From “who needs this?” to a decision you can explain.
We’re building a connected workspace for preparation, conversation records, AI-assisted analysis and decisions. It will bring the method’s prompts and skills together, so your team can follow the process and keep the reasoning in one place. People review the analysis and make the decisions.
Make your assumptions visible, so customers can change them.
Who?Customer profile
What matters?Need Bundles
Could this help?Value scenes
Open the project sketch+
Initial customer group
Maya and dog owners with regular office days who cannot get home at lunch.
Need Bundle / Care during the working day
Starting job assumption: “I need someone to take Milo out when my neighbour can’t.” Explore the reasons before filling in the pain and gain.
Three offering ideas
Short visit · Long walk · Backup care
Pricing models to explore
Pay per visit or reserve regular care.
Each offering gets its own rough value scene. These are hypotheses for customers to correct.
Converse / Keep the customer’s words
Make room for the correction.
First check the need. Then show a relevant offering. Keep both corrections.
Need
Only when help is unavailable. Without owing a favour.
→
Offering
A paid visit? “Who gets my key?”
Hear the customer correction+
P01 / Fictional conversation record
“She almost always says yes. But she rearranges her day, and I feel awful asking again. Sometimes I pay for daycare even though she’s available. I’d rather pay someone than keep owing a favour.”
Need Bundle correction
When my neighbour can’t. Without having to ask another favour.
Offering objection
“Paying would make asking easier. But who gets my key? I wouldn’t hand Milo over to a different stranger every week.”
Revision to discuss
Maya meets the carer first and can book that same person again. Explore their response to this version and the reason behind it.
AI can help organize the record. People check the words, context and interpretation before analysis. Preserve responses to both versions.
Analyze / Three Inquiries, in order
The grouping changes what the feedback means.
Compare customers, then needs, then offerings. Keep the evidence close.
Explore the three Inquiries+
Are we grouping the right customers?
Compare owners who feel burdened by asking for help with those who share care comfortably. Availability alone may hide that difference.
What do they actually need?
Explore guilt, negotiation and obligation alongside daytime care. Paid help could matter even when a neighbour is available.
How do the offerings meet those needs?
Read paid visits, familiar carers and emergency backup against the corrected needs. Keep trust concerns and counterexamples beside supporting reasons.
The AI proposes. Your team checks.
Review differences and contradictions, not just recurring words. Check how much credible material supports each claim.
Recommend / A Decision Brief to discuss
Show the call. Let people inspect the why.
See what to pursue, what to change and why. People make the decision.
Inspect the recommendation+
Fictional decision slate
Three offerings. Different recommendations.
Explore a familiar carerMeet first, then rebook. Investigate trust and the burden of favours.
Park long walksThe illustrated accounts give no reason to add exercise.
Revise paid short visitsPaying alone does not establish trust. Emergency-only cover misses the recurring burden.
Why explore a familiar carer?
Need / Regular office days
Arrange Milo’s daytime care without depending on personal favours.
Supporting record / P01 / Scripted
“I’d rather pay someone than keep owing a favour.”
Maya has help available. Her reason for exploring paid care is the emotional cost of asking.
Supporting record / P02 / Scripted
“I’d want to meet the carer first.”
Sam introduces a trust condition. Together with Maya’s objection, this supports exploring a meet-and-rebook revision. Their responses to that revision still need to be investigated.
Counterexample / P03 / Scripted
“We share care. It works for us.”
Lee does not describe the same burden. Keep the counterexample; available help and unwanted obligation are different things.
Pricing reaction / P02 / Scripted
“I’d only need my office days, not a visit every day.”
This challenges a daily plan. It informs packaging, but does not establish an accepted price or a purchase.
Decision boundary
These selected invented records explain the logic, not evidence sufficiency. A real brief must review the full relevant material, the revised offering, pricing and credibility before making the call. Delivery and commercial viability are separate tests.
A recommendation supports the decision. People decide what to fund, change or stop.
Planned workflow illustrated with scripted material. This is not a working AI analysis or the final interface. CBL Studio is not open for public use yet.
Your AI. Your choice.Planned connections +
We plan to let you connect your own AI provider using your API credentials and token budget. API and MCP connections are part of the direction. Supported integrations, security and data-handling details will be shared before launch.
Read it. Practice it. Keep going.Access options +
We’re exploring a standalone Studio subscription for individuals and teams, plus access alongside CBL Masterclasses. Licence options, pricing and inclusions are still taking shape.
The doors aren’t open. The list is.
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The CBL Masterclass
Build the confidence to use CBL in your work.
Studio is being built to support the process. In the Masterclass, practise CBL with AI, including customer conversations, reviewing the analysis and handling information responsibly, with guidance from the people who created it.
