diff --git a/README.md b/README.md
index 8b9f8868..9e5e9708 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,29 @@
-# pyogrio - Vectorized spatial vector file format I/O using GDAL/OGR
-
-Pyogrio provides a
-[GeoPandas](https://github.com/geopandas/geopandas)-oriented API to OGR vector
-data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources
-have geometries, such as points, lines, or polygons, and associated records
-with potentially many columns worth of data.
-
-Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and
-from OGR vector data sources in order to give you faster interoperability. It
-uses pre-compiled bindings for GDAL/OGR so that the performance is primarily
-limited by the underlying I/O speed of data source drivers in GDAL/OGR rather
-than multiple steps of converting to and from Python data types within Python.
+# pyogrio - bulk-oriented spatial vector file I/O using GDAL/OGR
+
+Pyogrio provides fast, bulk-oriented read and write access to 
+[GDAL/OGR](https://gdal.org/en/latest/drivers/vector/index.html) vector data
+sources, such as ESRI Shapefile, GeoPackage, GeoJSON, and several others.
+Vector data sources typically have geometries, such as points, lines, or
+polygons, and associated records with potentially many columns worth of data.
+
+The typical use is to read or write these data sources to/from
+[GeoPandas](https://github.com/geopandas/geopandas) `GeoDataFrames`. Because
+the geometry column is optional, reading or writing only non-spatial data is
+also possible. Hence, GeoPackage attribute tables, DBF files, or CSV files are
+also supported.
+
+Pyogrio is fast because it uses pre-compiled bindings for GDAL/OGR to read and
+write the data records in bulk. This approach avoids multiple steps of
+converting to and from Python data types within Python, so performance becomes
+primarily limited by the underlying I/O speed of data source drivers in
+GDAL/OGR.
 
 We have seen \>5-10x speedups reading files and \>5-20x speedups writing files
-compared to using non-vectorized approaches (Fiona and current I/O support in
-GeoPandas).
-
-You can read these data sources into
-`GeoDataFrames`, read just the non-geometry columns into Pandas `DataFrames`,
-or even read non-spatial data sources that exist alongside vector data sources,
-such as tables in a ESRI File Geodatabase, or antiquated DBF files.
-
-Pyogrio also enables you to write `GeoDataFrames` to at least a few different
-OGR vector data source formats.
+compared to using row-per-row approaches (e.g. Fiona).
 
 Read the documentation for more information:
 [https://pyogrio.readthedocs.io](https://pyogrio.readthedocs.io/en/latest/).
 
-WARNING: Pyogrio is still at an early version and the API is subject to
-substantial change. Please see [CHANGES](CHANGES.md).
-
 ## Requirements
 
 Supports Python 3.9 - 3.13 and GDAL 3.4.x - 3.9.x.
@@ -52,9 +46,9 @@ for more information.
 
 ## Supported vector formats
 
-Pyogrio supports some of the most common vector data source formats (provided
-they are also supported by GDAL/OGR), including ESRI Shapefile, GeoPackage,
-GeoJSON, and FlatGeobuf.
+Pyogrio supports most common vector data source formats (provided they are also
+supported by GDAL/OGR), including ESRI Shapefile, GeoPackage, GeoJSON, and
+FlatGeobuf.
 
 Please see the [list of supported formats](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)
 for more information.
@@ -64,7 +58,7 @@ for more information.
 Please read the [introduction](https://pyogrio.readthedocs.io/en/latest/supported_formats.html)
 for more information and examples to get started using Pyogrio.
 
-You can also check out the the [API documentation](https://pyogrio.readthedocs.io/en/latest/api.html)
+You can also check out the [API documentation](https://pyogrio.readthedocs.io/en/latest/api.html)
 for full details on using the API.
 
 ## Credits