail
Inspect the logic behind a finding.
The detailed view connects a recorded metric to its formula, dependencies and source evidence so that the interpretation can be checked.
Optimize
Choose and approve the target shape.
The accepted proposal organizes the source into staging, transformation and presentation layers, with frozen snapshots retained for the target workflow.
Optimize · Blueprint
Make target conventions explicit.
The Star Schema blueprint records reusable rules for staging, dimensions, facts, naming and layer dependencies before output is produced.
Modernaize · Output
Inspect the generated target project.
The result is a 19-file dbt project. Its model tree and SQL remain visible together, allowing engineers to review the generated structure and implementation.
Technology
One substrate holds the lifecycle together.
modernAIze separates source evidence from inference, gives model work bounded context and keeps the path to generated output inspectable. Eight technical components make that possible.
Deterministic extraction
Grammar, schema and audit-export parsers reconstruct what the supplied source exposes and validate it without an LLM.
TIL canonical model
A versioned, JSON-validated semantic model separates source connectors from target adapters and connects every stage.
Engineered context
Each task receives a bounded projection of the semantic graph: the constructs it needs, with a closed vocabulary.
Bounded orchestration
The estate is planned, clustered and divided among typed workers, then merged through schema validation and precedence rules.
Governed model execution
Every model call is routed, metered, costed and policy-controlled, including calls to configured private or local providers.
Blueprint-driven generation
Versioned blueprints express target conventions; adapters apply them through target-specific generation and quality rules.
Provenance & validation
Constructs retain their origin, confidence and transformation history so extracted fact remains distinct from inference.
Observability & cost
Runs trace stages, shards, workers and model calls with their timing, prompt version and attributed cost in one record.
Practical scope
Start with a representative system.
Coverage is assessed for the connector, target adapter and supplied system. The catalogue below shows the currently supported source and target technologies; the exact path and acceptance criteria are established during scoping.
Discuss your systemSource connectors
Systems modernAIze can reconstruct.
Application
COBOL
webMethodsData & analytics
DataStage
SAP BW
PowerCenter
Teradata
Qlik
SASTarget catalogue
Generated technologies and target platforms.
Target adapters
Java / Spring
dbtTarget platforms
Snowflake
BigQuery
Databricks
Redshift
Oracle
PostgreSQL
MySQL
SQL Server
Generic JDBCStart with evidence
What system are you considering?
Bring one representative application or data system, or a sanitized export. We can begin by checking the source boundary, the intended target and the evidence needed to continue.
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