diff --git a/Project.toml b/Project.toml
index 7d89e15..ee2ec89 100644
--- a/Project.toml
+++ b/Project.toml
@@ -11,6 +11,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 [compat]
 ArgCheck = "1, 2"
 DocStringExtensions = "0.8, 0.9"
+Random = "1.10"
 julia = "1.10"
 
 [extras]
diff --git a/src/LogDensityProblems.jl b/src/LogDensityProblems.jl
index 650fb44..cddca0e 100644
--- a/src/LogDensityProblems.jl
+++ b/src/LogDensityProblems.jl
@@ -17,6 +17,7 @@ using DocStringExtensions: SIGNATURES, TYPEDEF
 using Random: AbstractRNG, default_rng
 
 # https://github.com/JuliaLang/julia/pull/50105
+# NOTE remove this once we require Julia v1.11 and use public
 @static if VERSION >= v"1.11.0-DEV.469"
     eval(
         Expr(
@@ -27,6 +28,8 @@ using Random: AbstractRNG, default_rng
             :logdensity,
             :logdensity_and_gradient,
             :logdensity_gradient_and_hessian,
+            :stresstest,
+            :converting_logdensity,
         )
     )
 end
diff --git a/src/utilities.jl b/src/utilities.jl
index 7e7d514..2b189e8 100644
--- a/src/utilities.jl
+++ b/src/utilities.jl
@@ -59,3 +59,81 @@ function stresstest(f, ℓ; N = 1000, rng::AbstractRNG = default_rng(), scale =
     end
     failures
 end
+
+####
+#### converting input and output
+####
+
+"""
+Type implementating [`converting_logdensity`](@ref).
+
+Not part of the API *per se*, use the eponymous function to construct.
+"""
+struct ConvertingLogDensity{I,  # input type
+                            L,  # logdensity output type
+                            G,  # gradient output type
+                            H,  # Hessian output type
+                            P}
+    parent::P
+end
+
+"""
+$(SIGNATURES)
+
+Return an object implementing the same [`capabilities`](@ref) as the first argument,
+converting inputs and outputs are specified.
+
+All conversions are implemented via `convert(T, …)::T`. Typical use cases include fixing
+type instability introduced by automatic differentiation.
+
+The original logdensity can be retrieved with `parent`.
+
+# Keyword arguments (with defaults)
+
+- `input = Any`: convert *inputs* to the given type before evaluating log densities, gradients, ….
+
+- `logdensity = Any`: convert the *log density* to that type.
+
+- `gradient = Any`: convert the *gradient* to that type.
+
+- `hessian = Any`: convert the *Hessian* to that type.
+
+# Note
+
+The types are not checked for validity.
+
+# Examples
+
+```julia
+converting_logdensity(ℓ; input = Vector{Float32}, logdensity = Float64,
+                         gradient = Vector{Float64})
+```
+will convert to a vector of `Float32`s, then enforce Float64 elements for the logdensity
+and its gradient, leaving the Hessian alone.
+"""
+function converting_logdensity(ℓ::P; input::Type = Any, logdensity::Type = Any, gradient::Type = Any,
+                               hessian::Type = Any) where P
+    @argcheck(capabilities(ℓ) ≥ LogDensityOrder(0),
+              "Input does not implement the log density interface.")
+    ConvertingLogDensity{input,logdensity,gradient,hessian,P}(ℓ)
+end
+
+Base.parent(ℓ::ConvertingLogDensity) = ℓ.parent
+
+capabilities(ℓ::ConvertingLogDensity) = capabilities(ℓ.parent)
+
+dimension(ℓ::ConvertingLogDensity) = dimension(ℓ.parent)
+
+function logdensity(ℓ::ConvertingLogDensity{I,L}, x) where {I,L}
+    convert(L, logdensity(ℓ.parent, convert(I, x)))::L
+end
+
+function logdensity_and_gradient(ℓ::ConvertingLogDensity{I,L,G}, x) where {I,L,G}
+    l, g = logdensity_and_gradient(ℓ.parent, convert(I, x))
+    convert(L, l)::L, convert(G, g)::G
+end
+
+function logdensity_gradient_and_hessian(ℓ::ConvertingLogDensity{I,L,G,H}, x) where {I,L,G,H}
+    l, g, h = logdensity_gradient_and_hessian(ℓ.parent, convert(I, x))
+    convert(L, l)::L, convert(G, g)::G, convert(H, h)::H
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index bd04938..9912673 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,5 +1,6 @@
 using LogDensityProblems, Test, Random
-import LogDensityProblems: capabilities, dimension, logdensity
+import LogDensityProblems: capabilities, dimension, logdensity, logdensity_and_gradient,
+    logdensity_gradient_and_hessian
 using LogDensityProblems: logdensity_and_gradient, LogDensityOrder
 
 ####
@@ -96,6 +97,7 @@ end
 ####
 
 @testset "public API" begin
+    # NOTE remove this once we require Julia v1.11 and use public
     if isdefined(Base, :ispublic)
         @test Base.ispublic(LogDensityProblems, :capabilities)
         @test Base.ispublic(LogDensityProblems, :LogDensityOrder)
@@ -103,5 +105,42 @@ end
         @test Base.ispublic(LogDensityProblems, :logdensity)
         @test Base.ispublic(LogDensityProblems, :logdensity_and_gradient)
         @test Base.ispublic(LogDensityProblems, :logdensity_gradient_and_hessian)
+        @test Base.ispublic(LogDensityProblems, :stresstest)
+        @test Base.ispublic(LogDensityProblems, :converting_logdensity)
     end
 end
+
+####
+#### converting logdensity
+####
+
+struct BadLogDensity end
+dimension(::BadLogDensity) = 1
+capabilities(::BadLogDensity) = LogDensityOrder(2)
+_bad_x(x) = (_x = only(x); _x > 0 ? Float64(_x) : _x) # introduce type instability
+function logdensity(::BadLogDensity, x::Vector{Float32}) # deliberate restriction
+     -_bad_x(x)^2 / 2
+end
+function logdensity_and_gradient(::BadLogDensity, x::Vector{Float32})
+    _x = _bad_x(x)
+     -_x^2 / 2, [-_x]
+end
+function logdensity_gradient_and_hessian(::BadLogDensity, x::Vector{Float32})
+    _x = _bad_x(x)
+     -_x^2 / 2, [-_x], [-one(_x)]
+end
+
+@testset "converting logdensity" begin
+    bad = BadLogDensity()
+    ℓ = LogDensityProblems.converting_logdensity(bad;
+                                                 input = Vector{Float32},
+                                                 logdensity = Float64,
+                                                 gradient = Vector{Float64})
+    @test dimension(ℓ) == dimension(bad)
+    @test capabilities(ℓ) == capabilities(bad)
+    x = [0.9]                   # no such method for the parent
+    xF32 = Float32.(x)
+    @test @inferred(logdensity(ℓ, x)) == logdensity(bad, xF32)
+    @test @inferred(logdensity_and_gradient(ℓ, x)) == logdensity_and_gradient(bad, xF32)
+    @test eltype(logdensity_gradient_and_hessian(ℓ, .-x)[3]) ≡ Float32 # we do not touch this
+end