What I Built
Control Panel is a multi-tenant enterprise SaaS platform for field service and trade industries. It covers the entire business lifecycle — from first customer contact through invoicing, payroll, compliance, and client self-service. I designed and built the entire platform myself, drawing on 25+ years of operational experience running businesses as a COO. Every feature exists because I’ve seen the problem firsthand.
This isn’t a side project. It’s a production-grade platform with 4,800+ data models, 6,300+ enums, 22 business domains, full multi-tenant isolation, and enterprise security — built by one engineer who understands both the code and the business it serves.
Product Portfolio
Control Panel is a unified platform, but each module below operates at the scale and complexity of a standalone SaaS product — with its own domain logic, data models, and workflows. Building them independently would be impressive. Building them to interoperate seamlessly across a shared multi-tenant architecture is the actual engineering challenge.
Full lead-to-close pipeline with account management, opportunity tracking, contact relationships, activity logging, and automated follow-up workflows. Multi-stage deal management with probability-weighted forecasting.
Real-time dispatch board with drag-and-drop scheduling, technician availability tracking, route optimization awareness, skill-based assignment, and automated conflict detection. Integrated with work orders, service tickets, and fleet tracking.
Double-entry general ledger with chart of accounts, journal entries, financial period management, bank reconciliation, treasury operations, and multi-entity consolidation. Not a QuickBooks wrapper — a real accounting system built from first principles.
Interactive canvas-based design environment with 154 industry symbols, 18 engineering calculators, 14 renderers, drag-and-drop placement, layer management, and code-compliant output generation. Think Figma meets industry-specific CAD.
Full course authoring, enrollment management, quiz engine with multiple question types, progress tracking, certification management, and immersive 3D/WebXR training simulations with adaptive difficulty and AI-generated scenarios.
Quote-to-invoice pipeline with contract management, progress billing, change order tracking, payment processing, credit memos, revenue recognition, and automated collections workflows. Integrated with the native accounting engine.
Team management, time entry approval chains, pay run processing, garnishment handling, NACHA direct deposit file generation, benefits administration, certification tracking, and time-off management with accrual policies.
Vehicle lifecycle management with telematics event processing, preventive maintenance scheduling, fuel tracking, GPS-based analytics, driver safety scoring, DOT compliance tracking, and integration with dispatch for real-time fleet visibility.
25 compliance inspection templates built against real industry code standards. Configurable form engine, deficiency tracking, photo documentation, digital signatures, jurisdiction-aware submission workflows, and automated report generation.
Four distinct portal applications — client, vendor, lender, and employee — each with role-specific dashboards, document access, communication channels, approval workflows, and real-time status tracking. White-labeled per tenant.
AI & Intelligence Layer
AI isn’t bolted on — it’s woven into the platform’s architecture with production-grade safety controls, cost management, and audit trails. This is the area this team is specifically interested in, and it represents some of the most challenging engineering in the platform.
Multi-step AI agents that execute complex business workflows autonomously — proposal generation, document classification, meeting intelligence, design review. Each agent operates within defined boundaries with human-in-the-loop checkpoints for critical decisions.
A “second brain” built on retrieval-augmented generation. Documents are chunked, embedded, and indexed into a searchable knowledge base. The AI field assistant draws on company-specific knowledge — SOPs, past inspections, product specs — to provide contextually accurate answers.
Every AI call is wrapped in a governance layer: model routing for cost optimization, token budget enforcement, content filtering, PII redaction before prompts leave the system, full audit logging of every AI interaction, and circuit breakers for runaway costs. Built for enterprise trust.
Why this matters: Most teams bolt AI onto existing products as a feature checkbox. This platform was built with AI as a first-class architectural concern — with the safety rails, cost controls, and audit infrastructure that enterprise customers require before they’ll trust AI with their business data.
Architecture & Tech Stack
Modern full-stack architecture with TypeScript end-to-end, designed for multi-tenant isolation from day one. Every architectural decision was made for a reason — not because it’s trendy, but because it solves a real problem at this scale.
Multi-Tenant Architecture
Every tenant query passes through three independent isolation layers — application middleware, database row-level security, and transport-level JWT verification. No single-point-of-failure in tenant isolation. A compromised layer doesn’t expose cross-tenant data because the other two layers enforce independently.
Languages
any castsEngineering Discipline
Scale without discipline is just a large mess. The codebase enforces quality through automated gates, zero-tolerance policies, and infrastructure that makes it harder to ship bad code than good code.
as any, zero @ts-ignore, zero console.log across the entire codebase. TypeScript strict mode enforced everywhere.The philosophy: Every automated gate exists because a class of failure made it past manual review. When a bug gets through, the fix isn’t just a patch — it’s a permanent automated check that ensures the same class of failure never recurs.
Security Posture
Enterprise-grade security built for multi-tenant SaaS where cross-tenant data leakage is an extinction event. Not checkbox security — defense in depth with independent verification at every layer.
Application middleware, database RLS, and JWT verification enforce tenant boundaries independently. Any single layer can fail and data still doesn’t leak.
AES-256-GCM for PII at rest. JWT with 5-minute rotation. SSO/SAML for enterprise identity federation. MFA via SMS + WebAuthn/FIDO2 passwordless. bcrypt password hashing. Rate limiting on all auth endpoints.
44 automated control points. Full request context on every audit entry — IP, user agent, action, target entity. Separation of duties on financial approvals.
Formal gray-box pentest specification covering 8 phases with documented threat model. Automated static analysis runner covering auth, tenant isolation, and input validation.
Origin/host verification on all mutations. Content Security Policy with nonce-based style-src. File upload scanning with extension, MIME, and content validation.
Incident response plan, responsible disclosure policy, breach notification matrix, cybersecurity audit program, and vendor risk management — all documented and version-controlled.
The depth of this platform is truly remarkable. What typically requires a team of 8-10 engineers over 2-3 years has been architected and built by one person with genuine domain expertise. The multi-tenant isolation, the AI governance layer, the sheer breadth of business logic — this is not a demo or a prototype. This is full production-grade enterprise software.
Independent Technical Assessment — Architecture & Security Review, 2026 (from a Fortune 500 Company IT Data Engineering Director)
Case Study: Multi-Domain Architecture at Scale
A look at how deliberate architectural decisions handle the kind of scaling challenges that break most projects.
At 1,400+ models across 22 business domains, a single database client becomes a compile-time and runtime bottleneck. Rather than waiting for the system to break, I designed a domain-split architecture: 10 bounded-context database clients, each generated against a schema subset, sharing a unified type interface and a common tenant isolation layer.
Each domain client (CRM, Operations, Financial, Accounting, HR, Design, LMS, Integrations, Trades) connects to the same PostgreSQL database but is generated against only its relevant models. A unified type interface lets all 1,200+ server action files work against any domain client interchangeably. Tenant isolation is applied uniformly across all 10 clients via a single wrapper function — one security implementation, ten clients, zero gaps.
The architecture compiles cleanly, enforces tenant isolation uniformly, and scales to thousands of models without degrading developer experience or runtime performance. Automated gates prevent regression, and the type system catches cross-domain errors at compile time rather than in production.
Greg Collins
AI Strategist & Architect
I’m exploring opportunities to bring this level of engineering discipline, operational experience, and AI product thinking to the right team.
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