We Don't Consult.
We Deploy.
MatrixLabX is the world's leading Autonomous AI Agentic Consulting Firm — shifting mid-market enterprises ($20M–$500M ARR) from Software as a Service to Labor as a Service via PrescientIQ™ autonomous agents powered by Anthropic Claude and Google Vertex AI.
MatrixLabX is an autonomous AI agentic consulting firm founded in 2024 that deploys pre-trained, vertical-specific digital labor for mid-market enterprises. Rather than selling software seats, it deploys autonomous agents that execute marketing, sales, and operational workflows without human supervision — a model it calls Labor as a Service (LaaS). Agents run on the PrescientIQ™ platform, powered by Anthropic Claude and Google Vertex AI, and deliver +82% pipeline velocity and −47% CAC within 90 days at a 99.8% uptime SLA.
Replace human-operated tooling with autonomous agents that execute, adapt, and compound.
Every MatrixLabX engagement is measured against a pre-deployment baseline. Revenue-attributed, auditable, replicable results — not consulting retainers.
A world where every mid-market enterprise operates a fully autonomous digital workforce.
Not copilots. Not dashboards. Autonomous agents that sense signals, make decisions, and execute actions — 24/7/365 without human prompting.
How We Operate
Outcomes Over Outputs
We charge for P&L impact — not hours, seats, or dashboards. Every engagement has a measurable baseline.
Radical Transparency
Glass-box AI. Every autonomous action is logged, auditable, and mapped back to the data signal that triggered it.
Deploy in Days
No 6-month retainers. No consulting theater. Agents go live fast and P&L impact is measurable within 60 days.
Outcomes, Not Optics
Every figure below is measured against a pre-deployment baseline across production deployments.
Pipeline velocity within 90 days
CAC on Revenue Accelerator
Agent uptime SLA
Higher goal completion vs. copilots
Built by Operators
George Schildge
George founded MatrixLabX in 2024 to close the gap between AI hype and enterprise P&L impact. His thesis is simple: mid-market enterprises don't need another dashboard — they need autonomous digital labor that executes. Under his direction, MatrixLabX built PrescientIQ™ to run the full Sense → Decide → Act → Learn loop in production, shifting clients from Software as a Service to Labor as a Service. Reach him at george@matrixlabx.com.
Frequently Asked Questions
What does MatrixLabX do?
MatrixLabX deploys pre-trained, vertical-specific autonomous AI agents that execute marketing, sales, and operational workflows for mid-market enterprises ($20M–$500M ARR) without human supervision — a model called Labor as a Service. Agents run on PrescientIQ™, powered by Anthropic Claude and Google Vertex AI, delivering +82% pipeline velocity and −47% CAC within 90 days at 99.8% uptime.
How is MatrixLabX different from a traditional AI consulting firm?
Traditional firms sell hours and deliver slide decks; MatrixLabX deploys agents and charges for outcomes. No 6-month retainers — agents connect to your CRM and ERP and go live in days, with P&L impact measurable within 60 days and 4× higher goal completion than AI copilot tools. Copilots wait for prompts; our agents detect signals, decide, and execute independently.
What is Labor as a Service (LaaS)?
Instead of buying software your team operates (SaaS), you deploy autonomous agents that do the work themselves. You pay for workflows executed and outcomes delivered, not hours or seats. Each agent runs the Sense → Decide → Act → Learn loop continuously — sensing signals, deciding actions, executing across your systems, and learning from results, 24/7 without human handoffs.
Is MatrixLabX secure and compliant?
Yes. MatrixLabX is SOC 2 Type II certified, with every deployment GDPR, HIPAA, FINRA, PCI-DSS, CCPA, and ISO 27001 ready. The platform is glass-box: every agent action is logged, auditable, and mapped to the data signal that triggered it. Agents run on Google Vertex AI with Anthropic Claude under a zero-trust architecture, so proprietary data is never exposed to model training.