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diff --git a/source/use_case/inference_runner/src/UseCaseHandler.cc b/source/use_case/inference_runner/src/UseCaseHandler.cc
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+++ b/source/use_case/inference_runner/src/UseCaseHandler.cc
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+/*
+ * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "UseCaseHandler.hpp"
+
+#include "TestModel.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
+
+#include <cstdlib>
+
+namespace arm {
+namespace app {
+
+    bool RunInferenceHandler(ApplicationContext& ctx)
+    {
+        auto& platform = ctx.Get<hal_platform&>("platform");
+        auto& model = ctx.Get<Model&>("model");
+
+        constexpr uint32_t dataPsnTxtInfStartX = 150;
+        constexpr uint32_t dataPsnTxtInfStartY = 40;
+
+        if (!model.IsInited()) {
+            printf_err("Model is not initialised! Terminating processing.\n");
+            return false;
+        }
+
+        const size_t numInputs = model.GetNumInputs();
+
+        /* Populate each input tensor with random data. */
+        for (size_t inputIndex = 0; inputIndex < numInputs; inputIndex++) {
+
+            TfLiteTensor* inputTensor = model.GetInputTensor(inputIndex);
+
+            debug("Populating input tensor %zu@%p\n", inputIndex, inputTensor);
+            debug("Total input size to be populated: %zu\n", inputTensor->bytes);
+
+            /* Create a random input. */
+            if (inputTensor->bytes > 0) {
+
+                uint8_t* tData = tflite::GetTensorData<uint8_t>(inputTensor);
+
+                for (size_t j = 0; j < inputTensor->bytes; ++j) {
+                    tData[j] = static_cast<uint8_t>(std::rand() & 0xFF);
+                }
+            }
+        }
+
+        /* Strings for presentation/logging. */
+        std::string str_inf{"Running inference... "};
+
+        /* Display message on the LCD - inference running. */
+        platform.data_psn->present_data_text(
+                                str_inf.c_str(), str_inf.size(),
+                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+
+        RunInference(platform, model);
+
+        /* Erase. */
+        str_inf = std::string(str_inf.size(), ' ');
+        platform.data_psn->present_data_text(
+                                str_inf.c_str(), str_inf.size(),
+                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+
+#if VERIFY_TEST_OUTPUT
+        for (size_t outputIndex = 0; outputIndex < model.GetNumOutputs(); outputIndex++) {
+            arm::app::DumpTensor(model.GetOutputTensor(outputIndex));
+        }
+#endif /* VERIFY_TEST_OUTPUT */
+
+        return true;
+    }
+
+} /* namespace app */
+} /* namespace arm */