01
Map content before you launch
Coverage beats features at rollout. This tells you how many pages and docs to index before turning the assistant on.
Support Coverage Planner
Plan how much documentation, manual Q&A, and chatbot segmentation you need before launching AI support.
- Free to use
- No signup required
- Instant results
Coverage Recommendation
Indexed docs target
147
Aim to keep this much grounded content ready before rollout.
Suggested chatbots
2
Split support coverage when product areas diverge.
Manual Q&A seed
30
Seed repetitive questions before indexing catches up.
Rollout note
A lean launch is viable if manual Q&A covers the early gaps.
Use this as a planning baseline, then validate coverage quality in the ops dashboard once the assistant is live.
Intent
Readiness
Capture teams searching for rollout planning, chatbot setup, or knowledge coverage guidance.
Output
Coverage targets
The tool turns scope into document, Q&A, and segmentation targets.
Next move
Pilot setup
Best handoff is into launch planning, bot creation, and source syncing.
How it works
Know what you need before you launch.
Most chatbot rollouts fail on missing content, not missing features. Enter your product scope — you'll get concrete targets for pages to index, manual Q&A to write, and how many separate bots to run.
02
Split bots when scope diverges
When product areas, audiences, or doc sets stop looking alike, one bot gets noisy. You'll see when to split.
03
Catch the gaps early
Manual Q&A targets flag the questions your docs miss — so users don't find those gaps for you in production.
FAQs
Common questions
Why plan support coverage before launching an AI assistant?
Because rollout quality is mostly determined by source coverage, manual Q&A depth, and clear escalation rules. Launching early without those pieces usually creates mistrust faster than adoption.
When should one chatbot become multiple chatbots?
Split coverage when product areas, audiences, or content domains start to diverge enough that one prompt and one source mix create noisy answers. The planner uses product-area count as a practical proxy for that decision.
What should happen after this planner?
Use the result to prioritize source syncing, manual Q&A creation, and widget pilot scope. Then validate unresolved conversations and answer quality once the assistant is live.
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Next step
Turn useful results into a live assistant.
Create a workspace, index your content, and test real conversations against your support knowledge.
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