diff --git a/docs/make.jl b/docs/make.jl
index 8a2835d7..94546761 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -13,6 +13,6 @@ makedocs(modules = [DataInterpolations],
     format = Documenter.HTML(assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/DataInterpolations/stable/"),
     pages = ["index.md", "Methods" => "methods.md",
-        "Interface" => "interface.md", "Manual" => "manual.md"])
+        "Interface" => "interface.md", "Manual" => "manual.md", "Inverting Integrals" => "inverting_integrals.md"])
 
 deploydocs(repo = "github.com/SciML/DataInterpolations.jl"; push_preview = true)
diff --git a/docs/src/inverting_integrals.md b/docs/src/inverting_integrals.md
new file mode 100644
index 00000000..2d10f848
--- /dev/null
+++ b/docs/src/inverting_integrals.md
@@ -0,0 +1,53 @@
+# Inverting integrals
+
+Solving implicit integral problems of the form:
+
+```math
+\begin{equation}
+    \text{find $t$ such that } \int_{t_1}^t f(\tau)\text{d}\tau = V \ge 0
+\end{equation}
+```
+
+is supported for interpolations $f$ that are strictly positive and of one of these types:
+
+  - `ConstantInterpolation`
+  - `LinearInterpolation`
+
+This is done by creating an 'integral inverse' interpolation object which can efficiently compute $t$ for a given value of $V$, see the example below.
+
+```@example inverting_integrals
+using Random #hide
+Random.seed!(1234) # hide
+using DataInterpolations
+using Plots
+
+# Create LinearInterpolation object from the
+u = sqrt.(1:25) + (2.0 * rand(25) .- 1.0) / 3
+t = cumsum(rand(25))
+A = LinearInterpolation(u, t)
+
+# Create LinearInterpolationIntInv object
+# from the LinearInterpolation object
+A_intinv = DataInterpolations.invert_integral(A)
+
+# Get the t values up to and including the
+# solution to the integral problem
+V = 25.0
+t_ = A_intinv(V)
+ts = t[t .<= t_]
+push!(ts, t_)
+
+# Plot results
+plot(A; label = "Linear Interpolation")
+plot!(ts, A.(ts), fillrange = 0.0, fillalpha = 0.75,
+    fc = :blues, lw = 0, label = "Area of $V")
+```
+
+## Docstrings
+
+```@docs
+DataInterpolations.invert_integral
+ConstantInterpolationIntInv
+LinearInterpolationIntInv
+```
+
diff --git a/src/DataInterpolations.jl b/src/DataInterpolations.jl
index 4ff881d9..d5b69e77 100644
--- a/src/DataInterpolations.jl
+++ b/src/DataInterpolations.jl
@@ -90,7 +90,7 @@ end
 export LinearInterpolation, QuadraticInterpolation, LagrangeInterpolation,
        AkimaInterpolation, ConstantInterpolation, QuadraticSpline, CubicSpline,
        BSplineInterpolation, BSplineApprox, CubicHermiteSpline,
-       QuinticHermiteSpline
+       QuinticHermiteSpline, LinearInterpolationIntInv, ConstantInterpolationIntInv
 
 # added for RegularizationSmooth, JJS 11/27/21
 ### Regularization data smoothing and interpolation
diff --git a/src/integral_inverses.jl b/src/integral_inverses.jl
index a6219b35..e8ecface 100644
--- a/src/integral_inverses.jl
+++ b/src/integral_inverses.jl
@@ -1,8 +1,23 @@
 abstract type AbstractIntegralInverseInterpolation{T} <: AbstractInterpolation{T} end
 
 _integral(A::AbstractIntegralInverseInterpolation, idx, t) = throw(IntegralNotFoundError())
+
+"""
+    invert_integral(A::AbstractInterpolation)::AbstractIntegralInverseInterpolation
+
+Creates the inverted integral interpolation object from the given interpolation. Conditions:
+  - The range of `A` must be strictly positive
+  - There must be an ordering defined on the data type of `A.u`
+  - This is currently only supported for ConstantInterpolation and LinearInterpolation
+
+## Arguments
+  - `A`: interpolation object satisfying the above requirements
+"""
 invert_integral(A::AbstractInterpolation) = throw(IntegralInverseNotFoundError())
 
+"""
+    some stuff
+"""
 struct LinearInterpolationIntInv{uType, tType, itpType, T} <:
        AbstractIntegralInverseInterpolation{T}
     u::uType
@@ -36,6 +51,9 @@ function _interpolate(
     u, idx
 end
 
+"""
+    some stuff
+"""
 struct ConstantInterpolationIntInv{uType, tType, itpType, T} <:
        AbstractIntegralInverseInterpolation{T}
     u::uType