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

AI Evaluation and Reliability for Production Readiness

A convincing demo does not establish production reliability. AI evaluation must test the actual workflow, sources, refusal behavior, blocked actions, review states, model or prompt changes, and failure signals that matter to the organization.

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Who this guide is for

Teams with an AI pilot, RAG workflow, reviewer application, or managed model dependency that needs measurable release gates before broader operational use.

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.

The pilot looks useful, but there is no representative test set or reviewed expected behavior.

Prompt, model, source, or retrieval changes can alter output without a regression signal.

The team tracks answer quality but not refusal quality, blocked actions, reviewer disagreement, or escalation.

Production rollout has no explicit thresholds, rollback criteria, or owner for ongoing evaluation.

Technical approach

Reduce risk with explicit evidence, boundaries, and release decisions

Define workflow-specific quality, evidence, refusal, consistency, review, and safety measures.
Create representative gold-answer, refusal, blocked-action, and adversarial examples with provenance.
Run baseline evaluation across current models, prompts, retrieval, and policy configurations.
Set release thresholds, drift checks, review cadence, incident signals, and rollback rules.

Expected engagement outcomes

Evaluation rubric and representative test set.
Baseline report for factuality, source support, refusal, consistency, and reviewer agreement.
Blocked-action, drift, and failure-state monitoring plan.
Release-gate and rollback decision model.

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.

AI Evaluation and Reliability Program

Human-Reviewed AI Workflow Accelerator

Related case narrative

Related buyer resource

Open the related checklist or guide

Frequently asked questions

Questions buyers use to qualify this solution area

What is a gold-answer set?

It is a reviewed collection of representative questions, expected evidence, acceptable answers, refusals, and failure examples used to compare workflow versions.

How often should evaluation run?

Evaluation should run before material model, prompt, source, retrieval, policy, or tool changes and on a recurring cadence appropriate to workflow risk.

Can evaluation certify an AI system as safe?

No. The program creates scoped evidence, thresholds, and operating controls. It does not provide a general safety certification or compliance guarantee.

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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