SleekView Kanban for AI Engine
SleekView Kanban groups AI Engine conversation logs, content generation jobs, and moderation queues into status lanes inside WordPress, so support, content, and trust teams can drag cards across queued, reviewing, approved, and rejected without ever leaving the admin.
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Why AI Engine queues need a board
AI Engine writes a lot to the WordPress database. Chatbot sessions live in wp_meow_aie_logs with a status column, a session identifier, a model name, and the full conversation payload. Bulk content generation jobs sit in a separate wp_meow_aie_tasks table with their own task_status, target post id, and prompt template. The default admin lists each in a flat table, which makes it almost impossible to triage the next high-priority item when the queue gets busy.
SleekView Kanban reads either table directly and groups rows by their status column, which is the obvious pipeline field for both data sets. Conversation logs become a kanban grouped by status, with cards that show the session topic, model, and message count. Content jobs become a separate board grouped by task_status, with cards showing the target post title, prompt template, and elapsed generation time. Both boards live inside the same WordPress install and reuse the same SleekView configuration.
Dragging a card writes the new status back to the matching AI Engine table, so the plugin's own statistics screens stay accurate, retry logic still fires for any task that moves back to queued, and webhook integrations see the updated state on their next poll. Conversations marked as escalated drop into a separate lane, and content jobs that fail their guard rails sit in a clearly colored rejected column instead of getting lost.
Workflow
Connect AI Engine to SleekView in four steps
Pick AI Engine in the data picker
Group by status or task_status
Pick card fields per board
Enable drag-and-drop writes
Sample board
Sample AI Engine content jobs board
Comparison
Default AI Engine list vs SleekView Kanban
Default AI Engine logs list
- Logs and tasks live in separate flat tables that mirror WordPress admin styling
- Status changes require opening a row and changing a dropdown one at a time
- No way to see queue depth per status without exporting to a spreadsheet first
- Triage of escalated chatbot sessions involves manual filtering on each visit
- Bulk actions stop at delete, with no way to move many rows across statuses at once
SleekView Kanban
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Groups AI Engine logs or tasks by
statusortask_status - Drag a card to a new lane to write status back to the AI Engine table
- Card fronts surface session topic, model, prompt template, and elapsed time
- Separate boards for chatbot triage and bulk content jobs from one config
- Capability-aware drops keep junior staff out of the approved and rejected lanes
Features
What SleekView Kanban gives you for AI Engine
Chatbot triage board
Group AI Engine chatbot logs by status to triage real customer conversations. Cards show the session topic, model, message count, and last-message timestamp, so support leads can pick up escalated sessions before they age into complaints without scrolling through approved chats.
Content jobs board
A second board groups AI Engine bulk content tasks by task_status, with card fronts that show the target post title, prompt template, and the elapsed generation time. Editors drag drafts from reviewing to approved and SleekView writes the new status back to the same row.
Capability scoped drag and drop
Every drop is checked against the current user's WordPress capabilities, so junior writers can move cards inside the draft and review lanes while only editors with publish_posts can promote a card into the approved column or reject one back to queued.
Audience
Where AI Engine teams use the kanban
Support and trust triage
Trust and support leads use the chatbot logs board to triage flagged sessions, escalate hard cases, and approve auto-resolved conversations. The escalated lane becomes the team's working queue rather than a hidden filter in the default list.
Editorial review of AI drafts
Editors use the content tasks board to pick up drafts, send weak ones back to queued for regeneration, and approve the rest. Filtering by prompt template lets a specialist editor review only the listicles or only the buying guides at a time.
Marketing teams running campaigns
Marketing teams running themed AI Engine campaigns use filtered boards per campaign to track how many landing pages, blog posts, or chatbot intents are queued versus shipped, without leaning on screenshots from the AI Engine admin.
The bigger picture
Why a board belongs on top of AI Engine
AI Engine is two products in one. It is a chatbot platform that produces conversation logs, and it is a content generator that produces tasks. Both involve queues.
Both involve human judgment after the model finishes. A flat list per queue is fine for one editor working alone, but it stops scaling the moment more than one human has to coordinate. The default screens force every team member to remember which filter to apply and which sort to use, and important rows hide in plain sight while approved ones take up the most space at the top.
A kanban swaps that on its head. The lane that needs attention always sits where the team agreed it should sit, the card count makes queue depth obvious, and dragging takes the place of opening a row, finding a dropdown, and clicking save. Same data, different shape, dramatically faster team.
Questions
Common questions about SleekView Kanban for AI Engine
By default each board points at one AI Engine table at a time, which keeps the data model clean and the columns meaningful. You can build one board for chatbot triage and a second board for content tasks, and pin both to the same WordPress dashboard so each team finds its own view in one click.
SleekView reads any column AI Engine writes, including pro fields like guard rail outcomes, safety scores, and content quality grades. You can show those values on the card front, sort lanes by score, or filter a lane to only show cards above a chosen threshold without modifying any AI Engine source code.
Not by default. The drop only updates the status field. If you want a side effect, hook into the SleekView write event and trigger AI Engine's own response API or a custom webhook. Keeping the two steps separate prevents accidental sends and lets you preview transcripts before any message goes out.
Every column AI Engine stores is filterable. You can narrow a board to logs that used a specific model, to tasks whose prompt template matches a string, or to rows where the elapsed generation time exceeded a threshold, and save the filter as a per-user board preset for daily use.
SleekView is language agnostic. It displays whatever values AI Engine stores in the language column, surfaces them as card metadata, and supports filtering by language. A multilingual content team can keep separate lanes per language or one mixed board with language tags on the card front.
AI Engine's statistics dashboards read the same status fields SleekView writes to, so any move on the board immediately updates the official counts. There is no separate analytics path to keep in sync, and any third party reporting tool that reads AI Engine data sees the updated rows on its next poll.
Yes. SleekView includes a CSV export that respects the current filters and lane assignments, so you can hand a stakeholder a snapshot of the queue. You can also share a read-only public link that hides drag and drop and only exposes the lane counts and card titles.
SleekView only cares about the AI Engine database tables, which are identical between the free and pro builds. The kanban behaves the same way regardless of license, so you can start on the free tier, prove the workflow, and upgrade AI Engine separately if you need its pro generation features.
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