Systems in production.
Outcomes you can measure.
> Every engagement follows the Hendricks Method: study the operations, build the agents, deploy the system, manage it in production.
> Below is what that looks like in practice.
Autonomous campaign operations across five locations
Google Ads campaigns across 5 locations, different budget allocations, Search + Performance Max. Each location required independent budget discipline — every dollar deployed, no spend ever crossing location boundaries. Manual monitoring consumed hours per week. Performance shifts went undetected for days.
- >Deployed autonomous Google Ads management agents across all 5 locations with hard-coded guardrails enforcing per-location budget rules
- >Real-time anomaly detection that flags performance shifts before they impact spend efficiency
- >Zero-sum budget reallocation within each location — shifting spend toward whichever campaign type converts better
- >Paid-organic bridge: high-converting paid queries become organic content targets automatically
- >Automated weekly intelligence briefings covering spend, conversions, and anomalies across all locations
“We went from spending hours every week watching dashboards to having a system that watches everything for us and only surfaces what actually needs a human decision.”
Google Cloud · Vertex AI Agent Engine · ADK · BigQuery · Gemini
Multi-client autonomous Google Ads management
3 clients, Demand Gen + Performance Max + Search + Video, $5K+ per day in ad spend. The agency owner was spending 10+ hours per week checking performance, adjusting bids, monitoring budgets, adding negative keywords. Every new client scaled the workload linearly. Needed automation with tight guardrails.
- >Built a multi-client autonomous Google Ads agent monitoring, analyzing, and executing optimizations across all accounts from a single system
- >Per-client guardrails stored in Firestore: tCPA floors and ceilings, maximum change percentages, cooldown periods, daily execution limits
- >Every optimization follows a strict protocol: validate guardrails → read state → execute → log full audit trail with before/after and rollback
- >Agent handles tCPA adjustments, budget reallocation, and negative keyword management within defined limits
- >Anything outside guardrail boundaries escalates to a human — the system knows what it can and cannot do
“I was spending 10 hours a week manually checking dashboards and tweaking bids. Now the agent handles it and I only get involved when something genuinely needs a strategic decision.”
Google Cloud · Vertex AI Agent Engine · ADK · Firestore · Secret Manager · BigQuery
Autonomous content engine for thought leadership
Maintaining consistent thought leadership content across two separate websites while the founding team focused on client work. Content creation was sporadic — weeks would pass between publications. The firm needed daily, high-quality content without adding headcount or diverting the founder from revenue-generating work.
- >Built an autonomous content engine that generates, publishes, and promotes articles daily — with zero manual intervention
- >AI selects topics from defined content pillars aligned with the firm's positioning and audience
- >Generates long-form articles with original hero images
- >Auto-publishes to both websites via direct repository integration (Vercel auto-deploys on push)
- >Creates LinkedIn and X social post drafts for review
- >Sends daily email briefing summarizing what shipped
“We went from publishing once or twice a month — when we could find the time — to fresh, relevant content every single day.”
Google Cloud · Gemini · GitHub Actions · Vercel · Resend
Which deployment is closest to yours?
> Ask Axiom on the home page. The agent will map the closest deployment to your operational shape and show you what an equivalent system would look like for your firm.