What does behavior-preserving modernization mean?

It means documenting the current system’s observable inputs, outputs, calculations, permissions, workflow states, and exceptions so intentional changes can be separated from accidental behavioral drift.

Do you rewrite legacy systems from scratch?

Not by default. The preferred path is to map risk, introduce testable seams, and modernize in bounded stages when that reduces operational risk.

What kinds of .NET systems are a fit?

Common fits include ASP.NET, ASP.NET Core, MVC, Web Forms, Classic ASP, VB.NET, C#, SQL Server, stored-procedure-heavy systems, reporting workflows, internal admin tools, and API modernization.

What if our business logic is mostly in SQL Server?

SQL-side business rules are treated as part of the application behavior. The modernization work maps stored procedures, jobs, reports, transactions, owners, and representative parity scenarios before replacement.

Can you help with AI without exposing private data?

Yes, when the engagement is designed around approved data boundaries, secure channels, least-authority access, and an appropriate local, private, hybrid, or managed model approach. Public forms must not receive secrets or regulated data.

What is human-reviewed AI?

AI output remains proposed work until a named reviewer can inspect evidence, edit, approve, reject, block, or escalate it under explicit workflow rules.

What is governed RAG?

Governed RAG adds source ownership, access control, provenance, content states, citation rules, refusal behavior, evaluation, and human escalation around retrieval.

What happens after the first fit call?

If the problem fits, the next step is usually a scoped assessment or package proposal. Sensitive system details move to an approved private channel rather than the public form.

Do you publish client metrics?

Only when the source material and exact wording are approved. Public-safe case pages distinguish methods, sample artifacts, measurement models, and approved outcomes so templates are not presented as client results.

How do you handle confidential systems?

Public routes collect only high-level context. Private code, credentials, PHI, customer records, and confidential architecture require a separately approved secure handling path.

Route QA

Machine-readable QA and proof paths

Expose llms.txt, route QA contracts, manifests, and public route indexes for technical reviewers and AI readers.

Evidence-first governance dashboard showing review gates, validation, audit trails, risk controls, and source-bound outputs.

Proof detail

Technical buyers and AI readers should have canonical discovery paths and QA contracts.

This proof item exists to route a public claim to a narrower supporting artifact. It is not a certification, guarantee, vendor partnership claim, or autonomous execution authority.

Primary supporting route: /proof/machine-readable-qa/

Boundary: Machine-readable files support read-only discovery and do not authorize probing or mutation.

Review posture

Source-bound claim language
Human review before claim widening
Public-safe narrative where marked
No AGI, consciousness, certification, or partner overclaim

Proof maturity

Public-safe narrative: A buyer-readable case or proof path that avoids private client code, data, metrics, or confidential workflow details.
Artifact preview: A sample deliverable, ledger, checklist, proposal packet, or workflow diagram that shows how the work is structured.
Needs approved outcome data: A proof route that is intentionally conservative until a client-safe metric, quote, or before/after result is approved.
Machine-readable evidence: JSON, CSV, llms.txt, or manifest data intended for technical reviewers, AI agents, and procurement tooling.

Evidence artifacts

llms.txt
AI agent manifest
route QA contract
route QA script
proof-ledger JSON/CSV

Controls

canonical static files
route contract
redirect expectations
download content-type checks
forbidden dependency scan

Related visual

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