diff --git a/README.md b/README.md
index 034cccc..215c215 100644
--- a/README.md
+++ b/README.md
@@ -17,13 +17,34 @@ from those streams.
 > **Note:** Data must be acquired in the `aind-behavior-dynamic-foraging` data
 > contract format to be compatible with these tools.
 
+## Inputs and outputs
+**Input** — a raw acquisition directory in the `aind-behavior-dynamic-foraging`
+data contract format (Harp device registers, software events, and the task-logic /
+rig / session input schemas), loaded via `RawDataLoader`.
+
+**Outputs** — the NWB-ready containers assembled from those streams:
+
+- **Acquisition.** Built by `AcquisitionBuilder` and written to the NWB acquisition
+  module:
+  - 4 derived event series:
+    - `left_reward_delivery_time` / `right_reward_delivery_time` — reward delivery
+      times per lick port, with each event annotated as `earned`, `manual`, or
+      `automatic`.
+    - `left_lick_time` / `right_lick_time` — detected lick times per lick port.
+  - All raw contract streams — every stream returned by the loader
+    (`get_all_raw_data()`) is additionally packaged verbatim as a `DynamicTable`,
+    so the full raw dataset travels alongside the derived series.
+- **Trials table.** Built by `TrialTableBuilder`, one row per trial written to the
+  NWB `trials` table (see [`docs/trials_table_mapping.md`](docs/trials_table_mapping.md)).
+
 ## Level of Support
  - [x] Supported: We are releasing this code to the public as a tool we expect others to use. Issues are welcomed, and we expect to address them promptly; pull requests will be vetted by our staff before inclusion.
  - [ ] Occasional updates: We are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.
  - [ ] Unsupported: We are not currently supporting this code, but simply releasing it to the community AS IS but are not able to provide any guarantees of support. The community is welcome to submit issues, but you should not expect an active response.
 
 ## Modules
-The package (`src/dynamic_foraging_processing`) is organized into two modules:
+The package (`src/dynamic_foraging_processing`) is organized into the following
+modules:
 
 ### `raw_data_loader`
 Loads raw acquisition data via the data contract.
@@ -36,6 +57,14 @@ Loads raw acquisition data via the data contract.
    - `raw_data_stream_descriptions` → `dict` mapping each stream to its description
      (used when building NWB).
 
+### `nwb`
+Builds the NWB acquisition entries (requires the `nwb` extra).
+ - **`AcquisitionBuilder`** — assembles the acquisition module from the raw
+   streams: the four derived event series (`left`/`right_reward_delivery_time`,
+   `left`/`right_lick_time`) plus every raw contract stream packaged as a
+   `DynamicTable`. Returns `AcquisitionSeries` / `AcquisitionTable` models that a
+   writer translates into `pynwb` objects.
+
 ### `processing`
 Builds derived NWB containers from the raw streams.
  - **`TrialTableBuilder`** — assembles the NWB `trials` table from a `Dataset`.
@@ -49,15 +78,38 @@ Builds derived NWB containers from the raw streams.
    `TrialConfig.column_descriptions()` to retrieve them. The column-by-column source
    mapping is documented in [`docs/trials_table_mapping.md`](docs/trials_table_mapping.md).
 
+### `qc`
+Assembles an `aind-data-schema` `QualityControl` object (requires the `qc` extra).
+ - **`RawQC`** / **`ProcessedQC`** — the raw (contract QA) and processed (behavior
+   metrics) QC stages. `build_quality_control(metrics)` collects their metrics into
+   a single `QualityControl`.
+
+### `pipeline`
+Ties the building blocks into two Code Ocean capsule entry points (requires the
+`full` extra).
+ - **`Pipeline`** — over a `RawDataLoader`:
+   - `run_nwb(output_path)` — assembles the NWB file (base metadata + acquisition
+     entries + trials table), writes it to `output_path / "behavior.nwb.zarr"`, and
+     writes a `processing.json` alongside it.
+   - `run_qc(output_path)` — runs the QC stages and writes `quality_control.json`.
+
+   Both entry points build and return their objects in memory; `output_path` is
+   optional and only written to when provided.
+
 ## Repository structure
 ```
 src/dynamic_foraging_processing/
 ├── raw_data_loader/        # RawDataLoader: load raw streams via the data contract
 │   └── loader.py
-└── processing/             # Derived NWB containers
-    ├── _trial_table.py     # TrialTableBuilder
-    └── models/
-        └── trial_config.py # TrialConfig (one trials-table row + NWB descriptions)
+├── nwb/                    # NWB acquisition builder + models
+│   ├── acquisition/        # AcquisitionBuilder, AcquisitionSeries/Table
+│   └── utils.py            # clean_for_nwb: make frames safe to write to NWB
+├── processing/             # Derived NWB containers
+│   ├── _trial_table.py     # TrialTableBuilder
+│   └── models/
+│       └── trial_config.py # TrialConfig (one trials-table row + NWB descriptions)
+├── qc/                     # QualityControl assembly (raw + processed stages)
+└── pipeline/               # Pipeline: run_nwb / run_qc capsule entry points
 
 docs/                       # Source mapping and NWB notes (trials_table_mapping.md, ...)
 examples/                   # Runnable notebooks (raw_data_loader_example, trial_table_example)
@@ -69,16 +121,19 @@ tests/                      # Unit tests
 from pathlib import Path
 
 from dynamic_foraging_processing.raw_data_loader import RawDataLoader
-from dynamic_foraging_processing.processing import TrialTableBuilder
+from dynamic_foraging_processing.pipeline import Pipeline
 
 # Point at the root of an acquisition directory.
 loader = RawDataLoader(path=Path("/path/to/acquisition"))
+pipeline = Pipeline(loader)
 
-# Build the NWB trials table (one row per trial).
-trials = TrialTableBuilder(loader.dataset).build()
+# Write the NWB file (+ processing.json) and the QC (+ quality_control.json).
+pipeline.run_nwb("/path/to/output")
+pipeline.run_qc("/path/to/output")
 
-# Or inspect raw streams directly.
-raw = loader.get_all_raw_data()
+# Or use the building blocks directly.
+trials = pipeline.build_trials()   # NWB trials table, one row per trial
+raw = loader.get_all_raw_data()    # inspect raw streams
 ```
 See [`examples/`](examples) for end-to-end notebooks.
 
@@ -89,14 +144,16 @@ uv sync
 ```
 
 > [!IMPORTANT]
-> The base install includes **only** the raw data loader and does **not** pull in
-> `aind-data-schema` or `matplotlib`. The quality-control (`qc`) module requires
-> both, so importing `dynamic_foraging_processing.qc` will fail on a base install.
-> To use the QC module, install the `qc` (or equivalent `full`) extra:
+> The base install includes **only** the raw data loader. The optional modules
+> pull in their own dependencies, so importing them will fail on a base install:
+> - **`qc`** — `aind-data-schema`, `matplotlib` (for `dynamic_foraging_processing.qc`).
+> - **`nwb`** — `aind-nwb-utils`, `hdmf-zarr`, `pynwb` (for the `nwb` builder).
+> - **`full`** — everything above (`qc` + `nwb`); required by the `pipeline` module.
+>
+> Install the extra you need, e.g.:
 > ```bash
-> pip install -e ".[qc]"
+> pip install -e ".[full]"
 > ```
-> The `full` extra is currently an alias for `qc`.
 
 To develop the code, run
 to create the environment and install the package. To include the development
@@ -113,9 +170,9 @@ uv run ruff check . && uv run ruff format --check .   # lint + format
 uv run interrogate -v .                               # docstring coverage
 uv run coverage run -m pytest && uv run coverage report
 ```
-To include the QC dependencies with uv, run
+To include the optional-module dependencies (qc + nwb) with uv, run
 ```bash
-uv sync --extra qc
+uv sync --extra full
 ```
 
 ## Release Status
diff --git a/pyproject.toml b/pyproject.toml
index 81203f7..edf7cc7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,8 +26,13 @@ qc = [
     "aind-data-schema>=2.4.1",
     "matplotlib",
 ]
+nwb = [
+    "aind-nwb-utils",
+    "hdmf-zarr",
+    "pynwb",
+]
 full = [
-    "dynamic-foraging-processing[qc]",
+    "dynamic-foraging-processing[qc,nwb]",
 ]
 
 [dependency-groups]
diff --git a/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py b/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
index 042b4cc..047bd48 100644
--- a/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
+++ b/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
@@ -9,7 +9,7 @@
     AcquisitionSeries,
     AcquisitionTable,
 )
-from dynamic_foraging_processing.nwb.utils import clean_dataframe_for_nwb
+from dynamic_foraging_processing.nwb.utils import clean_for_nwb
 from dynamic_foraging_processing.raw_data_loader import RawDataLoader
 from dynamic_foraging_processing.utils.rewards import get_annotated_rewards
 
@@ -264,7 +264,7 @@ def build_acquisition(
             acquisiton_entries.append(
                 AcquisitionTable(
                     name=stream_name,
-                    data=clean_dataframe_for_nwb(stream_data),
+                    data=clean_for_nwb(stream_data),
                     description=description,
                 )
             )
diff --git a/src/dynamic_foraging_processing/nwb/utils.py b/src/dynamic_foraging_processing/nwb/utils.py
index c714f62..eed6452 100644
--- a/src/dynamic_foraging_processing/nwb/utils.py
+++ b/src/dynamic_foraging_processing/nwb/utils.py
@@ -38,7 +38,7 @@ def convert_values_in_nested_structure(
         return {
             k: convert_values_in_nested_structure(v, check_fn, convert_fn) for k, v in data.items()
         }
-    if isinstance(data, list):
+    if isinstance(data, (list, tuple)):
         return [convert_values_in_nested_structure(item, check_fn, convert_fn) for item in data]
     return convert_fn(data) if check_fn(data) else data
 
@@ -66,8 +66,8 @@ def convert_datetimes_to_iso_string(
     )
 
 
-def _dict_to_json(value: dict) -> str:
-    """JSON-encode a dict, converting nested enums/datetimes first."""
+def _to_json(value: Union[dict, list, tuple]) -> str:
+    """JSON-encode a dict/list/tuple, converting nested enums/datetimes first."""
     value = convert_values_in_nested_structure(
         value,
         check_fn=lambda x: isinstance(x, Enum),
@@ -77,9 +77,9 @@ def _dict_to_json(value: dict) -> str:
     return json.dumps(value, default=str)
 
 
-def clean_dataframe_for_nwb(data: Union[pd.DataFrame, dict, BaseModel]) -> pd.DataFrame:
+def clean_for_nwb(data: Union[pd.DataFrame, dict, BaseModel]) -> pd.DataFrame:
     """
-    Clean a pandas DataFrame to ensure compatibility with NWB format.
+    Clean input argument to ensure compatibility with NWB format.
 
     Parameters
     ----------
@@ -94,7 +94,9 @@ def clean_dataframe_for_nwb(data: Union[pd.DataFrame, dict, BaseModel]) -> pd.Da
         A cleaned DataFrame that adheres to NWB data types
     """
     if isinstance(data, BaseModel):
-        data = data.model_dump()
+        # mode="json" coerces non-JSON types (e.g. pathlib paths, datetimes) to
+        # JSON-safe values so they don't reach the NWB writer as raw objects.
+        data = data.model_dump(mode="json")
     if isinstance(data, dict):
         data = pd.DataFrame([data])
 
@@ -102,6 +104,19 @@ def clean_dataframe_for_nwb(data: Union[pd.DataFrame, dict, BaseModel]) -> pd.Da
         # convert to nwb allowable types
         data[column] = data[column].replace({None: np.nan})
         data[column] = data[column].apply(lambda x: x.value if isinstance(x, Enum) else x)
-        data[column] = data[column].apply(lambda x: _dict_to_json(x) if isinstance(x, dict) else x)
+        data[column] = data[column].apply(
+            lambda x: _to_json(x) if isinstance(x, (dict, list, tuple)) else x
+        )
+
+    # DynamicTable reserves these names for its own fields, so a data column
+    # sharing one clashes on write: ``description``/``colnames`` are serialized
+    # as group attributes and hard-fail ("cannot set in attributes"), while
+    # ``id``/``name``/``columns`` shadow table attributes and warn. Suffix any
+    # such column with a trailing underscore -- the idiomatic disambiguation for
+    # a name that shadows a reserved one.
+    reserved = {"id", "name", "description", "colnames", "columns", "data_type"}
+    rename = {column: f"{column}_" for column in data.columns if column in reserved}
+    if rename:
+        data = data.rename(columns=rename)
 
     return data
diff --git a/src/dynamic_foraging_processing/pipeline/__init__.py b/src/dynamic_foraging_processing/pipeline/__init__.py
new file mode 100644
index 0000000..97ef637
--- /dev/null
+++ b/src/dynamic_foraging_processing/pipeline/__init__.py
@@ -0,0 +1,5 @@
+"""Pipeline module: package raw dynamic foraging data to NWB and run QC."""
+
+from dynamic_foraging_processing.pipeline._pipeline import Pipeline
+
+__all__ = ["Pipeline"]
diff --git a/src/dynamic_foraging_processing/pipeline/_pipeline.py b/src/dynamic_foraging_processing/pipeline/_pipeline.py
new file mode 100644
index 0000000..0796cdc
--- /dev/null
+++ b/src/dynamic_foraging_processing/pipeline/_pipeline.py
@@ -0,0 +1,453 @@
+"""Dynamic foraging pipeline entry points.
+
+Two Code Ocean capsules drive this module, so it exposes two independent entry
+points over the shared building blocks rather than one combined flow:
+
+- :meth:`Pipeline.run_nwb` -- assemble the NWB file (base metadata + acquisition
+  entries + trials table), write it to disk, and write the ``processing.json``
+  ``aind-data-schema`` metadata alongside it.
+- :meth:`Pipeline.run_qc` -- run the raw QC stage (over the loader's raw dataset)
+  and the processed QC stage (over a given NWB file) and write the combined
+  ``quality_control.json`` to disk.
+
+The individual builders produce NWB-ready pydantic models
+(``AcquisitionSeries`` / ``AcquisitionTable``) and a trials ``DataFrame``; this
+module translates those into ``pynwb`` objects on top of the base file created
+by :func:`aind_nwb_utils.utils.create_base_nwb_file`, then writes the result
+with the Zarr backend.
+"""
+
+import os
+import typing as t
+from datetime import datetime, timezone
+from importlib.metadata import version
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pynwb
+from aind_data_schema.core.processing import (
+    Code,
+    DataProcess,
+    Processing,
+    ProcessName,
+    ProcessStage,
+)
+from aind_data_schema.core.quality_control import QualityControl
+from aind_nwb_utils.utils import create_base_nwb_file
+from hdmf.common import DynamicTable
+from hdmf_zarr.nwb import NWBZarrIO
+
+from dynamic_foraging_processing.nwb.acquisition import (
+    AcquisitionBuilder,
+    AcquisitionSeries,
+    AcquisitionTable,
+)
+from dynamic_foraging_processing.nwb.acquisition.acquisition_builder import LickSource
+from dynamic_foraging_processing.processing import TrialConfig, TrialTableBuilder
+from dynamic_foraging_processing.qc import ProcessedQC, RawQC, build_quality_control
+from dynamic_foraging_processing.raw_data_loader import RawDataLoader
+
+#: Default lick-port sources on the standard behavior board.
+_DEFAULT_LEFT_LICK = LickSource("HarpBehavior", "DigitalInputState", "DIPort0")
+_DEFAULT_RIGHT_LICK = LickSource("HarpBehavior", "DigitalInputState", "DIPort1")
+
+#: Trials-table columns NWB models natively; every other column is added as an
+#: extra trial column.
+_TRIAL_TIME_COLUMNS = ("start_time", "stop_time")
+
+#: Source repository recorded in the ``processing.json`` data process.
+_CODE_URL = "https://github.com/AllenNeuralDynamics/dynamic-foraging-processing"
+
+#: Installed version of this package, recorded on the processing ``Code``.
+_PACKAGE_VERSION = version("dynamic-foraging-processing")
+
+#: Filename of the NWB (Zarr) store written under the output directory.
+_NWB_FILENAME = "behavior.nwb.zarr"
+
+#: Acquisition-series names read back for the processed QC stage.
+_LEFT_LICK_SERIES = "left_lick_time"
+_RIGHT_LICK_SERIES = "right_lick_time"
+_LEFT_REWARD_SERIES = "left_reward_delivery_time"
+_RIGHT_REWARD_SERIES = "right_reward_delivery_time"
+
+#: Reward-delivery annotation marking a manual-water event.
+_MANUAL_ANNOTATION = "manual"
+
+
+class Pipeline:
+    """Package a raw dynamic foraging acquisition to NWB and run QC.
+
+    :meth:`run_nwb` and :meth:`run_qc` are the two entry points (one per Code
+    Ocean capsule); the ``build_*`` / :meth:`write` methods are the lower-level
+    building blocks they compose from, exposed for notebooks and debugging.
+
+    The pipeline reuses the existing builders: :class:`AcquisitionBuilder` for
+    the acquisition entries and lick / manual-water event times,
+    :class:`TrialTableBuilder` for the trials table, and the :class:`RawQC` /
+    :class:`ProcessedQC` stages assembled into a single ``QualityControl``.
+    """
+
+    def __init__(
+        self,
+        loader: RawDataLoader,
+        *,
+        left_lick: LickSource = _DEFAULT_LEFT_LICK,
+        right_lick: LickSource = _DEFAULT_RIGHT_LICK,
+        raise_on_error: bool = False,
+    ):
+        """Initialize the pipeline.
+
+        Parameters
+        ----------
+        loader : RawDataLoader
+            Loader providing access to the raw dynamic foraging dataset. Its
+            ``path`` must hold the acquisition metadata files
+            (``acquisition.json`` / ``session.json``, ``subject.json``, ...)
+            that seed the base NWB file.
+        left_lick, right_lick : LickSource, optional
+            Where to read each side's lick times. Defaults to the standard
+            behavior board (``HarpBehavior``/``DigitalInputState`` on
+            ``DIPort0``/``DIPort1``). For the lickometer board pass, e.g.,
+            ``LickSource("HarpLickometerLeft", "LickState", "Channel0")``.
+        raise_on_error : bool, optional
+            If ``True``, propagate errors raised while loading streams instead
+            of logging and continuing. Passed through to the trials-table
+            builder. Defaults to ``False``.
+        """
+        self.loader = loader
+        self.left_lick = left_lick
+        self.right_lick = right_lick
+        self.raise_on_error = raise_on_error
+        self._acquisition_builder = AcquisitionBuilder(loader)
+        self._trial_table_builder = TrialTableBuilder(loader.dataset, raise_on_error=raise_on_error)
+
+    # ------------------------------------------------------------------ #
+    # Builders
+    # ------------------------------------------------------------------ #
+    def build_acquisition(self) -> t.List[t.Union[AcquisitionSeries, AcquisitionTable]]:
+        """Build the NWB acquisition entries.
+
+        Returns
+        -------
+        list of AcquisitionSeries or AcquisitionTable
+            Acquisition entries to write to the NWB acquisition module.
+        """
+        return self._acquisition_builder.build_acquisition(
+            left_lick=self.left_lick,
+            right_lick=self.right_lick,
+        )
+
+    def build_trials(self) -> pd.DataFrame:
+        """Build the trials table.
+
+        Returns
+        -------
+        pandas.DataFrame
+            One row per trial.
+        """
+        return self._trial_table_builder.build()
+
+    def build_nwb(
+        self,
+        acquisition: t.Optional[t.List[t.Union[AcquisitionSeries, AcquisitionTable]]] = None,
+        trials: t.Optional[pd.DataFrame] = None,
+    ) -> pynwb.NWBFile:
+        """Assemble the NWB file: base metadata, acquisition entries, and trials.
+
+        The base file (subject / session metadata) is created from the loader's
+        metadata directory via :func:`create_base_nwb_file`; the acquisition
+        entries and trials table are then added onto it.
+
+        Parameters
+        ----------
+        acquisition : list of AcquisitionSeries or AcquisitionTable, optional
+            Acquisition entries to add. Built via :meth:`build_acquisition` when
+            omitted.
+        trials : pandas.DataFrame, optional
+            Trials table to add. Built via :meth:`build_trials` when omitted.
+
+        Returns
+        -------
+        pynwb.NWBFile
+            The assembled NWB file.
+        """
+        if acquisition is None:
+            acquisition = self.build_acquisition()
+        if trials is None:
+            trials = self.build_trials()
+
+        nwb_file = create_base_nwb_file(self.loader.path)
+        for entry in acquisition:
+            if isinstance(entry, AcquisitionSeries):
+                self._add_acquisition_series(nwb_file, entry)
+            else:
+                self._add_acquisition_table(nwb_file, entry)
+        self._add_trials(nwb_file, trials)
+        return nwb_file
+
+    # ------------------------------------------------------------------ #
+    # NWB assembly helpers
+    # ------------------------------------------------------------------ #
+    @staticmethod
+    def _add_acquisition_series(nwb_file: pynwb.NWBFile, series: AcquisitionSeries) -> None:
+        """Add one ``AcquisitionSeries`` to the NWB acquisition module."""
+        nwb_file.add_acquisition(
+            pynwb.TimeSeries(
+                name=series.name,
+                data=np.asarray(series.data),
+                timestamps=np.asarray(series.timestamps),
+                unit=series.unit,
+                description=series.description,
+            )
+        )
+
+    @staticmethod
+    def _add_acquisition_table(nwb_file: pynwb.NWBFile, table: AcquisitionTable) -> None:
+        """Add one ``AcquisitionTable`` to the NWB acquisition module.
+
+        The stream's timestamp index is reset into an explicit ``timestamp``
+        column so it is preserved as data rather than consumed as the table's
+        auto-generated ``id``.
+        """
+        frame = table.data.reset_index(names="timestamp")
+        nwb_file.add_acquisition(
+            DynamicTable.from_dataframe(
+                df=frame,
+                name=table.name,
+                table_description=table.description,
+            )
+        )
+
+    @staticmethod
+    def _add_trials(nwb_file: pynwb.NWBFile, trials: pd.DataFrame) -> None:
+        """Add the trials table to the NWB file's native ``trials`` table.
+
+        ``start_time`` / ``stop_time`` are modeled natively by NWB; every other
+        column is registered as an extra trial column (described by the matching
+        :class:`TrialConfig` field) and populated per row. The DataFrame index
+        (named ``id``) is replicated as each trial's NWB ``id``. An empty table
+        (or one missing the required time columns) is skipped.
+        """
+        if trials.empty or any(col not in trials.columns for col in _TRIAL_TIME_COLUMNS):
+            return
+        descriptions = TrialConfig.column_descriptions()
+        extra_columns = [col for col in trials.columns if col not in _TRIAL_TIME_COLUMNS]
+        for column in extra_columns:
+            nwb_file.add_trial_column(name=column, description=descriptions.get(column, column))
+        for row_id, row in trials.iterrows():
+            nwb_file.add_trial(id=int(row_id), **{column: row[column] for column in trials.columns})
+
+    # ------------------------------------------------------------------ #
+    # Writers
+    # ------------------------------------------------------------------ #
+    def write(self, nwb_file: pynwb.NWBFile, output_path: t.Union[str, os.PathLike]) -> None:
+        """Write an NWB file to disk using the Zarr backend.
+
+        Parameters
+        ----------
+        nwb_file : pynwb.NWBFile
+            The NWB file to write (e.g. from :meth:`build_nwb`).
+        output_path : os.PathLike
+            Destination path for the ``.nwb.zarr`` store.
+        """
+        with NWBZarrIO(output_path, mode="w") as io:
+            io.write(nwb_file)
+
+    def _write_processing(
+        self,
+        output_path: t.Union[str, os.PathLike],
+        start_date_time: datetime,
+        end_date_time: datetime,
+    ) -> None:
+        """Write the ``processing.json`` metadata for the NWB packaging step.
+
+        Parameters
+        ----------
+        output_path : os.PathLike
+            Directory the ``processing.json`` is written into.
+        start_date_time, end_date_time : datetime
+            When the NWB packaging started and finished.
+        """
+        processing = Processing(
+            data_processes=[
+                DataProcess(
+                    name="Dynamic foraging NWB packaging",
+                    process_type=ProcessName.PIPELINE,
+                    stage=ProcessStage.PROCESSING,
+                    code=Code(url=_CODE_URL, version=_PACKAGE_VERSION),
+                    experimenters=["Alex Piet", "Micah Woodard", "Bruno Cruz", "Arjun Sridhar"],
+                    start_date_time=start_date_time,
+                    end_date_time=end_date_time,
+                )
+            ]
+        )
+        processing.write_standard_file(output_directory=Path(output_path))
+
+    # ------------------------------------------------------------------ #
+    # QC helpers
+    # ------------------------------------------------------------------ #
+    def _read_processed_inputs(
+        self, nwb_file: pynwb.NWBFile
+    ) -> t.Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+        """Read the processed-QC inputs from an NWB file.
+
+        Parameters
+        ----------
+        nwb_file : pynwb.NWBFile
+            The NWB file to read the trials table and acquisition series from.
+
+        Returns
+        -------
+        tuple
+            ``(trials, left_lick_times, right_lick_times, manual_left_times,
+            manual_right_times)``.
+        """
+        trials = nwb_file.trials.to_dataframe()
+        left_lick_times = np.asarray(nwb_file.acquisition[_LEFT_LICK_SERIES].timestamps)
+        right_lick_times = np.asarray(nwb_file.acquisition[_RIGHT_LICK_SERIES].timestamps)
+        manual_left_times, manual_right_times = self._manual_water_times(nwb_file)
+        return trials, left_lick_times, right_lick_times, manual_left_times, manual_right_times
+
+    @staticmethod
+    def _manual_water_times(nwb_file: pynwb.NWBFile) -> t.Tuple[np.ndarray, np.ndarray]:
+        """Return the ``(left, right)`` manual-water delivery times from the NWB.
+
+        Manual-water deliveries are the reward-delivery events annotated
+        ``"manual"`` on each side's ``*_reward_delivery_time`` acquisition series.
+        """
+        left = Pipeline._annotated_times(nwb_file, _LEFT_REWARD_SERIES, _MANUAL_ANNOTATION)
+        right = Pipeline._annotated_times(nwb_file, _RIGHT_REWARD_SERIES, _MANUAL_ANNOTATION)
+        return left, right
+
+    @staticmethod
+    def _annotated_times(nwb_file: pynwb.NWBFile, series_name: str, annotation: str) -> np.ndarray:
+        """Return timestamps of ``series_name`` whose annotation ``data`` equals ``annotation``."""
+        series = nwb_file.acquisition[series_name]
+        data = np.asarray(series.data)
+        timestamps = np.asarray(series.timestamps)
+        return timestamps[data == annotation]
+
+    def _assemble_quality_control(
+        self,
+        trials: pd.DataFrame,
+        left_lick_times: np.ndarray,
+        right_lick_times: np.ndarray,
+        manual_left_times: np.ndarray,
+        manual_right_times: np.ndarray,
+        results_folder: t.Optional[str] = None,
+    ) -> QualityControl:
+        """Run the raw and processed QC stages and assemble one ``QualityControl``.
+
+        Parameters
+        ----------
+        trials : pandas.DataFrame
+            The trials table, consumed by the processed (behavior) QC stage.
+        left_lick_times, right_lick_times : numpy.ndarray
+            Left/right-port lick times for the processed QC stage.
+        manual_left_times, manual_right_times : numpy.ndarray
+            Left/right manual-water delivery times passed through to the side-bias
+            figure.
+        results_folder : str, optional
+            Directory to write figure assets into so the metric references
+            resolve. If ``None``, assets are skipped.
+
+        Returns
+        -------
+        QualityControl
+            The combined raw (contract QA) + processed (behavior) metrics as a
+            single flat metric list.
+        """
+        raw_metrics = RawQC().run(self.loader.dataset, results_folder)
+        processed_metrics = ProcessedQC().run(
+            trials,
+            left_lick_times,
+            right_lick_times,
+            results_folder,
+            manual_left_times=manual_left_times,
+            manual_right_times=manual_right_times,
+        )
+        return build_quality_control([*raw_metrics, *processed_metrics])
+
+    # ------------------------------------------------------------------ #
+    # Entry points (one per Code Ocean capsule)
+    # ------------------------------------------------------------------ #
+    def run_nwb(self, output_path: t.Optional[t.Union[str, os.PathLike]] = None) -> pynwb.NWBFile:
+        """Assemble the NWB file, optionally writing it plus its ``processing.json``.
+
+        Parameters
+        ----------
+        output_path : os.PathLike, optional
+            Output directory. The NWB store is written to
+            ``output_path / "behavior.nwb.zarr"`` and the ``processing.json`` to
+            ``output_path``. When ``None`` (the default), the NWB file is built
+            and returned without touching disk.
+
+        Returns
+        -------
+        pynwb.NWBFile
+            The assembled NWB file. When ``output_path`` is given, the NWB store
+            and ``processing.json`` are written to disk as a side effect.
+        """
+        start_date_time = datetime.now(timezone.utc)
+        acquisition = self.build_acquisition()
+        trials = self.build_trials()
+        nwb_file = self.build_nwb(acquisition, trials)
+        if output_path is not None:
+            self.write(nwb_file, Path(output_path) / _NWB_FILENAME)
+            self._write_processing(output_path, start_date_time, datetime.now(timezone.utc))
+        return nwb_file
+
+    def run_qc(
+        self,
+        nwb_file: pynwb.NWBFile,
+        output_path: t.Optional[t.Union[str, os.PathLike]] = None,
+        folder_directory: str = "qc",
+    ) -> QualityControl:
+        """Run the raw and processed QC stages, optionally writing to disk.
+
+        The raw (contract QA) stage runs over the loader's raw dataset; the
+        processed-QC inputs (trials table, lick times, manual-water times) are read
+        from ``nwb_file`` (e.g. the output of :meth:`run_nwb`).
+
+        Parameters
+        ----------
+        nwb_file : pynwb.NWBFile
+            The NWB file to run QC against.
+        output_path : os.PathLike, optional
+            Destination directory for ``quality_control.json``. When ``None`` (the
+            default), the QC is assembled and returned without touching disk
+            (figure assets are skipped too).
+        folder_directory : str, optional
+            Subdirectory of ``output_path`` that the QC figure assets (plots, ...)
+            are written into, i.e. ``output_path / folder_directory``. Defaults to
+            ``"qc"``. Ignored when ``output_path`` is ``None``.
+
+        Returns
+        -------
+        QualityControl
+            The combined raw + processed quality-control object. When
+            ``output_path`` is given, the QC JSON and figure assets are written to
+            disk as a side effect.
+        """
+        trials, left_lick_times, right_lick_times, manual_left_times, manual_right_times = (
+            self._read_processed_inputs(nwb_file)
+        )
+
+        results_folder: t.Optional[str] = None
+        if output_path is not None:
+            artifacts_dir = Path(output_path) / folder_directory
+            artifacts_dir.mkdir(parents=True, exist_ok=True)
+            results_folder = os.fspath(artifacts_dir)
+
+        quality_control = self._assemble_quality_control(
+            trials,
+            left_lick_times,
+            right_lick_times,
+            manual_left_times,
+            manual_right_times,
+            results_folder,
+        )
+        if output_path is not None:
+            quality_control.write_standard_file(output_directory=Path(output_path))
+        return quality_control
diff --git a/tests/test_nwb/test_utils.py b/tests/test_nwb/test_utils.py
index 3b35109..aa5bf6a 100644
--- a/tests/test_nwb/test_utils.py
+++ b/tests/test_nwb/test_utils.py
@@ -3,13 +3,14 @@
 import json
 from datetime import datetime
 from enum import Enum
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
 from pydantic import BaseModel
 
 from dynamic_foraging_processing.nwb.utils import (
-    clean_dataframe_for_nwb,
+    clean_for_nwb,
     convert_datetimes_to_iso_string,
     convert_values_in_nested_structure,
 )
@@ -56,7 +57,7 @@ def test_clean_dataframe_replaces_none_with_nan():
     """``None`` values become ``NaN`` for NWB compatibility."""
     df = pd.DataFrame({"x": [1.0, None, 3.0]})
 
-    cleaned = clean_dataframe_for_nwb(df)
+    cleaned = clean_for_nwb(df)
 
     assert cleaned["x"].isna().tolist() == [False, True, False]
     assert cleaned["x"].dropna().tolist() == [1.0, 3.0]
@@ -66,7 +67,7 @@ def test_clean_dataframe_unwraps_enums():
     """Enum cells are replaced by their ``.value``."""
     df = pd.DataFrame({"side": [_Side.LEFT, _Side.RIGHT]})
 
-    cleaned = clean_dataframe_for_nwb(df)
+    cleaned = clean_for_nwb(df)
 
     assert cleaned["side"].tolist() == ["left", "right"]
 
@@ -75,16 +76,45 @@ def test_clean_dataframe_serializes_dicts_with_datetimes():
     """Dict cells are JSON-encoded with nested datetimes converted to ISO."""
     df = pd.DataFrame({"payload": [{"t": datetime(2026, 6, 30), "n": 1}]})
 
-    cleaned = clean_dataframe_for_nwb(df)
+    cleaned = clean_for_nwb(df)
 
     assert cleaned["payload"].iloc[0] == json.dumps({"t": "2026-06-30T00:00:00", "n": 1})
 
 
+def test_convert_values_in_nested_structure_recurses_tuples():
+    """Conversion reaches values nested inside tuples, returning a list."""
+    result = convert_values_in_nested_structure(
+        (1, 2, [3, 4]),
+        check_fn=lambda x: isinstance(x, int) and x % 2 == 0,
+        convert_fn=lambda x: x * 10,
+    )
+
+    assert result == [1, 20, [3, 40]]
+
+
+def test_clean_dataframe_serializes_lists_with_enums():
+    """List cells are JSON-encoded with nested enums converted to their value."""
+    df = pd.DataFrame({"payload": [[_Side.LEFT, 1]]})
+
+    cleaned = clean_for_nwb(df)
+
+    assert cleaned["payload"].iloc[0] == json.dumps(["left", 1])
+
+
+def test_clean_dataframe_serializes_tuples_with_datetimes():
+    """Tuple cells are JSON-encoded (as arrays) with nested datetimes as ISO."""
+    df = pd.DataFrame({"payload": [(datetime(2026, 6, 30), "x")]})
+
+    cleaned = clean_for_nwb(df)
+
+    assert cleaned["payload"].iloc[0] == json.dumps(["2026-06-30T00:00:00", "x"])
+
+
 def test_clean_dataframe_accepts_dict_as_single_row():
     """A dict input becomes a one-row DataFrame with cleaned values."""
     data = {"n": 1, "side": _Side.LEFT, "payload": {"t": datetime(2026, 6, 30), "k": _Side.RIGHT}}
 
-    cleaned = clean_dataframe_for_nwb(data)
+    cleaned = clean_for_nwb(data)
 
     assert isinstance(cleaned, pd.DataFrame)
     assert len(cleaned) == 1
@@ -103,7 +133,7 @@ class _Row(BaseModel):
         n: int
         side: _Side
 
-    cleaned = clean_dataframe_for_nwb(_Row(n=1, side=_Side.LEFT))
+    cleaned = clean_for_nwb(_Row(n=1, side=_Side.LEFT))
 
     assert isinstance(cleaned, pd.DataFrame)
     assert len(cleaned) == 1
@@ -111,11 +141,53 @@ class _Row(BaseModel):
     assert cleaned["side"].iloc[0] == "left"
 
 
+def test_clean_dataframe_pydantic_model_json_serializes_paths():
+    """Model dumping uses JSON mode so pathlib fields become strings, not objects."""
+
+    class _Row(BaseModel):
+        """Sample model row carrying a pathlib field."""
+
+        path: Path
+
+    cleaned = clean_for_nwb(_Row(path=Path("a") / "b"))
+
+    value = cleaned["path"].iloc[0]
+    assert isinstance(value, str)
+    assert value == str(Path("a", "b"))
+
+
+def test_clean_dataframe_suffixes_reserved_column_names():
+    """Columns clashing with DynamicTable's reserved field names are suffixed."""
+    df = pd.DataFrame(
+        {
+            "id": [1],
+            "name": ["a"],
+            "description": ["b"],
+            "colnames": ["c"],
+            "columns": ["d"],
+            "data_type": ["e"],
+            "value": [1],
+        }
+    )
+
+    cleaned = clean_for_nwb(df)
+
+    assert list(cleaned.columns) == [
+        "id_",
+        "name_",
+        "description_",
+        "colnames_",
+        "columns_",
+        "data_type_",
+        "value",
+    ]
+
+
 def test_clean_dataframe_leaves_plain_columns():
     """Scalar non-dict, non-enum columns are left as-is."""
     df = pd.DataFrame({"n": [1, 2, 3], "s": ["a", "b", "c"]})
 
-    cleaned = clean_dataframe_for_nwb(df)
+    cleaned = clean_for_nwb(df)
 
     assert cleaned["n"].tolist() == [1, 2, 3]
     assert cleaned["s"].tolist() == ["a", "b", "c"]
diff --git a/tests/test_pipeline/__init__.py b/tests/test_pipeline/__init__.py
new file mode 100644
index 0000000..6fb5b63
--- /dev/null
+++ b/tests/test_pipeline/__init__.py
@@ -0,0 +1 @@
+"""Tests for the pipeline module."""
diff --git a/tests/test_pipeline/test_pipeline.py b/tests/test_pipeline/test_pipeline.py
new file mode 100644
index 0000000..cef052f
--- /dev/null
+++ b/tests/test_pipeline/test_pipeline.py
@@ -0,0 +1,446 @@
+"""Tests for ``dynamic_foraging_processing.pipeline._pipeline``."""
+
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import numpy as np
+import pandas as pd
+from aind_data_schema.core.processing import Processing, ProcessName
+
+from dynamic_foraging_processing.nwb.acquisition.acquisition_builder import LickSource
+from dynamic_foraging_processing.nwb.acquisition.models import (
+    AcquisitionSeries,
+    AcquisitionTable,
+)
+from dynamic_foraging_processing.pipeline import _pipeline
+from dynamic_foraging_processing.pipeline._pipeline import Pipeline
+from dynamic_foraging_processing.processing.models import TrialConfig
+
+
+def _make_loader() -> MagicMock:
+    """Build a mock loader with a dataset and a metadata path."""
+    loader = MagicMock()
+    loader.dataset = object()
+    loader.path = Path("dataset")
+    return loader
+
+
+def _make_pipeline() -> Pipeline:
+    """Build a pipeline whose internal builders are replaced with mocks."""
+    pipeline = Pipeline(_make_loader())
+    pipeline._acquisition_builder = MagicMock()
+    pipeline._trial_table_builder = MagicMock()
+    return pipeline
+
+
+def _trials_frame() -> pd.DataFrame:
+    """Two-trial table with the time columns plus one modeled, one unmodeled column."""
+    return pd.DataFrame(
+        {
+            "start_time": [0.0, 1.0],
+            "stop_time": [0.5, 1.5],
+            "animal_response": [0, 1],
+            "not_in_model": [7, 8],
+        }
+    )
+
+
+class _FakeSeries:
+    """Stand-in for an NWB acquisition series exposing ``data``/``timestamps``."""
+
+    def __init__(self, data, timestamps):
+        """Store the series arrays."""
+        self.data = data
+        self.timestamps = timestamps
+
+
+def test_init_stores_config_and_builds_helpers():
+    """``__init__`` stores the loader/lick sources and constructs the builders."""
+    loader = _make_loader()
+    left = LickSource("HarpLickometerLeft", "LickState", "Channel0")
+
+    pipeline = Pipeline(loader, left_lick=left, raise_on_error=True)
+
+    assert pipeline.loader is loader
+    assert pipeline.left_lick is left
+    assert pipeline.right_lick == _pipeline._DEFAULT_RIGHT_LICK
+    assert pipeline.raise_on_error is True
+    assert pipeline._acquisition_builder.loader is loader
+    assert pipeline._trial_table_builder.dataset is loader.dataset
+    assert pipeline._trial_table_builder.raise_on_error is True
+
+
+def test_build_acquisition_delegates_with_lick_sources():
+    """``build_acquisition`` forwards the configured lick sources to the builder."""
+    pipeline = _make_pipeline()
+    pipeline._acquisition_builder.build_acquisition.return_value = ["entry"]
+
+    result = pipeline.build_acquisition()
+
+    assert result == ["entry"]
+    pipeline._acquisition_builder.build_acquisition.assert_called_once_with(
+        left_lick=pipeline.left_lick,
+        right_lick=pipeline.right_lick,
+    )
+
+
+def test_build_trials_delegates_to_builder():
+    """``build_trials`` returns the trials-table builder's frame."""
+    pipeline = _make_pipeline()
+    frame = _trials_frame()
+    pipeline._trial_table_builder.build.return_value = frame
+
+    assert pipeline.build_trials() is frame
+    pipeline._trial_table_builder.build.assert_called_once_with()
+
+
+def test_build_nwb_uses_provided_components(monkeypatch):
+    """Provided acquisition/trials are added onto the base NWB file."""
+    nwb_file = MagicMock()
+    monkeypatch.setattr(_pipeline, "create_base_nwb_file", lambda path: nwb_file)
+    pipeline = _make_pipeline()
+
+    acquisition = [
+        AcquisitionSeries(
+            name="left_lick_time",
+            data=np.array([True]),
+            timestamps=np.array([1.0]),
+            unit="second",
+            description="left licks",
+        ),
+        AcquisitionTable(
+            name="Behavior.RawStream",
+            data=pd.DataFrame({"x": [1, 2]}),
+            description="raw",
+        ),
+    ]
+    trials = _trials_frame()
+
+    result = pipeline.build_nwb(acquisition, trials)
+
+    assert result is nwb_file
+    # One series + one table added to acquisition; trials rows populated.
+    assert nwb_file.add_acquisition.call_count == 2
+    assert nwb_file.add_trial.call_count == len(trials)
+    # The internal builders are not called when components are supplied.
+    pipeline._acquisition_builder.build_acquisition.assert_not_called()
+    pipeline._trial_table_builder.build.assert_not_called()
+
+
+def test_build_nwb_builds_missing_components(monkeypatch):
+    """When omitted, acquisition and trials are built from the builders."""
+    nwb_file = MagicMock()
+    monkeypatch.setattr(_pipeline, "create_base_nwb_file", lambda path: nwb_file)
+    pipeline = _make_pipeline()
+    pipeline.build_acquisition = MagicMock(return_value=[])
+    pipeline.build_trials = MagicMock(return_value=_trials_frame())
+
+    pipeline.build_nwb()
+
+    pipeline.build_acquisition.assert_called_once_with()
+    pipeline.build_trials.assert_called_once_with()
+
+
+def test_write_uses_zarr_io(monkeypatch):
+    """``write`` opens ``NWBZarrIO`` in write mode and writes the file."""
+    captured = {}
+
+    class _FakeIO:
+        """Context-managed stand-in for ``NWBZarrIO``."""
+
+        def __init__(self, path, mode):
+            """Record the path and mode."""
+            captured["path"] = path
+            captured["mode"] = mode
+
+        def __enter__(self):
+            """Enter the context."""
+            return self
+
+        def __exit__(self, *exc):
+            """Exit the context without suppressing exceptions."""
+            return False
+
+        def write(self, nwb_file):
+            """Record the written file."""
+            captured["written"] = nwb_file
+
+    monkeypatch.setattr(_pipeline, "NWBZarrIO", _FakeIO)
+    pipeline = _make_pipeline()
+    nwb_file = object()
+
+    result = pipeline.write(nwb_file, "out.nwb.zarr")
+
+    assert result is None
+    assert captured == {"path": "out.nwb.zarr", "mode": "w", "written": nwb_file}
+
+
+def test_assemble_quality_control_combines_metrics_into_single_list(monkeypatch):
+    """``_assemble_quality_control`` flattens raw and processed metrics into one list."""
+    captured = {}
+
+    class _FakeRawQC:
+        """Raw QC stub returning two sentinel metrics."""
+
+        def run(self, dataset, results_folder):
+            """Record args and return raw metrics."""
+            captured["raw"] = (dataset, results_folder)
+            return ["raw1", "raw2"]
+
+    class _FakeProcessedQC:
+        """Processed QC stub returning one sentinel metric."""
+
+        def run(
+            self, trials, left, right, results_folder, *, manual_left_times, manual_right_times
+        ):
+            """Record args and return processed metrics."""
+            captured["processed"] = {
+                "left": left,
+                "right": right,
+                "results_folder": results_folder,
+                "manual_left": manual_left_times,
+                "manual_right": manual_right_times,
+            }
+            return ["proc1"]
+
+    monkeypatch.setattr(_pipeline, "RawQC", _FakeRawQC)
+    monkeypatch.setattr(_pipeline, "ProcessedQC", _FakeProcessedQC)
+    monkeypatch.setattr(_pipeline, "build_quality_control", lambda metrics: metrics)
+
+    pipeline = _make_pipeline()
+    trials = _trials_frame()
+
+    result = pipeline._assemble_quality_control(
+        trials,
+        np.array([1.0]),
+        np.array([2.0]),
+        np.array([0.1]),
+        np.array([0.2]),
+        "out",
+    )
+
+    assert result == ["raw1", "raw2", "proc1"]
+    assert captured["raw"] == (pipeline.loader.dataset, "out")
+    np.testing.assert_array_equal(captured["processed"]["left"], np.array([1.0]))
+    np.testing.assert_array_equal(captured["processed"]["right"], np.array([2.0]))
+    np.testing.assert_array_equal(captured["processed"]["manual_left"], np.array([0.1]))
+    np.testing.assert_array_equal(captured["processed"]["manual_right"], np.array([0.2]))
+
+
+def test_add_acquisition_series_builds_time_series():
+    """``_add_acquisition_series`` adds a ``TimeSeries`` mirroring the model."""
+    nwb_file = MagicMock()
+    series = AcquisitionSeries(
+        name="left_lick_time",
+        data=np.array([True, False]),
+        timestamps=np.array([1.0, 2.0]),
+        unit="second",
+        description="left licks",
+    )
+
+    Pipeline._add_acquisition_series(nwb_file, series)
+
+    (added,), _ = nwb_file.add_acquisition.call_args
+    assert added.name == "left_lick_time"
+    assert added.unit == "second"
+    np.testing.assert_array_equal(added.timestamps, np.array([1.0, 2.0]))
+
+
+def test_add_acquisition_table_builds_dynamic_table():
+    """``_add_acquisition_table`` resets the timestamp index into a column."""
+    nwb_file = MagicMock()
+    table = AcquisitionTable(
+        name="Behavior.RawStream",
+        data=pd.DataFrame({"x": [1, 2], "y": [3, 4]}, index=pd.Index([0.1, 0.2], name="Time")),
+        description="raw stream",
+    )
+
+    Pipeline._add_acquisition_table(nwb_file, table)
+
+    (added,), _ = nwb_file.add_acquisition.call_args
+    assert added.name == "Behavior.RawStream"
+    assert added.description == "raw stream"
+    assert len(added) == 2
+    # The former index is preserved as a ``timestamp`` column, not the row id.
+    assert "timestamp" in added.colnames
+    assert list(added["timestamp"].data) == [0.1, 0.2]
+
+
+def test_add_trials_populates_columns_and_rows():
+    """Extra columns are described via ``TrialConfig``; rows are added."""
+    nwb_file = MagicMock()
+    trials = _trials_frame()
+
+    Pipeline._add_trials(nwb_file, trials)
+
+    added_columns = {
+        call.kwargs["name"]: call.kwargs["description"]
+        for call in nwb_file.add_trial_column.call_args_list
+    }
+    # start_time / stop_time are native and not registered as extra columns.
+    assert set(added_columns) == {"animal_response", "not_in_model"}
+    assert added_columns["animal_response"] == TrialConfig.column_descriptions()["animal_response"]
+    # A column absent from TrialConfig falls back to its own name as description.
+    assert added_columns["not_in_model"] == "not_in_model"
+    assert nwb_file.add_trial.call_count == 2
+    # The DataFrame index is replicated as each trial's NWB id.
+    assert [call.kwargs["id"] for call in nwb_file.add_trial.call_args_list] == [0, 1]
+
+
+def test_add_trials_skips_empty_frame():
+    """An empty trials table adds nothing."""
+    nwb_file = MagicMock()
+
+    Pipeline._add_trials(nwb_file, pd.DataFrame())
+
+    nwb_file.add_trial_column.assert_not_called()
+    nwb_file.add_trial.assert_not_called()
+
+
+def test_add_trials_skips_when_time_columns_missing():
+    """A table lacking start/stop time columns is skipped."""
+    nwb_file = MagicMock()
+    trials = pd.DataFrame({"animal_response": [0, 1]})
+
+    Pipeline._add_trials(nwb_file, trials)
+
+    nwb_file.add_trial_column.assert_not_called()
+    nwb_file.add_trial.assert_not_called()
+
+
+def test_manual_water_times_reads_manual_annotations():
+    """Manual-water times are the reward events annotated ``"manual"`` per side."""
+    nwb_file = MagicMock()
+    nwb_file.acquisition = {
+        "left_reward_delivery_time": _FakeSeries(
+            np.array(["manual", "earned", "manual"]), np.array([0.1, 0.2, 0.3])
+        ),
+        "right_reward_delivery_time": _FakeSeries(np.array(["automatic"]), np.array([0.5])),
+    }
+
+    left, right = Pipeline._manual_water_times(nwb_file)
+
+    np.testing.assert_array_equal(left, np.array([0.1, 0.3]))
+    assert right.size == 0
+
+
+def test_read_processed_inputs_reads_from_nwb():
+    """The trials table, lick times, and manual-water times are read from the NWB."""
+    trials = _trials_frame()
+    fake_nwb = MagicMock()
+    fake_nwb.trials.to_dataframe.return_value = trials
+    fake_nwb.acquisition = {
+        "left_lick_time": _FakeSeries(np.array([True]), np.array([1.0])),
+        "right_lick_time": _FakeSeries(np.array([True, True]), np.array([2.0, 2.5])),
+        "left_reward_delivery_time": _FakeSeries(
+            np.array(["manual", "earned"]), np.array([0.1, 0.2])
+        ),
+        "right_reward_delivery_time": _FakeSeries(np.array(["manual"]), np.array([0.3])),
+    }
+
+    pipeline = _make_pipeline()
+
+    got_trials, left, right, manual_left, manual_right = pipeline._read_processed_inputs(fake_nwb)
+
+    assert got_trials is trials
+    np.testing.assert_array_equal(left, np.array([1.0]))
+    np.testing.assert_array_equal(right, np.array([2.0, 2.5]))
+    np.testing.assert_array_equal(manual_left, np.array([0.1]))
+    np.testing.assert_array_equal(manual_right, np.array([0.3]))
+
+
+def test_run_nwb_writes_nwb_and_processing(tmp_path):
+    """``run_nwb`` writes the NWB store and a valid ``processing.json``."""
+    pipeline = _make_pipeline()
+    acquisition = ["entry"]
+    trials = _trials_frame()
+    nwb_file = object()
+
+    pipeline.build_acquisition = MagicMock(return_value=acquisition)
+    pipeline.build_trials = MagicMock(return_value=trials)
+    pipeline.build_nwb = MagicMock(return_value=nwb_file)
+    pipeline.write = MagicMock()
+
+    result = pipeline.run_nwb(str(tmp_path))
+
+    assert result is nwb_file
+    pipeline.build_nwb.assert_called_once_with(acquisition, trials)
+    pipeline.write.assert_called_once_with(nwb_file, tmp_path / "behavior.nwb.zarr")
+    # processing.json is written and round-trips through the schema.
+    processing_json = tmp_path / "processing.json"
+    assert processing_json.exists()
+    loaded = Processing.model_validate_json(processing_json.read_text())
+    assert loaded.data_processes[0].process_type == ProcessName.PIPELINE
+
+
+def test_run_qc_writes_quality_control(tmp_path):
+    """``run_qc`` reads the NWB, assembles into a subfolder, and writes the JSON."""
+    pipeline = _make_pipeline()
+    nwb_file = object()
+    inputs = (
+        _trials_frame(),
+        np.array([1.0]),
+        np.array([2.0]),
+        np.array([0.1]),
+        np.array([0.2]),
+    )
+    quality_control = MagicMock()
+
+    pipeline._read_processed_inputs = MagicMock(return_value=inputs)
+    pipeline._assemble_quality_control = MagicMock(return_value=quality_control)
+
+    result = pipeline.run_qc(nwb_file, str(tmp_path), folder_directory="plots")
+
+    assert result is quality_control
+    pipeline._read_processed_inputs.assert_called_once_with(nwb_file)
+    # Figure assets go to output_path / folder_directory, which is created.
+    artifacts_dir = tmp_path / "plots"
+    assert artifacts_dir.is_dir()
+    args = pipeline._assemble_quality_control.call_args.args
+    # The read inputs are forwarded unchanged (same objects), then the folder.
+    assert [args[i] is inputs[i] for i in range(5)] == [True] * 5
+    assert args[5] == str(artifacts_dir)
+    quality_control.write_standard_file.assert_called_once_with(output_directory=Path(tmp_path))
+
+
+def test_run_nwb_without_output_skips_writes():
+    """``run_nwb`` returns the NWB file and touches no disk when no path is given."""
+    pipeline = _make_pipeline()
+    nwb_file = object()
+
+    pipeline.build_acquisition = MagicMock(return_value=["entry"])
+    pipeline.build_trials = MagicMock(return_value=_trials_frame())
+    pipeline.build_nwb = MagicMock(return_value=nwb_file)
+    pipeline.write = MagicMock()
+    pipeline._write_processing = MagicMock()
+
+    result = pipeline.run_nwb()
+
+    assert result is nwb_file
+    pipeline.write.assert_not_called()
+    pipeline._write_processing.assert_not_called()
+
+
+def test_run_qc_without_output_skips_write():
+    """``run_qc`` assembles with no results folder and skips the JSON write."""
+    pipeline = _make_pipeline()
+    nwb_file = object()
+    inputs = (
+        _trials_frame(),
+        np.array([1.0]),
+        np.array([2.0]),
+        np.array([]),
+        np.array([]),
+    )
+    quality_control = MagicMock()
+
+    pipeline._read_processed_inputs = MagicMock(return_value=inputs)
+    pipeline._assemble_quality_control = MagicMock(return_value=quality_control)
+
+    result = pipeline.run_qc(nwb_file)
+
+    assert result is quality_control
+    pipeline._read_processed_inputs.assert_called_once_with(nwb_file)
+    # No output_path -> results_folder is None and nothing is written.
+    assert pipeline._assemble_quality_control.call_args.args[5] is None
+    quality_control.write_standard_file.assert_not_called()
diff --git a/uv.lock b/uv.lock
index 3f9b950..6e5b612 100644
--- a/uv.lock
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 ]
 
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