Months 1 to 3
Join the customer journey
KynticAI connects browsing, products, baskets, checkout, stock, fulfilment, service and returns into one story.
KynticAI
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KynticAI
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Retail and ecommerce · Synthetic portability proof
A safe 12 month example using browsing, search, products, stock, checkout, fulfilment, returns, service and customer events.
Protect conversion and margin by fixing the real cause before reaching for another broad discount.
This is one synthetic proof estate, not a special industry edition of KynticAI. The same horizontal Intelligence Engine is intended to work across any company, industry and estate.
The decision
Is this basket failing because of price, or because stock, checkout, delivery or service is getting in the way?
ACT
Fix the cause supported by the evidence, or use a targeted offer only where it makes sense.
DO NOTHING
Keep the current experience or use a broad discount without fixing the cause.
The maths behind the choice
Change the price to £79
93%
Probability of £10,000 additional contribution
Do nothing
78%
Probability of losing 400 customers and £28,000 in revenue
These probabilities come from the Intelligence Engine, not an AI confidence score.
The company information
The case study uses safe synthetic data shaped like the business. In production, most companies bring historic data, so the maths can work from day one; a genuinely new estate starts producing useful maths after the first day or two and improves as measured outcomes build.
A simulated year inside the proof estate
We use the safe 12-month simulated timeline to test what happens as new evidence arrives, previous evidence becomes stale, decisions are made and measured outcomes become part of later company history. It demonstrates improvement over time; it is not a 12-month wait before the maths works.
Months 1 to 3
KynticAI connects browsing, products, baskets, checkout, stock, fulfilment, service and returns into one story.
Months 4 to 6
Patterns across digital, stock and service data start to expose what deserves a closer look.
Months 7 to 9
Possible actions can include clearer messaging, different stock routing, service recovery, product changes or a targeted offer where it is actually justified.
Months 10 to 12
The company keeps which actions were approved and what happened to conversion, returns, repeat purchase and margin afterwards.
Questions the company can create
Which valuable baskets fail because delivery or stock information is unclear?
Which product combinations repeatedly lead to returns?
Which service problems have the biggest effect on repeat purchase?
Where are we discounting when price is not the real problem?
Choices worth testing
An approved outcome can be assigned to staff, sent through an existing workflow or system, or handed to a governed Outcome Agent where company policy allows it.
Change delivery or stock wording where uncertainty appears to be hurting the decision.
Goes to · Ecommerce team
Review stock routing when the same availability problem repeatedly loses valuable orders.
Goes to · Operations
Use a targeted offer or service action only where the evidence says it is worth doing.
Goes to · CRM and customer service
Where suitable, prepare a software change and tests for engineering review.
Goes to · Engineering review
What the business cares about
What happened after the action becomes additional company evidence for the next similar decision.
Could improve
The business can fix the real cause instead of treating every failed basket as a price problem.
Could improve
Fewer blanket discounts are needed when other causes are visible.
Could fall
Repeated product and fulfilment patterns become easier to spot.
Could improve
Service and fulfilment problems can be connected to later customer behaviour.
Could fall
Stock and delivery problems become visible as part of the same customer story.
By the end of the simulation
Which changes improved conversion without damaging margin
Which stock problems repeatedly lose sales
Which service problems affect repeat purchase
Which product combinations create unnecessary returns
How later decisions changed after earlier results
Now make the proof look like your company
Synthetic ecommerce scenario. The data, questions and possible effects are examples, not a named customer claim or guaranteed commercial result.