Principal-led software architecture for critical systems

Modernize critical Microsoft systems. Put AI into production—with evidence.

Preserve business logic, human authority, and auditability. Map the system, measure representative behavior, and leave evidence that engineering, leadership, security, and procurement can inspect.

Discuss the system Compare starting engagements

Call 1 (464) 274-1476 Download the capability statement

20+ years of public professional history
Principal-led delivery
Fixed-scope entry
Client-owned evidence and handoff

Decision navigator

What decision is in front of you?

Choose the blocked decision. The buyer-specific path and the smallest credible engagement follow from it.

01Enterprise
An AI workload is approaching production
Measured behavior, human-review boundaries, release criteria, and procurement evidence.
Likely first pathAI Production Readiness & Evidence SprintReview this path
02Enterprise · Government
A .NET or SQL system is too risky to change
Current-state mapping, business-rule discovery, parity evidence, and reversible sequencing.
Likely first pathAI-Ready .NET & SQL Modernization BlueprintReview this path
03Enterprise · Partner
A vendor, buyer, or security review is blocked
Architecture, data flow, ownership, system cards, risk records, and evidence that can travel across the buying committee.
Likely first pathTechnical evidence capabilityReview this path
04Government · Partner
A government or prime workstream needs specialist depth
A bounded scope, direct technical access, accurate evidence labels, and explicit workshare.
Likely first pathGovernment and partner pathReview this path

Service comparison

Five bounded engagements. One canonical commercial model.

Prices, duration, payment terms, artifacts, acceptance, and exclusions are generated from the same reviewed source.

LongTermCapabilities service comparison
Service	Best trigger	Decision produced	Duration	Starting investment	Scope
AI Production Readiness Sprint	An AI feature, RAG system, copilot, reviewer, or agent is approaching production, enterprise review, or a consequential expansion.	Proceed, narrow, remediate, or stop - with a measured baseline and explicit release conditions.	3 weeks	$20,000	View scope
.NET & SQL Modernization Blueprint	A business-critical .NET or SQL Server system is difficult to change because behavior is hidden across code, stored procedures, reports, integrations, and workarounds.	What must be preserved, where reversible seams can be created, and how modernization should be sequenced.	3-5 weeks	$30,000	View scope
AI Evaluation & Release Gates	A measured baseline exists, but the team cannot reproduce evaluations, compare changes, calibrate reviewers, or block degraded releases consistently.	How quality, access, cost, latency, human review, and failure thresholds become an inspectable release process.	6-8 weeks	$60,000	View scope
Architecture & Reliability Office	An initial assessment or implementation has exposed recurring architecture and reliability decisions that cannot be handled as occasional ad hoc advice.	Which risks, releases, vendors, and modernization priorities deserve attention each month and quarter.	3-6 month initial term	$9,500-$12,500 per month	View scope
Joint Discovery Workshop	A partner needs to test technical fit, client interaction, workstream boundaries, and the smallest credible next step without requesting extensive free presales.	Whether a specialist workstream is viable, how it should be bounded, and what proposal-ready next scope is justified.	2 working days plus preparation and readout	$5,000	View scope

Capabilities

Six ways to reduce technical uncertainty

Capabilities describe work the practice can perform. Services package that work into a bounded commercial decision.

01Unplanned behavioral change, rewrite uncertainty, brittle cutovers, and dependency surprises.

Legacy system rescue and modernization

A long-lived Microsoft application still runs important work, but unsupported components, tightly coupled data logic, and undocumented dependencies make every change risky.

Work: Current-state application, data, integration, and dependency inventory
Artifact: Current-state architecture map
Decision: Stabilize, incrementally modernize, replace a bounded component, or defer with known risk.

Review capability

02Silent calculation, approval, permission, report, and exception-handling drift.

Business logic preservation and parity validation

Important behavior is distributed across code, stored procedures, reports, configuration, exception handling, and human workarounds - and no single source fully defines intent.

Work: Behavior discovery and source tracing
Artifact: Business-rule map
Decision: Which behavior must remain, which change is intentional, and whether a release is acceptably equivalent.

Review capability

03Coupling, ambiguous ownership, integration failure, inaccessible evidence, and vendor-driven architecture.

Application, API, integration, and data architecture

Teams cannot safely change or integrate systems because ownership, service boundaries, data flows, interface contracts, failure behavior, and vendor dependencies are unclear.

Work: Current-state and target-state mapping
Artifact: Architecture and data-flow diagrams
Decision: Where to place boundaries, which dependencies to retain, and how to integrate without widening operational risk.

Review capability

04Unmeasured failures, weak release decisions, access leakage, unsupported claims, and unreliable change comparison.

AI production readiness and evaluation

An AI pilot appears useful, but the team cannot explain representative performance, access failures, cost, latency, refusal behavior, human review, or release thresholds.

Work: Use-case and system inventory
Artifact: Evaluation dataset
Decision: Whether the workload should proceed, narrow, remediate, remain in pilot, or stop.

Review capability

05Unclear accountability, irreversible automation, hidden overrides, and unsupported downstream action.

Human-reviewed AI workflow design

AI output can influence consequential work without clear authority, named reviewers, refusal behavior, escalation, or separation between approval and execution.

Work: Proposed-versus-executed action separation
Artifact: Authority and review matrix
Decision: Which actions AI may propose, what a human must approve, and what the system must refuse or escalate.

Review capability

06Procurement stalls, inconsistent answers, unsupported claims, and evidence disconnected from the system.

Technical evidence and procurement support

Engineering reality is not organized into artifacts that leadership, security, procurement, legal, partners, or evaluators can inspect and reuse.

Work: System cards and architecture summaries
Artifact: Procurement-ready technical evidence package
Decision: Whether a buyer, partner, or reviewer has enough technical evidence to continue qualification, approve the next phase, or ask targeted questions.

Review capability

Long-Term Capability Framework

Make the next decision useful after the project ends.

The framework connects behavior preservation, durable evidence, explicit authority, feedback, and client-owned continuity.

Explore the framework Open the review worksheet

Preserve behavior that matters
Identify calculations, approvals, permissions, reports, exceptions, and human workarounds before replacing technology.
Make system state and evidence durable
Record architecture, data boundaries, models, prompts, configuration, representative tests, observed results, decisions, and limitations so work survives personnel change and can be reproduced.
Keep authority and consequence explicit
Separate AI proposals from approval and downstream execution. Name reviewers, escalation paths, overrides, refusals, and blocked actions.
Build feedback loops that improve people and systems
Use representative tests, domain review, disagreement records, operational feedback, and structured learning rather than optimizing only for frictionless completion.
Leave reusable capability and a client-owned handoff
Use exportable artifacts, reusable schemas, documented dependencies, clear ownership, and planned handoff instead of manufactured lock-in.

Featured evidence and resources

Read the classification before relying on the artifact

The public library mixes key-personnel background, method demonstrations, public R&D, and decision resources without presenting them as the same kind of proof.

Key-personnel past performancePublicly documented key-personnel background

Enterprise Microsoft systems delivery across rule-heavy domains

Mike Kappel has more than two decades of public professional history delivering and modernizing Microsoft-based enterprise systems across commercial and public-sector contexts.

Boundary: This is key-personnel experience, not LongTermCapabilities corporate government past performance. It does not identify confidential clients or claim unverified metrics.

Open evidence record

Method demonstrationPublic method demonstration

AI evaluation and blocked-action program

An AI reviewer, copilot, or agent produces useful demonstrations, but acceptable failure, human review, and prohibited downstream actions remain undefined.

Boundary: This is an evaluation pattern, not a formal audit, safety certification, or guarantee that a system will be error-free.

Open evidence record

Public R&D projectPublicly inspectable research artifacts

Governed handoff and machine-readable evidence research

Long-running software and AI-assisted work requires durable handoff records, source boundaries, review status, and public/private separation.

Boundary: Public R&D is not a client engagement, certification, or proof of suitability for a specific regulated environment.

Open evidence record

Decision guide

AI Production Readiness Evidence Pack

A structured evidence inventory for system boundaries, representative tests, human authority, failure taxonomy, release criteria, and unresolved risk.

Decision: Teams need a practical inventory of evidence before approving an AI production release or enterprise review.
Version: 1.52.0
Reviewed: 2026-07-24

Read as HTML Download PDF · 62 KB

Decision guide

Legacy Modernization Evidence Inventory

An inventory of application, database, integration, workflow, testing, operational, and ownership evidence for a safer modernization decision.

Decision: A team wants to estimate or modernize a legacy system without knowing which evidence must be collected first.
Version: 1.52.0
Reviewed: 2026-07-24

Read as HTML Download PDF · 62 KB

Browse the evidence library · Browse all resources

Insights

Research that supports real buyer decisions

Original, source-linked articles connect durable AI, modernization, human authority, evaluation, and organizational capability to the practice's working methods.

AI production readiness

Move AI from a useful demo to a defensible release decision

A polished demo shows possibility. A release decision requires representative evidence, ownership, failure boundaries, rollback conditions, and an honest path for unknowns.

Published: 2026-07-22
Reading time: 6 min
Decision: Decide whether an AI workload should proceed, narrow, remediate, remain in pilot, or stop.

Legacy modernization

Modernize .NET and SQL without losing the business

A modernization is safe only when the team can distinguish technology change from business-behavior change and prove what must remain.

Published: 2026-07-22
Reading time: 6 min
Decision: Decide how to sequence a Microsoft modernization without silently changing critical behavior.

Human-reviewed AI and governance

Human-reviewed AI requires a boundary between proposal, approval, and execution

"Human in the loop" is not a control until the system names who may decide, what they can approve, and how execution remains bounded.

Published: 2026-07-22
Reading time: 8 min
Decision: Decide which AI-assisted actions require named human authority and how that authority should be implemented.

Browse all insights

Trust preview

Visible boundaries before a questionnaire arrives

Static-first public site with local assets and local search
No external analytics, advertising pixels, hosted search, external fonts, or third-party lead platform
Consent-based first-party public-safe intake with email and phone fallback; sensitive evidence moves to an approved channel
AI output is proposed work and consequential action retains named human authority
Client-owned artifacts, exportable formats, and planned handoff

Review the Trust Center Accessibility statement

Machine intelligence research

Move from AI prototype to governed machine intelligence.

Source-linked guidance separates model behavior, agent control loops, multi-agent coordination, and swarm-style feedback so teams can choose the simplest viable architecture, preserve human authority, and build evidence for a real production decision.

Research pillar

Machine intelligence

Models, memory, tools, people, evidence, and operations as one decision system.

Read the research path

Research pillar

Agentic AI

A bounded control loop for planning, tool use, state, authorization, verification, and stopping.

Read the research path

Research pillar

Multi-agent systems

Role separation, coordination topology, communication contracts, team evaluation, and failure containment.

Read the research path

Research pillar

Swarm intelligence

Decentralized local rules, feedback, task allocation, emergence, and collective control.

Read the research path

Explore machine intelligence Review AI production readiness

Next action

Bring the system, the trigger, and what cannot fail.

The first message must remain public-safe. Sensitive evidence moves only after fit, responsibility, scope, terms, and an approved channel are established.

Discuss the system Review the capability statement

Prefer a direct conversation? Call 1 (464) 274-1476

Modernize critical Microsoft systems. Put AI into production—with evidence.

What decision is in front of you?

An AI workload is approaching production

A .NET or SQL system is too risky to change

A vendor, buyer, or security review is blocked

A government or prime workstream needs specialist depth

Five bounded engagements. One canonical commercial model.

Six ways to reduce technical uncertainty

Legacy system rescue and modernization

Business logic preservation and parity validation

Application, API, integration, and data architecture

AI production readiness and evaluation

Human-reviewed AI workflow design

Technical evidence and procurement support

Make the next decision useful after the project ends.

Read the classification before relying on the artifact

Research that supports real buyer decisions

Visible boundaries before a questionnaire arrives

Move from AI prototype to governed machine intelligence.

Bring the system, the trigger, and what cannot fail.

Move from terminology to a reviewable architecture decision.

Make agentic systems explainable before they become consequential.

Build traffic by publishing the evidence questions serious AI buyers already need answered.

Putting an AI agent into production?

Direct answers, named authorship, public data, and visible editorial standards.