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.

Solution guide

Enterprise RAG Governance for Source-Bound Knowledge Systems

A reliable enterprise RAG system is a governed knowledge product, not just a vector index. It needs explicit content ownership, access boundaries, provenance, source states, evaluation examples, citation rules, and a path for refusing or escalating unsupported answers.

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Who this guide is for

Knowledge, compliance, operations, support, and engineering teams that need internal AI answers grounded in reviewed documents, policies, code notes, procedures, or technical records.

These solution pages use conventional search and procurement language to explain the buyer problem. The productized service pages remain the source of current package scope, timelines, and pricing floors.

Common buyer signals

When this problem usually needs structured architecture work

The examples below are common patterns, not claims about a specific client or guarantee that every environment requires the same response.

Documents are indexed without clear owners, approval status, access scope, or stale-content rules.

Answers appear plausible but users cannot inspect the supporting source or content status.

Retrieval quality is measured only by demos rather than representative questions and failure cases.

The system lacks refusal, escalation, and correction paths when evidence is absent or conflicting.

Technical approach

Reduce risk with explicit evidence, boundaries, and release decisions

Inventory source systems, owners, access rules, content types, and publication states.
Define ingestion, normalization, provenance, trust-state, and stale-content policies.
Design retrieval, citation, answer-boundary, refusal, escalation, and reviewer workflows.
Evaluate representative questions, source support, access behavior, stale sources, and unsupported-answer handling.

Expected engagement outcomes

Source inventory, ownership model, and content-state taxonomy.
Provenance, metadata, access, and retrieval architecture.
Cited-answer, refusal, correction, and escalation rules.
Evaluation set and operating playbook for ongoing knowledge governance.

Related packages and evidence

Move from category research to a concrete starting scope

Review the related service, public-safe case narrative, and buyer resource before sharing private system details.

Governed Knowledge / RAG Foundation

AI Evaluation and Reliability Program

Related case narrative

Related buyer resource

Open the related checklist or guide

Frequently asked questions

Questions buyers use to qualify this solution area

How is governed RAG different from vector search?

Vector search is one retrieval technique. Governed RAG also defines source ownership, access, provenance, review states, citations, stale-content handling, evaluation, refusal, and correction.

Can private and public sources be mixed?

Only when access boundaries, user identity, retrieval authorization, logging, and publication rules are explicitly designed. Source visibility should not exceed the user’s permitted access.

Does RAG eliminate hallucination?

No. Retrieval can improve source grounding, but the system still requires evaluation, evidence display, refusal behavior, and review for high-impact uses.

Next step

Confirm whether the problem fits before sharing sensitive system details.

Use a short fit call to identify the likely assessment or package. Public forms should not contain source code, credentials, PHI, customer records, financial records, or confidential production architecture.

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