docs: remove planning ai

docs/src/modules/ROOT/nav.adoc

@@ -1,5 +1,4 @@
 * xref:introduction.adoc[leveloffset=+1]
-* xref:planning-ai-concepts.adoc[leveloffset=+1]
 * Getting started
 ** xref:quickstart/overview.adoc[Overview]
 ** xref:quickstart/service/getting-started.adoc[Build as a service]

docs/src/modules/ROOT/pages/.index.adoc

@@ -23,7 +23,6 @@ endif::[]
 
 include::introduction.adoc[leveloffset=+1]
 include::quickstart/.quickstart.adoc[leveloffset=+1]
-include::planning-ai-concepts.adoc[leveloffset=+1]
 include::running-timefold-solver/overview.adoc[leveloffset=+1]
 include::running-timefold-solver/configuration.adoc[leveloffset=+1]
 include::running-timefold-solver/library/library-integration.adoc[leveloffset=+1]

docs/src/modules/ROOT/pages/frequently-asked-questions.adoc

@@ -16,7 +16,7 @@ To find out more, see xref:commercial-editions/commercial-editions.adoc[Plus / E
 
 == Does Timefold offer pre-built models?
 
-Timefold offers a suite of pre-built PlanningAI models designed to expedite development by addressing complex scheduling and routing challenges across various industries.
+Timefold offers a suite of pre-built planning models designed to expedite development by addressing complex scheduling and routing challenges across various industries.
 These models are built upon _Timefold Solver Enterprise Edition_ technology and are accessible through a REST API, facilitating seamless integration into your applications.
 
 See all available models on https://app.timefold.ai/[our platform].

docs/src/modules/ROOT/pages/introduction.adoc

@@ -1,5 +1,5 @@
 [#introduction]
-:page-aliases: ../index.adoc
+:page-aliases: ../index.adoc, planning-ai-concepts.adoc
 :doctype: book
 :sectnums:
 :icons: font
@@ -8,7 +8,7 @@
 = Introduction
 
 Every organization faces planning problems: providing products or services with a limited set of _constrained_ resources (employees, assets, time, and money).
-Timefold Solver’s xref:planning-ai-concepts.adoc[PlanningAI] optimizes these problems to do more business with fewer resources using Constraint Satisfaction Programming.
+https://timefold.ai[Timefold Solver] is a lightweight, embeddable constraint satisfaction engine that optimizes these problems to do more business with fewer resources.
 
 [TIP]
 ====
@@ -18,22 +18,43 @@ For common planning problems, we also offer ready-made models that can be seamle
 Explore our documentation and available models https://docs.timefold.ai/[here]
 ====
 
-https://timefold.ai[Timefold Solver] is a lightweight, embeddable constraint satisfaction engine which optimizes planning problems.
-Example usecases include:
-
 .Timefold Solver's use cases include vehicle routing, employee scheduling, rostering, bin packing, and equipment scheduling.
 image::introduction/useCaseOverview.png[align="center"]
 
-Timefold Solver is 100% pure Java^TM^ and runs on Java {java-version} or higher.
-It xref:running-timefold-solver/library/integration.adoc#integration[integrates very easily] with other technologies.
-Timefold Solver works on any Java Virtual Machine and is compatible with the major JVM languages and all major platforms.
-It also supports Kotlin.
+== Planning problems
+
+A planning problem arises from a need to achieve a *goal* by organizing available *resources* under a set of *constraints*:
+
+* Building a house, where workers and materials are the resources, and construction order and site hours are the constraints.
+* Staffing a hospital shift, where qualified personnel are the resources, and working hour rules and required specializations are the constraints.
+* Routing delivery vehicles, where vehicles and drivers are the resources, and time windows, capacity, and distance are the constraints.
+
+Planning problems grow combinatorially as resources and constraints are added.
+Even a small field service problem with 4 vehicles and 8 visits produces over 19 million possible assignments for a brute-force algorithm to consider.
+Timefold Solver evaluates this space efficiently, finding good solutions in a fraction of the time it would take to do manually.
+
+== Constraints
+
+Constraints can be classified as *hard*, *medium*, or *soft*:
+
+* **Hard constraints** are rules that must not be broken — for example, an employee can only be in one place at a time, or dependent tasks must complete in order.
+  Violating a hard constraint produces an infeasible plan.
+* **Medium constraints** apply when resources are scarce — for instance, assign as many customer visits as possible when there aren't enough technicians to cover all of them.
+* **Soft constraints** express business preferences — minimize travel time, assign employees to preferred shifts, and similar goals.
+
+Solutions are scored to reflect how well they satisfy these constraints.
+For example: `0hard/-257medium/-6119520soft`.
+A solution with zero hard constraint violations is always considered better than one that breaks even a single hard constraint, regardless of the soft score.
+
+== Technology
+
+Timefold Solver is 100% pure Java^TM^ and runs on Java {java-version} or higher, on any JVM platform.
+It integrates natively with xref:running-timefold-solver/library/quarkus.adoc[Quarkus] and xref:running-timefold-solver/library/spring-boot.adoc[Spring Boot], and supports Kotlin.
 
-You can use Timefold Solver to build an xref:quickstart/overview.adoc#runAsAService[optimization service],
+You can use it to build an xref:quickstart/overview.adoc#runAsAService[optimization service],
 or embed it into your software as a xref:quickstart/overview.adoc#embedAsALibrary[library].
 
 :!sectnums:
 == Next
 
-* Follow the xref:quickstart/overview.adoc[Quickstart Example] to tackle your first planning problem.
-* Learn about some important concepts used in the realm of xref:planning-ai-concepts.adoc[PlanningAI].
+* Follow the xref:quickstart/overview.adoc[Quickstart] to tackle your first planning problem.