Research before drafting
A research agent combines keyword demand, search results, competitor coverage and the website's existing content. It prepares a brief for the writer rather than treating a search snippet as a verified fact.
WAIM product build
AI BLOG
AUTOMATION.
BUILT BY WAIM.
WAIM built a content platform that connects topic research, source evidence, article production, WordPress publishing and performance measurement. This is our own product build: a practical example of orchestrated AI agents working inside one controlled business process.
Content workflowWAIM / BUILD
01
ResearchDemand, sources and business context
02
Draft & checkEvidence carried into the article
03
PublishWordPress and recorded status
04
MeasureSearch Console and GA4, when connected
- Project
- WAIM's own content automation platform
- Scope
- Research, drafting, publishing and measurement
- Integrations
- WordPress, Google Search Console and Google Analytics 4
- Evidence presented
- Implemented workflow and controls
The challenge
The problem: content work was more than writing
A useful article depends on several decisions: what people search for, whether a topic fits the business, which sources support it, where it belongs on the website and what happens after publication. Automating only the writing step leaves those decisions disconnected.
We designed one workflow to carry the business context and research into the draft, keep publishing accountable and return performance data to the planning process. Supporting separate websites also required boundaries around their credentials, content and data.
The system
What we built
Evidence with context
A dedicated academic research agent can discover relevant papers and prepare supporting passages. It retains source details, reading scope and limitations. A paper title or abstract is not treated as proof that a claim applies to every business.
Connected publishing
The workflow produces articles and sends them to the configured WordPress site. It records publication status and supports recovery after interrupted requests, helping operators reconcile an uncertain result before retrying.
Measurement after publication
Google Search Console and GA4 integrations bring search and engagement data back into the system when access is configured. The platform can track article outcomes over defined windows and keeps missing measurements separate from measured zero results.
The controls
Controls around the automation
Each website has its own business context, publishing destination and data scope. Routine content updates are limited to posts managed by the platform.
Teams can influence topics, edit proposed outlines and exclude facts where those options are enabled. Review settings vary by workflow; automated checks do not replace editorial accountability.
Research and generation have budget limits. Matching, reusable results can be cached, while freshness checks and publication permissions remain separate. Errors and missing measurements are surfaced for review.
The evidence
What this project demonstrates
The delivered capability is a connected workflow with specialized agents, CMS integration, recoverable publication records and measurement controls. It demonstrates how WAIM approaches custom automation: define the process, connect the necessary systems and make exceptions visible.
This case does not claim a measured percentage increase in traffic, revenue or staff productivity. Those outcomes need a defined baseline, a comparable measurement period and attributable results. AI research and review can still make mistakes; publication volume alone does not establish business value.
Your next move
Bring us the process you want to improve
Tell us about the work, the systems involved and the outcome that matters. We can define a starting scope and how to judge whether it works.
Discuss your automation project