diff --git a/etc/nnet/dn/cnn_autoenc_sector1_nBlocks2.pt b/etc/nnet/dn/cnn_autoenc_sector1_nBlocks2.pt
new file mode 100644
index 0000000000..f7af492f18
Binary files /dev/null and b/etc/nnet/dn/cnn_autoenc_sector1_nBlocks2.pt differ
diff --git a/reconstruction/ai/pom.xml b/reconstruction/ai/pom.xml
new file mode 100644
index 0000000000..b8e10fcba8
--- /dev/null
+++ b/reconstruction/ai/pom.xml
@@ -0,0 +1,43 @@
+<?xml version="1.0" encoding="UTF-8"?>
+ <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+
+  <groupId>org.jlab.clas12.detector</groupId>
+  <artifactId>clas12detector-ai</artifactId>
+  <version>13.3.0-SNAPSHOT</version>
+  <packaging>jar</packaging>
+
+  <parent>
+    <groupId>org.jlab.clas12</groupId>
+    <artifactId>reconstruction</artifactId>
+    <version>13.3.0-SNAPSHOT</version>
+  </parent>
+
+  <dependencies>
+
+    <dependency>
+      <groupId>org.jlab.clas</groupId>
+      <artifactId>clas-utils</artifactId>
+      <version>13.3.0-SNAPSHOT</version>
+    </dependency>
+
+    <dependency>
+      <groupId>org.jlab.clas</groupId>
+      <artifactId>clas-io</artifactId>
+      <version>13.3.0-SNAPSHOT</version>
+    </dependency>
+
+    <dependency>
+      <groupId>org.jlab.clas</groupId>
+      <artifactId>clas-reco</artifactId>
+      <version>13.3.0-SNAPSHOT</version>
+    </dependency>
+
+    <dependency>
+      <groupId>ai.djl</groupId>
+      <artifactId>api</artifactId>
+    </dependency>
+
+  </dependencies>
+
+ </project>
diff --git a/reconstruction/ai/src/main/java/org/jlab/service/ai/DCDenoiseEngine.java b/reconstruction/ai/src/main/java/org/jlab/service/ai/DCDenoiseEngine.java
new file mode 100644
index 0000000000..12560c7a95
--- /dev/null
+++ b/reconstruction/ai/src/main/java/org/jlab/service/ai/DCDenoiseEngine.java
@@ -0,0 +1,221 @@
+package org.jlab.service.ai;
+
+import ai.djl.MalformedModelException;
+import java.nio.file.Paths;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.types.Shape;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.training.util.ProgressBar;
+import ai.djl.translate.Translator;
+import ai.djl.translate.TranslatorContext;
+import ai.djl.inference.Predictor;
+import ai.djl.repository.zoo.ModelNotFoundException;
+import ai.djl.translate.Batchifier;
+import ai.djl.translate.TranslateException;
+import java.io.IOException;
+import java.util.concurrent.BlockingQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+
+import org.jlab.clas.reco.ReconstructionEngine;
+import org.jlab.io.base.DataBank;
+import org.jlab.io.base.DataEvent;
+import org.jlab.utils.system.ClasUtilsFile;
+
+public class DCDenoiseEngine extends ReconstructionEngine {
+
+    final static String[] BANK_NAMES = {"DC::tot","DC::tdc"};
+    final static String CONF_THRESHOLD = "threshold";
+    final static int LAYERS = 36;
+    final static int WIRES = 112;
+
+    float threshold = 0.025f;
+    Criteria<float[][],float[][]> criteria;
+    ZooModel<float[][], float[][]> model;
+    PredictorPool predictors;
+    
+    public static class PredictorPool {
+        final BlockingQueue<Predictor> pool;
+        public PredictorPool(int size, ZooModel model) {
+            pool = new LinkedBlockingQueue<>(size);
+            for (int i=0; i<size; i++) pool.offer(model.newPredictor());
+        }
+        public Predictor get() throws InterruptedException {
+            return pool.take();
+        }
+        public void put(Predictor p) {
+            if (p != null) pool.offer(p);
+        }
+    }
+
+    public DCDenoiseEngine() {
+        super("DenoiseEngine","lleztlab","1.0");
+    }
+
+    @Override
+    public boolean init() {
+        if (getEngineConfigString(CONF_THRESHOLD) != null)
+            threshold = Float.parseFloat(getEngineConfigString(CONF_THRESHOLD));
+        try {
+            criteria = Criteria.builder()
+                .setTypes(float[][].class, float[][].class)
+                .optModelPath(Paths.get(ClasUtilsFile.getResourceDir("CLAS12DIR","etc/nnet/dn/cnn_autoenc_sector1_nBlocks2.pt")))
+                .optEngine("PyTorch")
+                .optTranslator(DCDenoiseEngine.getTranslator())
+                .optProgress(new ProgressBar())
+                .build();
+            model = criteria.loadModel();
+            predictors = new PredictorPool(64, model);
+            return true;
+        } catch (NullPointerException | MalformedModelException | IOException | ModelNotFoundException ex) {
+            System.getLogger(DCDenoiseEngine.class.getName()).log(System.Logger.Level.ERROR, (String) null, ex);
+            return false;
+        }
+    }
+
+    @Override
+    public boolean processDataEvent(DataEvent event) {
+
+        //if (true) return processFakeEvent();
+       
+        for (int i=0; i<BANK_NAMES.length; i++){
+            if (event.hasBank(BANK_NAMES[i])) {
+                DataBank bank = event.getBank(BANK_NAMES[i]);
+                try {
+                    // WARNING:  Predictor is *not* thread safe.
+                    Predictor<float[][], float[][]> predictor = predictors.get();
+                    for (int sector=0; sector<6; sector++) {
+                        float[][] input = DCDenoiseEngine.read(bank, sector+1);
+                        float[][] output = predictor.predict(input);
+                        //System.out.println("IN:");show(input);
+                        //System.out.println("OUT:");show(output);
+                        update(bank, threshold, output, sector);
+                    }
+                    predictors.put(predictor);
+                    event.removeBank(BANK_NAMES[i]);
+                    event.appendBank(bank);
+                }
+                catch (TranslateException | InterruptedException e) {
+                    throw new RuntimeException(e);
+                }
+                break;
+            }
+        }
+        return true;
+    }
+
+    boolean processFakeEvent() {
+        try {
+            Predictor<float[][], float[][]> predictor = model.newPredictor();
+            float[][] input = getAlmostStraightSlightlyBendingTrack();
+            float[][] output = predictor.predict(input);
+            //System.out.println("IN:");show(input);
+            //System.out.println("OUT:");show(output);
+        }
+        catch (TranslateException e) {
+            throw new RuntimeException(e);
+        }
+        return true;
+    }
+    
+    /**
+     * Reject sub-threshold hits by modifying the bank's order variable.
+     * WARNING:  This is not a full implementation of OrderType enum and
+     * all its names, but for now a copy of the subset in C++ DC denoising, see:
+     * https://code.jlab.org/hallb/clas12/coatjava/denoising/-/blob/main/denoising/code/drift.cc?ref_type=heads#L162-198 
+     */
+    static void update(DataBank b, float threshold, float[][] data, int sector) {
+        //System.out.println("IN:");b.show();
+        for (int row=0; row<b.rows(); row++) {
+            if (b.getByte(0,row)-1 != sector) continue;
+            if (data[b.getByte(1,row)-1][b.getShort(2,row)-1] < threshold) {                
+                if(b.getByte(3,row) == 0) b.setByte(3, row, (byte)(60));
+                if(b.getByte(3,row) == 10) b.setByte(3, row, (byte)(90));
+            }
+        }
+        //System.out.println("OUT:");b.show();
+    }
+
+    /**
+     * Get one-sector data with weights set to 0/1.
+     */
+    static float[][] read(DataBank bank, int sector) {
+        float[][] data = new float[LAYERS][WIRES];
+        for (int i=0; i<bank.rows(); ++i) {
+            if (bank.getByte(0,i) == sector) {
+                byte o = bank.getByte(3,i);
+                if (0==o || 10==o)
+                    // got a hit, set weight to one:
+                    data[bank.getByte(1,i)-1][bank.getShort(2,i)-1] = 1.0f;
+            }
+        }
+        return data;
+    }
+
+    /**
+     * Print all hits for one sector.
+     */
+    static void show(float[][] data) {
+        System.out.println("Shape: [" + data.length + "," + data[0].length + "]");
+        for (int i = 0; i < LAYERS; i++) {
+            for (int j = 0; j < WIRES; j++)
+                System.out.printf("%.3f ", data[i][j]);
+            System.out.println();
+        }
+    }
+
+    /**
+     * @return a dummy sector/track 
+     */
+    static float[][] getAlmostStraightSlightlyBendingTrack() {
+        float[][] data = new float[LAYERS][WIRES];
+        for (int y = 0; y < LAYERS; y++) {
+            int x = 50 + (y / 10);
+            data[y][x] = 1.0f;
+        }
+        return data;
+    }
+
+    public static Translator<float[][],float[][]> getTranslator() {
+        return new Translator<float[][],float[][]>() {
+            @Override
+            public NDList processInput(TranslatorContext ctx, float[][] input) throws Exception {
+                NDManager manager = ctx.getNDManager();
+                int height = input.length;
+                int width = input[0].length;
+                float[] flat = new float[height * width];
+                for (int i = 0; i < height; i++) {
+                    System.arraycopy(input[i], 0, flat, i * width, width);
+                }
+                NDArray x = manager.create(flat, new Shape(height, width));
+                // Add batch and channel dims -> [1,1,36,112]
+                x = x.expandDims(0).expandDims(0);
+                return new NDList(x);
+            }
+            @Override
+            public float[][] processOutput(TranslatorContext ctx, NDList list) throws Exception {
+                NDArray result = list.get(0);
+                // Remove batch and channel dims -> [36,112]
+                result = result.squeeze();
+                // Convert to 1D float array
+                float[] flat = result.toFloatArray();
+                // Reshape manually into 2D array
+                long[] shape = result.getShape().getShape();
+                int height = (int) shape[0];
+                int width = (int) shape[1];
+                float[][] output2d = new float[height][width];
+                for (int i = 0; i < height; i++) {
+                    System.arraycopy(flat, i * width, output2d[i], 0, width);
+                }
+                return output2d;
+            }
+            @Override
+            public Batchifier getBatchifier() {
+                return null; // no batching
+            }
+        };
+    }
+
+}
diff --git a/reconstruction/pom.xml b/reconstruction/pom.xml
index 2d77b0d614..f62500c9d9 100644
--- a/reconstruction/pom.xml
+++ b/reconstruction/pom.xml
@@ -14,6 +14,7 @@
   </parent>
 
   <modules>
+    <module>ai</module>
     <module>dc</module>
     <module>tof</module>
     <module>cvt</module>