Results

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. Here is what that looks like in practice.

15+
Autonomous agents deployed
5+
Client operations running 24/7
0
Hours of manual monitoring required
100%
Systems running on Google Cloud
Multi-Location Services

Autonomous Campaign Operations for a Multi-Location Workspace Company

A multi-location workspace company operating across 5 locations in Texas

5
Locations managed autonomously
10+
Campaigns monitored 24/7
0
Hours per week of manual campaign monitoring
100%
Budget deployment rate — no dollar left unspent
4
AI search engines monitored for brand visibility
28
Probe queries tracked across all locations

The Challenge

Managing Google Ads campaigns across 5 locations with different budget allocations, campaign types (Search and Performance Max), and performance targets. Each location required independent budget optimization while maintaining strict spending discipline — every dollar allocated had to be deployed, and budgets could never shift between locations. Manual monitoring consumed hours per week and performance shifts went undetected for days.

What We Built

  • Deployed autonomous Google Ads management agents across all 5 locations with hard-coded guardrails enforcing budget rules per location
  • Built real-time anomaly detection that flags performance shifts before they impact spend efficiency
  • Implemented zero-sum budget reallocation within each location — automatically shifting spend toward whichever campaign type is converting better
  • Created a paid-organic bridge connecting ad performance data to organic content strategy, so high-converting paid queries become organic content targets
  • Deployed a 7-agent AEO system monitoring visibility across ChatGPT, Perplexity, Google AI Overviews, and Gemini with 28 probe queries across all locations

Systems Deployed

01Autonomous Google Ads management agent with execution capability
02Location-level budget guardrails (code-enforced, not prompt-level)
03Real-time anomaly detection and performance alerting
047-agent AEO monitoring system
05Paid-organic bridge connecting acquisition data to content strategy
06Automated reporting and intelligence briefings

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.

Operations Lead

Stack:Google Cloud, Vertex AI Agent Engine, ADK, BigQuery, Gemini
Professional Services

Autonomous Content Engine for a Professional Services Firm

A professional services firm building thought leadership across two brands

Daily
Content published automatically
2
Websites receiving fresh content every day
0
Hours per week spent writing content
30+
Articles published in the first month
2
Social platforms with daily draft posts
100%
Publishing consistency — no missed days

The Challenge

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. Social media presence was inconsistent. The firm needed daily, high-quality content without adding headcount or diverting the founder from revenue-generating work.

What We Built

  • 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
  • Creates LinkedIn and X social post drafts for review and publishing
  • Sends daily email notifications with published content summary

Systems Deployed

01AI-powered topic selection from content pillar framework
02Long-form article generation with brand voice consistency
03Automated hero image generation
04Direct-to-repository publishing (auto-deploys via Vercel)
05Social media draft generation (LinkedIn + X)
06Email notification system via Resend

We went from publishing once or twice a month — when we could find the time — to having fresh, relevant content every single day. Our LinkedIn presence completely transformed.

Founder

Stack:Google Cloud, Gemini, GitHub Actions, Vercel, Resend
Marketing Agency

Autonomous Google Ads Management for a Digital Marketing Agency

A digital marketing agency managing Google Ads for multiple clients across different verticals

3
Client accounts managed by a single agent
$600+
Daily ad spend managed autonomously
0
Manual optimization hours per week
24/7
Monitoring — no performance shift goes undetected
100%
Audit trail coverage — every change logged with rollback
<25%
Maximum bid/budget change per action (guardrail-enforced)

The Challenge

The agency managed Google Ads campaigns for 3 clients across Demand Gen, Performance Max, Search, and Video campaign types, totaling $600+ per day in ad spend. Manual optimization was consuming the agency owner's time — checking performance, adjusting bids, monitoring budgets, adding negative keywords. With each new client, the workload scaled linearly. The agency needed to automate execution while maintaining tight guardrails on what the AI could change.

What We Built

  • Built a multi-client autonomous Google Ads agent that monitors, analyzes, and executes optimizations across all accounts from a single system
  • Implemented per-client guardrails stored in Firestore — target CPA floors and ceilings, maximum change percentages, cooldown periods between actions, and daily execution limits
  • Every optimization follows a strict protocol: validate guardrails, read current state, execute change, log full audit trail with before/after values and rollback capability
  • Agent handles tCPA adjustments, budget reallocation, and negative keyword management autonomously within defined limits
  • Anything outside guardrail boundaries gets escalated to a human — the system knows what it can and cannot do

Systems Deployed

01Multi-client autonomous Google Ads agent
02Per-client guardrail system (Firestore-backed, code-enforced)
03Automated tCPA optimization, budget management, negative keyword management
04Full audit trail with before/after state and rollback capability
05Secret Manager integration for per-client API credentials
06Cloud Scheduler for daily autonomous monitoring cycles

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.

Agency Owner

Stack:Google Cloud, Vertex AI Agent Engine, ADK, Firestore, Secret Manager, BigQuery

Your operations could run like this.

Every case study above started with a 30-minute conversation. We mapped the bottlenecks, designed the architecture, and deployed systems that run autonomously.

30 minutes. No pitch deck. We'll show you what's possible for your firm.