Legacy .NET / SQL modernization · human-reviewed AI · principal-led architecture
LongTermSoftware helps teams make hidden behavior visible before a rewrite, define safer modernization seams, and build AI workflows that keep evidence, reviewers, and downstream authority under control.
Who we help
These are the situations where a fixed-scope assessment, blueprint, pilot, or evaluation program can reduce decision risk.
Buyer risk: A working system is expensive to change because behavior is buried across code, forms, reports, and data rules.
Response: Map current behavior and regression risk before choosing a rewrite, strangler seam, or migration sequence.
Buyer risk: Critical calculations and approvals live in SQL Server without complete tests or documentation.
Response: Inventory SQL-side rules, select comparison scenarios, and define parity checkpoints before replacement.
Buyer risk: The platform still supports operations, but framework age and coupling make releases increasingly risky.
Response: Introduce testable seams and service boundaries without pretending the old behavior can be rediscovered later.
Buyer risk: AI drafts are useful, but the team cannot allow unreviewed output to become a customer or production action.
Response: Design review states, blocked actions, evidence visibility, fallback rules, and audit trails around one workflow.
Buyer risk: Policies, SOPs, code notes, and support knowledge conflict or go stale across many source systems.
Response: Create explicit source boundaries, provenance metadata, review states, retrieval tests, and citation rules.
Buyer risk: A pilot looks promising, but no one has defined acceptable failure, refusal, drift, or reviewer disagreement.
Response: Build test sets, measurement categories, release gates, blocked-action checks, and rollback criteria.
Common starting scenarios
A buyer should not have to decode the full services catalog before finding the most relevant path.
Start with behavior inventory, stored-procedure review, representative comparison scenarios, and a sequenced modernization blueprint.
Review the modernization blueprintDefine source checks, reviewer roles, approved and blocked states, escalation, and downstream permissions before scaling.
Review the AI workflow acceleratorEstablish content ownership, trust labels, retrieval boundaries, stale-content handling, and answer citation rules.
Review the governed RAG foundationUse a fixed-scope assessment to identify dependency hotspots, missing tests, business-rule ownership, and the safest first seam.
Review the assessment scopeTranslate governance requirements into testable workflow controls, evidence visibility, audit events, and release criteria.
Review the reliability programWhat LongTermSoftware does
The commercial promise stays concrete: preserve important behavior, keep AI reviewable, and make proof available at the level each stakeholder needs.
Map hidden SQL, reporting, approval, and UI behavior before modernization so new services can be compared against current production behavior.
Design AI workflows with explicit evidence, reviewer states, fallback rules, blocked actions, and bounded downstream authority.
Use scoped assessments, public-safe artifacts, case narratives, machine-readable ledgers, and clear boundary notes instead of unsupported marketing language.
Solution guides
These guides use conventional enterprise search language. The service pages remain the source of current scope, pricing, deliverables, and commercial boundaries.
Modernize brittle .NET, SQL Server, Web Forms, Classic ASP, and VB.NET systems by mapping current behavior, hidden business rules, service seams, parity tests, and staged release decisions.
Get principal-led support for aging .NET and SQL systems, recurring architecture decisions, database risk review, testing strategy, delivery repair, and modernization governance.
Design human-reviewed AI workflows with visible sources, explicit blocked actions, named approval ownership, audit events, escalation paths, and limited downstream authority.
Build retrieval-augmented generation with source ownership, access control, provenance, trust states, citations, stale-content handling, evaluation, refusal rules, and human escalation.
Evaluate AI workflows with representative test sets, source-support and refusal checks, blocked-action tracking, reviewer agreement, drift monitoring, release thresholds, and rollback rules.
Service ladder
Each package is a bounded starting point with public planning guidance, named outputs, and a clear commercial boundary.
2-week entry engagement
A fixed-scope assessment for legacy systems, internal AI workflows, governed knowledge, or AI reliability before committing to a build.
Legacy-system risk reduction
A modernization plan for brittle .NET, SQL Server, Web Forms, Classic ASP, VB.NET, or stored-procedure-heavy systems that cannot casually change behavior.
Review-first pilot
A scoped AI workflow where generated output is drafted, reviewed, accepted, rejected, or blocked before it affects production work.
Source-governed retrieval
A knowledge foundation for documents, SOPs, policies, code notes, and support content that need provenance and trust labels, not generic vector sprawl.
Internal AI product
A reviewable internal AI app such as a documentation reviewer, analyst assistant, triage workbench, or migration-note reviewer with typed UI and audit trails.
Measure before scaling
A reliability program for AI pilots that need rubrics, fallback rules, drift checks, trace models, blocked-action logging, and reviewer worksheets.
Ongoing delivery stewardship
Quarterly architecture stewardship for vendor/model decisions, backlog shaping, code and data review, governance oversight, and executive updates.
How the first engagement works
The first conversation stays low-friction. Detailed evidence and private system access move into the right channel only after fit and scope are clear.
Confirm whether the problem matches the service model without asking for secrets, source code, PHI, or private production data.
Identify system types, business owners, known risk, evidence access, and secure-channel needs.
Define scope, artifacts, responsibilities, timeline, acceptance criteria, and explicit exclusions.
Review maps, risks, test gaps, workflow controls, evidence, and unresolved questions with the right stakeholders.
Proceed to a blueprint, pilot, implementation, evaluation program, retainer, or no further work based on the evidence.
Do not submit through public forms: secrets, credentials, private source code, PHI, customer records, financial account data, or confidential production architecture.
Proof before purchase
Public pages distinguish methods and sample artifacts from approved client outcomes so a buyer can understand what each proof surface does and does not establish.
Problem, risk, intervention, controls, artifact preview, and approved outcome or decision. This is the format for non-technical stakeholders.
Claim, evidence type, proof route, maturity status, and boundary note. This is the format for architects and procurement reviewers.
Sample checklists, proposal templates, proof packets, and diagnostic outputs that show how deliverables are structured before a purchase.
llms.txt, AI manifest, proof ledger JSON/CSV, and route QA files for AI agents, procurement tools, and deep technical review.
Security, review, and source-bound AI posture
Controls are selected for the actual workflow and risk. Public materials do not claim certification, universal safety, or autonomous production authority.
Generated drafts, recommendations, classifications, and summaries do not automatically become production actions.
Each workflow identifies which sources are allowed, what evidence must be visible, and when an answer should refuse or escalate.
Public forms and diagnostics must not receive secrets, credentials, PHI, private code, or regulated records; sensitive work moves to an approved private channel.
A workflow receives only the access required for its bounded task, with privileged writes and irreversible actions gated separately.
Important review decisions, rejections, blocked actions, and escalation paths should be observable and exportable.
Local, private, or managed model options are selected after data, integration, latency, cost, and governance constraints are understood.
Featured case studies
Each case explains the environment, business risk, intervention, controls, artifacts, and measurement model without inventing client metrics.
Public-safe narrative grounded in existing modernization and parity-validation proof themes; approved outcome data can be added when supplied.
A public-safe case path for safely moving brittle .NET and SQL-heavy systems toward modern service seams.
Public-safe narrative tied to the AI documentation review and TypeScript/Angular workflow evidence; approved outcome data can be added when supplied.
A public-safe case path for using AI to accelerate code understanding without treating generated documentation as automatically approved.
Public-safe narrative tied to source-governed memory, LLMWikis/AIWikis, and UAIX-style handoff themes; approved outcome data can be added when supplied.
A public-safe case path for replacing scattered prompt residue with source-governed knowledge and retrieval.
Public-safe narrative tied to evaluation-lab and calibration proof themes; not a safety certification.
A public-safe case path for teams that need AI behavior measured, reviewed, and blocked when evidence is insufficient.
Buyer resources
Each major resource now has a human-readable landing page that explains who should use it, how to use it, what it does not prove, and which service or case it supports.
Open ResourcesA buyer and technical-team checklist for deciding whether an AI use case has clear sources, owners, review boundaries, and measurable value before tool selection.
Review Enterprise AI Readiness ChecklistA structured review for stored procedures, hidden business rules, parity tests, migration seams, release constraints, and rollback planning.
Review Modernization Risk ReviewA practical checklist for prompt contracts, typed outputs, reviewer roles, evidence visibility, fallback rules, blocked actions, and audit destinations.
Review Human-Reviewed AI Workflow ChecklistReusable proposal structures for an assessment, modernization blueprint, review-first AI pilot, and AI evaluation engagement.
Review Enterprise Proposal Template PacketFrequently asked questions
The answers stay plain-language and do not widen public claims beyond the evidence available.
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.
Not by default. The preferred path is to map risk, introduce testable seams, and modernize in bounded stages when that reduces operational risk.
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.
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.
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.
AI output remains proposed work until a named reviewer can inspect evidence, edit, approve, reject, block, or escalate it under explicit workflow rules.
Governed RAG adds source ownership, access control, provenance, content states, citation rules, refusal behavior, evaluation, and human escalation around retrieval.
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.
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.
Public routes collect only high-level context. Private code, credentials, PHI, customer records, and confidential architecture require a separately approved secure handling path.
Next step
If the problem fits, the next step is a fixed-scope package with named artifacts, boundaries, responsibilities, and acceptance criteria.
