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Case Studies in Legacy Modernization and Governed AI Delivery
Review public-safe case studies showing environment, risk, approach, controls, artifacts, measurement models, and outcome boundaries for modernization and governed AI work.
Public-safe narrative grounded in existing modernization and parity-validation proof themes; approved outcome data can be added when supplied.
Mapping Legacy .NET and SQL Behavior Before a Modernization Rewrite
A public-safe case path for safely moving brittle .NET and SQL-heavy systems toward modern service seams.
- Buyer context
- An application owner needs to modernize a brittle Microsoft-stack system, but current production behavior is distributed across UI flows, stored procedures, reports, scheduled jobs, and undocumented exceptions.
- Technical environment
- Legacy .NET / ASP.NET application patterns, SQL Server and stored procedures, Reports and exports
- Business risk
- A rewrite can silently change calculations, approvals, or operational behavior before the business notices.
- Evidence type
- Public-safe narrative, artifact list, control model, and measurement categories.
Read the case narrative and measurement model
Public-safe narrative tied to the AI documentation review and TypeScript/Angular workflow evidence; approved outcome data can be added when supplied.
Keeping AI-Generated Documentation Reviewable Before Operational Use
A public-safe case path for using AI to accelerate code understanding without treating generated documentation as automatically approved.
- Buyer context
- An engineering or operations team wants AI-assisted documentation, summaries, or migration notes, but cannot let unsupported text become accepted system knowledge.
- Technical environment
- Legacy code and database context, Documentation or migration-note workflow, Reviewers with domain knowledge
- Business risk
- AI-generated documentation can widen claims, miss edge cases, or become stale if it is not source-bound and reviewed.
- Evidence type
- Public-safe narrative, artifact list, control model, and measurement categories.
Read the case narrative and measurement model
Public-safe narrative tied to source-governed memory, LLMWikis/AIWikis, and UAIX-style handoff themes; approved outcome data can be added when supplied.
Building a Governed RAG Foundation for Source-Bound Internal Knowledge
A public-safe case path for replacing scattered prompt residue with source-governed knowledge and retrieval.
- Buyer context
- A knowledge-heavy team needs better retrieval across documents, SOPs, tickets, and technical notes, but source ownership, content age, and access rules vary.
- Technical environment
- Multiple document and knowledge repositories, Conflicting or stale content, Internal access controls
- Business risk
- Generic retrieval can mix unapproved drafts with authoritative knowledge and produce answers that are hard to audit.
- Evidence type
- Public-safe narrative, artifact list, control model, and measurement categories.
Read the case narrative and measurement model
Public-safe narrative tied to evaluation-lab and calibration proof themes; not a safety certification.
Evaluating AI Behavior Before It Becomes a Production Dependency
A public-safe case path for teams that need AI behavior measured, reviewed, and blocked when evidence is insufficient.
- Buyer context
- A team has an AI pilot that appears useful, but there is no stable test set, release threshold, reviewer agreement model, or drift-monitoring plan.
- Technical environment
- LLM or RAG pilot, Human reviewers, Representative questions and sources
- Business risk
- Ambiguous output may reach users, production workflows, or public materials without adequate review.
- Evidence type
- Public-safe narrative, artifact list, control model, and measurement categories.
Read the case narrative and measurement model
System and risk context
Name the system type, modernization risk, hidden business-rule area, or AI workflow hazard without exposing confidential details.
Control method used
Show the parity strategy, review queue, source-bound retrieval model, evaluation rubric, or blocked-action control that reduced risk.
Artifact preview
Include a sanitized screenshot, sample table, checklist, ledger row, architecture map, or deliverable excerpt.
Outcome or decision
Publish only approved metrics or qualitative outcomes, such as reduced rediscovery, clearer release gates, or approved pilot scope.
Boundary note
State what the example does not prove: no universal zero-regression guarantee, certification, vendor partnership, or autonomous production authority.
Next step
Start with a short fit call, then scope the assessment.
The first conversation should decide whether the next step is a fixed-scope assessment, modernization blueprint, governed AI pilot, or reliability review.
Book a 20-minute fit call
