Who This Is For
Typical Clients
Agencies50+ clients, recurring deliverables
SEO agencies managing many clients who need scalable reporting, consistent deliverables, and less manual work per account.
- 50+ clients, recurring reports
- Multi-source data (GSC, rankings, etc.)
- Client-ready outputs on a schedule
In-house & enterpriseComplex reporting, custom pipelines
SEO and content teams inside larger organizations with complex reporting, internal tools, and data pipelines.
- Complex reporting and dashboards
- Custom data pipelines and integrations
- Internal tools and workflows
By approachDiscovery, training, and handoff
Teams that care as much about how the work gets done as what gets built—discovery, requirements, and training included.
- Discovery and mapping, not just build
- Training and handoff so you own it
- Open to LLM/AI where it helps
You're likely a fit if…
You spend 10+ hrs/week on manual, repeatable tasks
Off-the-shelf tools don't match your process
You want the team to adopt and own the solution
You're open to LLM/AI where it adds value
The Operational Efficiency Challenge
Large SEO teams and agencies face operational challenges that limit growth and eat into profitability.
01
Manual Processes
Time-consuming tasks that could be automated, eating up hours your team could spend on strategy.
02
Data Integration Challenges
Multiple tools and datasets that don't talk to each other, creating data silos.
03
Reporting Inefficiencies
Hours spent creating reports instead of analyzing insights and making strategic decisions.
04
Scale Limitations
Current workflows break when managing hundreds of clients or properties.
05
Tool Limitations
Off-the-shelf solutions don't fit your specific processes and unique business needs.
06
Data Accuracy Issues
Human error and inconsistent processes affecting the quality of your decision-making.
How I work
The Hardest Part Isn't Building—It's Discovery & Adoption
Most workflow automation fails because requirements were guessed, the current process was never fully mapped, or the team wasn't involved until after the build. My core skill is not only building the solution, but discovering where the real pain points are, mapping how work gets done today, and working with your existing people so the solution fits and they can own it.
I run discovery sessions with your team, document the workflow as it actually works, collect and validate requirements with the people who will use the system, and work with them again during testing so they can adapt and understand. Training and handoff are a major part of the engagement—so the biggest challenge (getting your team to adopt and maintain the automation) is built into how I work.
Discovery-first process
- Discover — Sessions with your team to find pain points and map the current workflow
- Requirements — Collect and validate with the people who will use the system
- Design & build — Architecture and implementation (including LLM/AI where it helps)
- Test with your team — Iterate with real users so they can feedback and build confidence
- Train & handoff — Documentation and training so your team can maintain and extend it
Types of Automation I Build
From data pipelines and reporting to LLM and AI-powered analysis—these automations eliminate manual work, improve accuracy, and surface insights that used to require manual review.
Data Collection & Processing
- GSC API data pulls
- Rank tracking automation
- Crawl data processing
- Multi-source data aggregation
Reporting & Dashboards
- Automated client reports
- Real-time performance dashboards
- Custom KPI tracking
- Executive summaries
LLM & AI-Powered Automation
- AI summarization of reports and insights
- LLM-based content or data classification
- Natural language queries over SEO data
- Intelligent anomaly detection and recommendations
- Automated insight generation from large datasets
- AI-assisted audit and gap analysis
Monitoring & Alerts
- Technical SEO issue detection
- Traffic anomaly alerts
- Competitor monitoring
- Performance threshold warnings
Workflow Automation
- Task assignment systems
- QA process automation
- Client onboarding flows
- Data validation pipelines
Real-world examples
Example scenarios to help you contextualize what this kind of automation can look like.
Multi-client GSC reporting for an agency
Context. An SEO agency spends 15+ hours every week pulling GSC data for 80+ clients, copying into spreadsheets, and building reports by hand.
Outcome. A typical approach: discovery with the team to map how each report is actually used, then a pipeline that pulls from the GSC API, aggregates by client, and generates client-ready reports on a schedule. The team reviews and sends instead of building from scratch, with training so they can adjust dimensions and filters when clients ask for changes.
AI-powered weekly insights for an enterprise publisher
Context. A large publisher has millions of URLs and multiple data sources (crawl, rankings, traffic). SEO leads are drowning in raw exports and have no time to spot what changed or why it matters.
Outcome. One way to solve it: a system that ingests the key data, runs it through an LLM to summarize changes and highlight actionable insights (e.g. “Traffic dropped on X section; crawl budget shifted to Y”). The deliverable becomes a short weekly brief instead of spreadsheets, with discovery and training so the team can refine what “actionable” means for them.
Content brief → QA pipeline for an in-house SEO team
Context. An in-house team manages content briefs and QA in spreadsheets and docs. Handoffs are manual, checks are inconsistent, and nobody has a single view of status.
Outcome. An example approach: map the real workflow with the content and SEO leads, then build a pipeline that connects briefs to content and runs automated QA checks (readability, structure, internal links). The team tests in the tool before go-live and is trained to run it and interpret results so they own the process.
Frequently Asked Questions
Common questions about SEO workflow automation and implementation.
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