Solutions: Add explanation texts, by CodeRabbit

Teaser texts have been missing on the "time series", "analytics", and
"machine learning" sections.

Author: amotl

docs/solution/analytics/index.md

@@ -5,24 +5,26 @@
 CrateDB provides real-time analytics on raw data stored for the long term.
 :::
 
-In all domains of real-time analytics where you absolutely must have access to all
-the records, and can't live with any down-sampled variants, because records are
-unique, and need to be accounted for within your analytics queries.
-
-If you find yourself in such a situation, you need a storage system which
-manages all the high-volume data in its hot zone, to be available right on
-your fingertips, for live querying. Batch jobs to roll up raw data into
-analytical results are not an option, because users' queries are too
-individual, so you need to run them on real data in real time.
-
-:::{todo}
-**Instructions:**
-Elaborate a bit longer about the topic domain and the ingredients of this section
-in an abstract way, concisely highlighting and summarizing relevant benefits,
-like the `../analytics/index`, `../industrial/index`, and `../longterm/index`
-pages are doing it already.
-Use concise language, active voice, and avoid yapping.
-:::
+CrateDB eliminates the trade-off between data accessibility and storage costs
+by keeping all high-volume raw data in the hot zone without requiring
+downsampling or aggregation. Unlike traditional systems that force you to
+choose between real-time query capabilities and long-term retention,
+CrateDB handles billions of unique records while maintaining fast query
+performance on the full dataset.
+
+Traditional analytics pipelines rely on pre-aggregated rollups or batch
+processing to handle query loads, limiting users to predefined metrics
+and losing the granularity needed for ad-hoc analysis. CrateDB's
+distributed architecture scales horizontally to support individual,
+exploratory queries on complete raw datasets in real time, enabling
+analysts to discover insights that would be invisible in downsampled data.
+
+By keeping all records immediately available for querying, you avoid the
+complexity of maintaining separate hot and cold storage tiers, ETL
+pipelines for aggregation, or data movement processes. Your analytics
+queries run directly on raw data across any time range, delivering the
+accuracy and flexibility that business intelligence and data science
+teams require.
 
 With CrateDB, compatible to PostgreSQL, you can do all of that using plain SQL.
 Other than integrating well with commodity systems using standard database

docs/solution/index.md

@@ -3,6 +3,12 @@
 
 # Solutions and use cases
 
+:::{div} sd-text-muted
+CrateDB is a distributed and scalable SQL database for storing and analyzing
+massive amounts of data in near real-time, even with complex queries. It is
+PostgreSQL-compatible, and based on Lucene.
+:::
+
 :::{toctree}
 :hidden:
 time-series/index
@@ -12,7 +18,6 @@ analytics/index
 machine-learning/index
 :::
 
-
 ## Explanations
 
 :::{div} sd-text-muted

docs/solution/machine-learning/index.md

@@ -11,14 +11,30 @@ CrateDB provides a vector type natively, and adapters for integrating
 with machine learning frameworks.
 :::
 
-:::{todo}
-**Instructions:**
-Elaborate a bit longer about the topic domain and the ingredients of this section
-in an abstract way, concisely highlighting and summarizing relevant benefits,
-like the `../analytics/index`, `../industrial/index`, and `../longterm/index`
-pages are doing it already.
-Use concise language, active voice, and avoid yapping.
-:::
+Modern AI and machine learning applications demand efficient storage and
+retrieval of high-dimensional vectors, seamless integration with ML frameworks,
+and the ability to combine traditional analytics with semantic search capabilities.
+From retrieval-augmented generation (RAG) systems to predictive maintenance models,
+organizations need a unified platform that handles vector embeddings, training datasets,
+and production model artifacts without juggling multiple specialized systems.
+
+CrateDB unifies vector search, time series analysis, and ML operations in a single
+platform. Store and query high-dimensional embeddings using native FLOAT_VECTOR support
+with HNSW-based similarity search, integrate directly with LangChain and LlamaIndex for
+AI applications, and leverage MLflow and PyCaret for end-to-end MLOps workflows. Whether
+you're building semantic search engines, training forecasting models on large time series
+datasets, or implementing hybrid search combining full-text and vector similarity, CrateDB
+eliminates data movement and infrastructure complexity.
+
+By keeping vector embeddings, training data, and model metadata in one queryable system,
+you avoid the overhead of synchronizing between specialized vector databases, data lakes,
+and model registries. Your ML pipelines remain agile, your queries span structured and
+vector data seamlessly, and your infrastructure stays lean.
+
+With CrateDB, compatible to PostgreSQL, you can do all of that using plain SQL.
+Other than integrating well with commodity systems using standard database
+access interfaces like ODBC or JDBC, it provides a proprietary HTTP interface
+on top.
 
 ## Vector store
 

docs/solution/time-series/index.md

@@ -5,18 +5,30 @@
 Use CrateDB to store and query massive amounts of time series data.
 :::
 
-CrateDB is a distributed and scalable SQL database for storing and analyzing
-massive amounts of data in near real-time, even with complex queries. It is
-PostgreSQL-compatible, and based on Lucene.
-
-:::{todo}
-**Instructions:**
-Elaborate a bit longer about the topic domain and the ingredients of this section
-in an abstract way, concisely highlighting and summarizing relevant benefits,
-like the `../analytics/index`, `../industrial/index`, and `../longterm/index`
-pages are doing it already.
-Use concise language, active voice, and avoid yapping.
-:::
+Time series data represents one of the fastest-growing data types across industries,
+from IoT sensors and industrial equipment to application metrics and financial transactions.
+The challenge lies not just in handling the sheer volume of incoming data points, but in
+maintaining query performance across both real-time streams and historical datasets while
+managing storage costs effectively.
+
+Traditional databases struggle with the unique characteristics of time series workloads:
+high write throughput, time-based queries spanning variable ranges, the need for downsampling
+and aggregation, and retention policies that balance storage with analytical requirements.
+Many organizations find themselves cobbling together multiple systems—one for ingestion,
+another for querying, and yet another for long-term storage—creating operational complexity
+and data silos.
+
+CrateDB handles time series data natively through its distributed architecture, combining
+high-speed ingestion with powerful SQL analytics across any time range. Its partitioning
+capabilities enable efficient data lifecycle management, while built-in functions for
+downsampling, interpolation, and time-window operations simplify complex analytical tasks.
+You can query billions of data points in seconds, whether analyzing recent trends or exploring
+patterns across years of historical data.
+
+With CrateDB, compatible to PostgreSQL, you can do all of that using plain SQL.
+Other than integrating well with commodity systems using standard database
+access interfaces like ODBC or JDBC, it provides a proprietary HTTP interface
+on top.
 
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 :margin: 4 4 0 0