A multi-agent marketing engine, not a SaaS product
MarCommand is currently positioned as a demonstration: the engine that runs Preisser Solutions' own marketing operations and serves as a reference architecture for custom client builds. It is not productized SaaS — you can't sign up for it. But the architecture is portable, and similar engines have been delivered as custom client builds (the Cassidy HVAC marketing engine is one example).
The framing matters: MarCommand is proof that multi-agent marketing systems work, that a single operator can run marketing operations end-to-end with the right AI infrastructure, and that the technology is ready for serious custom builds in 2026.
Specialized agents orchestrated by a central coordinator
MarCommand is built around the multi-agent pattern: a central orchestrator agent coordinates multiple specialized worker agents, each responsible for one slice of marketing operations:
- Content Strategist agent — keyword research, content calendar planning, brand-voice consistency, content gap analysis
- Copywriter agent — long-form content generation, AEO answer capsule engineering, FAQ generation
- Designer agent — image generation (custom marketing visuals using persuasive-psychology frameworks), brand-system enforcement
- Paid Ads Manager agent — campaign architecture, audience definition, budget pacing recommendations, creative briefing
- AEO Architect agent — engineered first paragraphs, FAQ schema, named-entity placement, comparison-table fairness audits
- Analyst agent — performance reporting, attribution, anomaly detection, next-action recommendations
- Quality Reviewer agent — final pass on every artifact for brand voice, factual accuracy, and policy compliance
- Central Orchestrator — assigns work, manages dependencies, handles errors, routes human-approval checkpoints
The end-to-end workflow
A typical MarCommand cycle: the Content Strategist agent identifies a content gap (e.g., 'we have no comparison page for X vs Y'); the orchestrator routes the brief to the Copywriter agent for draft generation, the AEO Architect agent for answer-capsule engineering, the Designer agent for visual production, and the Quality Reviewer for final approval; a human checkpoint approves or revises; the orchestrator publishes the artifact, schedules paid promotion, and the Analyst agent tracks results.
The system runs daily without daily human input — content gets produced, ads get managed, reactivation runs continue, and reporting surfaces in dashboards. Human input is reserved for strategic decisions, approval checkpoints, and exception handling.
What MarCommand is built on
Next.js + React + TypeScript front-end (orchestrator dashboard, agent status, approval queue), custom Node.js back-end, PostgreSQL for state, Cloudflare Workers for agent execution. Agent reasoning runs primarily on Claude (Anthropic) via the Claude API, with GPT-4 (OpenAI) used for specific specialized tasks. Image generation via current best-in-class image models. Each agent is a TypeScript module with its own system prompt, tool calls, and quality gates.
What MarCommand demonstrates
Three things MarCommand is designed to prove:
- Custom multi-agent infrastructure is now feasible at SMB economics — what was impractical in 2023 is shippable in 2026
- A single operator with the right AI infrastructure can run marketing operations at agency-level output (Tyler runs Preisser Solutions' marketing through MarCommand)
- The architecture is portable to client builds — every component of MarCommand can be specialized and delivered as a custom build for a specific operator
What MarCommand produces
MarCommand currently runs Preisser Solutions' marketing operations including:
- Daily content production across the AEO page graph (~180+ pages and growing)
- Programmatic paid-ads management with transparent attribution
- Customer-relationship outreach to leads and clients
- Quarterly AEO citation tracking across ChatGPT, Perplexity, Gemini, Claude
- End-to-end analytics — pipeline, conversions, content performance, attribution
- Reference architecture used to scope and deliver client builds like the Cassidy HVAC marketing engine
Screens (placeholder — to be added)
MarCommand orchestrator dashboard and agent status screens are being prepared for the demo. Available during scoping calls.
Lessons for operators considering custom AI marketing infrastructure
Custom multi-agent infrastructure has crossed the feasibility threshold for serious operators. The framing in 2026 is no longer 'should we use AI?' — it's 'which work should each agent handle, where do humans stay in the loop, and how do we ship it.' MarCommand exists to demonstrate the answer.