diff --git a/NAMESPACE b/NAMESPACE
index 151aa6149..9e741cbe7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -44,6 +44,7 @@ S3method(score,forecast_quantile)
 S3method(score,forecast_sample)
 S3method(score,forecast_sample_multivariate)
 S3method(tail,forecast)
+export(add_interval_range)
 export(add_relative_skill)
 export(ae_median_quantile)
 export(ae_median_sample)
@@ -69,6 +70,7 @@ export(get_duplicate_forecasts)
 export(get_forecast_counts)
 export(get_forecast_unit)
 export(get_grouping)
+export(get_interval_range)
 export(get_metrics)
 export(get_pairwise_comparisons)
 export(get_pit_histogram)
diff --git a/R/get-coverage.R b/R/get-coverage.R
index 66974e41c..2324f56b6 100644
--- a/R/get-coverage.R
+++ b/R/get-coverage.R
@@ -75,7 +75,7 @@ get_coverage <- function(forecast, by = "model") {
 
   # merge interval range data with original data -------------------------------
   # preparations
-  forecast[, interval_range := get_range_from_quantile(quantile_level)]
+  forecast[, interval_range := get_interval_range(quantile_level)]
   forecast_cols <- colnames(forecast) # store so we can reset column order later
   forecast_unit <- get_forecast_unit(forecast)
 
diff --git a/R/helper-quantile-interval-range.R b/R/helper-quantile-interval-range.R
index cba7b5ff7..4e490d4b1 100644
--- a/R/helper-quantile-interval-range.R
+++ b/R/helper-quantile-interval-range.R
@@ -68,7 +68,7 @@ quantile_to_interval_dataframe <- function(forecast,
   forecast <- as.data.table(forecast)
 
   forecast[, boundary := ifelse(quantile_level <= 0.5, "lower", "upper")]
-  forecast[, interval_range := get_range_from_quantile(quantile_level)]
+  forecast[, interval_range := get_interval_range(quantile_level)]
 
   # add median quantile
   median <- forecast[quantile_level == 0.5, ]
@@ -172,7 +172,7 @@ sample_to_interval_long <- function(data,
 #' Get interval range belonging to a quantile
 #' @description
 #' Every quantile can be thought of either as the lower or the
-#' upper bound of a symmetric central prediction interval. This helper function
+#' upper bound of a symmetric central prediction interval. This function
 #' returns the range of the central prediction interval to which the quantile
 #' belongs.
 #'
@@ -181,8 +181,10 @@ sample_to_interval_long <- function(data,
 #' use cases, but it is something to be aware of.
 #' @param quantile_level A numeric vector of quantile levels of size N.
 #' @returns a numeric vector of interval ranges of size N
-#' @keywords internal
-get_range_from_quantile <- function(quantile_level) {
+#' @examples
+#' get_interval_range(c(0.05, 0.25, 0.5, 0.75, 0.95))
+#' @export
+get_interval_range <- function(quantile_level) {
   boundary <- ifelse(quantile_level <= 0.5, "lower", "upper")
   interval_range <- ifelse(
     boundary == "lower",
@@ -191,3 +193,32 @@ get_range_from_quantile <- function(quantile_level) {
   )
   return(interval_range)
 }
+
+
+#' Add interval range column to a data.table with quantile-level forecasts
+#'
+#' @description
+#' Adds an `interval_range` column to a data.table that has a
+#' `quantile_level` column. The interval range is computed using
+#' [get_interval_range()].
+#'
+#' @param data A data.table (or object coercible to data.table) with a
+#'   `quantile_level` column.
+#' @returns A data.table with an additional `interval_range` column.
+#' @examples
+#' library(data.table)
+#' dt <- data.table(
+#'   observed = 5,
+#'   predicted = c(1, 3, 5, 7, 9),
+#'   quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95)
+#' )
+#' add_interval_range(dt)
+#' @importFrom data.table as.data.table
+#' @importFrom checkmate assert_names
+#' @export
+add_interval_range <- function(data) {
+  data <- ensure_data.table(data)
+  assert_names(colnames(data), must.include = "quantile_level")
+  data[, interval_range := get_interval_range(quantile_level)]
+  return(data[])
+}
diff --git a/R/metrics-quantile.R b/R/metrics-quantile.R
index fd5775076..6376711fe 100644
--- a/R/metrics-quantile.R
+++ b/R/metrics-quantile.R
@@ -185,7 +185,7 @@ wis <- function(observed,
   reformatted <- quantile_to_interval(observed, predicted, quantile_level)
 
   # check that all quantile levels form valid prediction intervals
-  interval_ranges <- get_range_from_quantile(
+  interval_ranges <- get_interval_range(
     quantile_level[quantile_level != 0.5]
   )
   complete_intervals <-
diff --git a/man/add_interval_range.Rd b/man/add_interval_range.Rd
new file mode 100644
index 000000000..ca9fb337b
--- /dev/null
+++ b/man/add_interval_range.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/helper-quantile-interval-range.R
+\name{add_interval_range}
+\alias{add_interval_range}
+\title{Add interval range column to a data.table with quantile-level forecasts}
+\usage{
+add_interval_range(data)
+}
+\arguments{
+\item{data}{A data.table (or object coercible to data.table) with a
+\code{quantile_level} column.}
+}
+\value{
+A data.table with an additional \code{interval_range} column.
+}
+\description{
+Adds an \code{interval_range} column to a data.table that has a
+\code{quantile_level} column. The interval range is computed using
+\code{\link[=get_interval_range]{get_interval_range()}}.
+}
+\examples{
+library(data.table)
+dt <- data.table(
+  observed = 5,
+  predicted = c(1, 3, 5, 7, 9),
+  quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95)
+)
+add_interval_range(dt)
+}
diff --git a/man/get_range_from_quantile.Rd b/man/get_interval_range.Rd
similarity index 75%
rename from man/get_range_from_quantile.Rd
rename to man/get_interval_range.Rd
index 08df972b9..ce684ea5f 100644
--- a/man/get_range_from_quantile.Rd
+++ b/man/get_interval_range.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/helper-quantile-interval-range.R
-\name{get_range_from_quantile}
-\alias{get_range_from_quantile}
+\name{get_interval_range}
+\alias{get_interval_range}
 \title{Get interval range belonging to a quantile}
 \usage{
-get_range_from_quantile(quantile_level)
+get_interval_range(quantile_level)
 }
 \arguments{
 \item{quantile_level}{A numeric vector of quantile levels of size N.}
@@ -14,7 +14,7 @@ a numeric vector of interval ranges of size N
 }
 \description{
 Every quantile can be thought of either as the lower or the
-upper bound of a symmetric central prediction interval. This helper function
+upper bound of a symmetric central prediction interval. This function
 returns the range of the central prediction interval to which the quantile
 belongs.
 
@@ -22,4 +22,6 @@ Due to numeric instability that sometimes occurred in the past, ranges are
 rounded to 10 decimal places. This is not a problem for the vast majority of
 use cases, but it is something to be aware of.
 }
-\keyword{internal}
+\examples{
+get_interval_range(c(0.05, 0.25, 0.5, 0.75, 0.95))
+}
diff --git a/tests/testthat/test-get-coverage.R b/tests/testthat/test-get-coverage.R
index 13d992e06..cfab41e7f 100644
--- a/tests/testthat/test-get-coverage.R
+++ b/tests/testthat/test-get-coverage.R
@@ -37,7 +37,7 @@ test_that("get_coverage() can deal with non-symmetric prediction intervals", {
 
   cov <- expect_no_condition(get_coverage(test))
 
-  prediction_intervals <- get_range_from_quantile(c(0.2, 0.3, 0.5))
+  prediction_intervals <- get_interval_range(c(0.2, 0.3, 0.5))
 
   missing <- cov[interval_range %in% prediction_intervals]
   not_missing <- cov[!interval_range %in% prediction_intervals]
diff --git a/tests/testthat/test-helper-quantile-interval-range.R b/tests/testthat/test-helper-quantile-interval-range.R
index e64cc3211..f39e089a3 100644
--- a/tests/testthat/test-helper-quantile-interval-range.R
+++ b/tests/testthat/test-helper-quantile-interval-range.R
@@ -219,3 +219,76 @@ test_that("quantile_to_interval works - data.frame case", {
     "Input must be either a data.frame or a numeric vector."
   )
 })
+
+
+# ==============================================================================
+# get_interval_range() # nolint: commented_code_linter
+# ==============================================================================
+
+test_that("get_interval_range() returns correct interval ranges for standard quantiles", {
+  result <- get_interval_range(c(0.05, 0.25, 0.5, 0.75, 0.95))
+  expect_identical(result, c(90, 50, 0, 50, 90))
+})
+
+test_that("get_interval_range() handles edge cases correctly", {
+  expect_identical(get_interval_range(c(0, 1)), c(100, 100))
+  expect_identical(get_interval_range(0.5), 0)
+  expect_identical(get_interval_range(c(0.1, 0.9)), c(80, 80))
+})
+
+test_that("get_interval_range() is exported and accessible without :::", {
+  expect_no_error(get_interval_range(0.25))
+  expect_identical(get_interval_range(0.25), 50)
+})
+
+
+# ==============================================================================
+# add_interval_range() # nolint: commented_code_linter
+# ==============================================================================
+
+test_that("add_interval_range() adds interval_range column to quantile forecast", {
+  dt <- data.table::data.table(
+    observed = 5,
+    predicted = c(1, 3, 5, 7, 9),
+    quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95)
+  )
+  ncol_before <- ncol(dt)
+  result <- add_interval_range(dt)
+  expect_true("interval_range" %in% colnames(result))
+  expect_identical(result$interval_range, c(90, 50, 0, 50, 90))
+  expect_identical(ncol(result), ncol_before + 1L)
+})
+
+test_that("add_interval_range() works with example_quantile dataset", {
+  ex <- na.omit(example_quantile)
+  result <- add_interval_range(ex)
+  expect_true("interval_range" %in% colnames(result))
+  expect_identical(nrow(result), nrow(ex))
+  expect_identical(result$interval_range, get_interval_range(result$quantile_level))
+})
+
+test_that("add_interval_range() returns a copy (does not modify input in place)", {
+  dt <- data.table::data.table(
+    observed = 5,
+    predicted = c(1, 3, 5, 7, 9),
+    quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95)
+  )
+  result <- add_interval_range(dt)
+  # ensure_data.table() copies, so original dt should be unchanged
+  expect_false("interval_range" %in% colnames(dt))
+  expect_true("interval_range" %in% colnames(result))
+})
+
+test_that("add_interval_range() errors gracefully on non-quantile input", {
+  dt <- data.table::data.table(
+    observed = 1:5,
+    predicted = 2:6,
+    sample_id = 1:5
+  )
+  expect_error(add_interval_range(dt))
+})
+
+test_that("internal call sites work correctly after rename", {
+  expect_no_error(get_coverage(as_forecast_quantile(example_quantile)))
+  expect_no_error(score(as_forecast_quantile(example_quantile)))
+})
diff --git a/vignettes/Deprecated-visualisations.Rmd b/vignettes/Deprecated-visualisations.Rmd
index d16f6e8e6..1df02ecd6 100644
--- a/vignettes/Deprecated-visualisations.Rmd
+++ b/vignettes/Deprecated-visualisations.Rmd
@@ -438,7 +438,7 @@ The functionality currently relies on a hack. In previous versions of `scoringut
 ```{r}
 range_example <- copy(example_quantile) |>
   na.omit()
-range_example[, range := scoringutils:::get_range_from_quantile(quantile_level)] # nolint: undesirable_operator_linter
+range_example[, range := scoringutils::get_interval_range(quantile_level)]
 
 sum_scores <- range_example |>
   as_forecast_quantile() |>