diff --git a/README.md b/README.md
index bba87b0..257e97a 100644
--- a/README.md
+++ b/README.md
@@ -16,3 +16,8 @@ When using this code, please cite: [![DOI:10.1016/j.aca.2023.340789](http://img.
 [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
 [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
 [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
+
+## Project handover package
+
+A standalone refactor of the retention-model code is available in `chromatogram_handover/`.
+It is structured as an installable mini-repository focused on computing chromatograms from retention parameters and gradient programs.
diff --git a/chromatogram_handover/.gitignore b/chromatogram_handover/.gitignore
new file mode 100644
index 0000000..41c33c8
--- /dev/null
+++ b/chromatogram_handover/.gitignore
@@ -0,0 +1,3 @@
+
+__pycache__/
+*.pyc
diff --git a/chromatogram_handover/README.md b/chromatogram_handover/README.md
new file mode 100644
index 0000000..b112c3c
--- /dev/null
+++ b/chromatogram_handover/README.md
@@ -0,0 +1,52 @@
+# chromatogram-handover
+
+This folder is a standalone, handover-friendly Python package extracted from
+`rm_code` in `Jimbo994/AutoLC-BO`.
+
+## Goal
+
+Make it straightforward to compute chromatograms from:
+- retention parameters (`k0`, `S`), and
+- gradient programs (`phi`, `time`, `t_init`),
+
+without navigating the full research repository.
+
+## Install (editable)
+
+```bash
+pip install -e .
+```
+
+## Minimal usage
+
+```python
+from chromatogram_handover import (
+    GradientProgram,
+    compute_chromatogram,
+    load_retention_parameters,
+)
+
+params = load_retention_parameters("../data/RetentionParams_Section4.5.txt")
+gradient = GradientProgram(
+    phi=(0.1, 0.4, 0.8),
+    time=(0.0, 15.0, 30.0),
+    t_init=0.5,
+)
+
+chrom = compute_chromatogram(
+    parameters=params,
+    gradient=gradient,
+    t0=0.8,
+    dwell_time=0.12,
+    n_theoretical=25000,
+)
+
+print(chrom.retention_times)
+print(chrom.peak_widths)
+```
+
+## Notes for handover
+
+- `chromatogram_handover.model` contains the refactored multisegment retention model.
+- Data classes (`GradientProgram`, `RetentionParameters`, `Chromatogram`) provide an explicit API.
+- Input validation is built into the gradient and retention parameter constructors.
diff --git a/chromatogram_handover/pyproject.toml b/chromatogram_handover/pyproject.toml
new file mode 100644
index 0000000..5d95b4d
--- /dev/null
+++ b/chromatogram_handover/pyproject.toml
@@ -0,0 +1,15 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "chromatogram-handover"
+version = "0.1.0"
+description = "Refactored chromatogram simulator extracted from AutoLC-BO rm_code"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = ["numpy>=1.24"]
+
+[tool.pytest.ini_options]
+pythonpath = ["src"]
+addopts = "-q"
diff --git a/chromatogram_handover/src/chromatogram_handover/__init__.py b/chromatogram_handover/src/chromatogram_handover/__init__.py
new file mode 100644
index 0000000..0b14fcd
--- /dev/null
+++ b/chromatogram_handover/src/chromatogram_handover/__init__.py
@@ -0,0 +1,17 @@
+from .io import load_retention_parameters
+from .model import (
+    Chromatogram,
+    GradientProgram,
+    RetentionParameters,
+    compute_chromatogram,
+    retention_time_and_width,
+)
+
+__all__ = [
+    "Chromatogram",
+    "GradientProgram",
+    "RetentionParameters",
+    "compute_chromatogram",
+    "retention_time_and_width",
+    "load_retention_parameters",
+]
diff --git a/chromatogram_handover/src/chromatogram_handover/io.py b/chromatogram_handover/src/chromatogram_handover/io.py
new file mode 100644
index 0000000..2c14bf6
--- /dev/null
+++ b/chromatogram_handover/src/chromatogram_handover/io.py
@@ -0,0 +1,17 @@
+"""I/O helpers for handover workflows."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from .model import RetentionParameters
+
+
+def load_retention_parameters(path: str) -> RetentionParameters:
+    """Load two-column text file with k0 and S values."""
+    data = np.loadtxt(path, dtype=float)
+    if data.ndim == 1:
+        data = np.expand_dims(data, axis=0)
+    if data.shape[1] != 2:
+        raise ValueError("expected exactly two columns: k0 and S")
+    return RetentionParameters(k0=data[:, 0], s=data[:, 1])
diff --git a/chromatogram_handover/src/chromatogram_handover/model.py b/chromatogram_handover/src/chromatogram_handover/model.py
new file mode 100644
index 0000000..282a991
--- /dev/null
+++ b/chromatogram_handover/src/chromatogram_handover/model.py
@@ -0,0 +1,215 @@
+"""Retention-model utilities for predicting chromatograms from gradient programs."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from math import exp, log, sqrt
+from typing import Iterable
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class GradientProgram:
+    """Gradient definition used by the multisegment retention model.
+
+    Times are in minutes and represent gradient turning points relative to the
+    program start. The first point is the initial composition.
+    """
+
+    phi: tuple[float, ...]
+    time: tuple[float, ...]
+    t_init: float = 0.0
+
+    def __post_init__(self) -> None:
+        if len(self.phi) != len(self.time):
+            raise ValueError("phi and time must have equal length")
+        if len(self.phi) < 2:
+            raise ValueError("gradient requires at least two turning points")
+        if any(t2 <= t1 for t1, t2 in zip(self.time, self.time[1:])):
+            raise ValueError("time points must be strictly increasing")
+
+    @property
+    def slopes(self) -> tuple[float, ...]:
+        return tuple(
+            (self.phi[i + 1] - self.phi[i]) / (self.time[i + 1] - self.time[i])
+            for i in range(len(self.phi) - 1)
+        )
+
+
+@dataclass(frozen=True)
+class RetentionParameters:
+    """k0 and S values per analyte."""
+
+    k0: np.ndarray
+    s: np.ndarray
+
+    @classmethod
+    def from_iterables(
+        cls, k0_values: Iterable[float], s_values: Iterable[float]
+    ) -> "RetentionParameters":
+        k0 = np.asarray(list(k0_values), dtype=float)
+        s = np.asarray(list(s_values), dtype=float)
+        if k0.shape != s.shape:
+            raise ValueError("k0 and s must have identical shape")
+        return cls(k0=k0, s=s)
+
+
+@dataclass(frozen=True)
+class Chromatogram:
+    """Computed chromatogram descriptors."""
+
+    retention_times: np.ndarray
+    peak_widths: np.ndarray
+
+
+def _retention_factor(k0: float, s_value: float, phi: float) -> float:
+    return k0 * exp(-s_value * phi)
+
+
+def _isocratic_peak_width(n_theoretical: float, t0: float, k: float) -> float:
+    return 4.0 * (1.0 / sqrt(n_theoretical)) * t0 * (1.0 + k)
+
+
+def _gradient_compression_factor(
+    cumulative_width_term: float,
+    k_at_elution: float,
+    k_initial: float,
+    dwell_time: float,
+    t0: float,
+    t_init: float,
+) -> float:
+    term = ((dwell_time + t_init) * ((1.0 + k_initial) ** 2)) / (k_initial**3)
+    g_sq = (k_at_elution**2 / (t0 * (1.0 + k_at_elution) ** 2)) * (
+        term + cumulative_width_term
+    )
+    return sqrt(g_sq)
+
+
+def _gradient_peak_width(n_theoretical: float, t0: float, k: float, g: float) -> float:
+    return 4.0 * g * ((t0 * (1.0 + k)) / sqrt(n_theoretical))
+
+
+def retention_time_and_width(
+    k0: float,
+    s_value: float,
+    t0: float,
+    dwell_time: float,
+    gradient: GradientProgram,
+    n_theoretical: float,
+) -> tuple[float, float]:
+    """Compute retention time and peak width for one analyte."""
+    phi_init = gradient.phi[0]
+    k_init = _retention_factor(k0, s_value, phi_init)
+    frac_before_gradient = (dwell_time + gradient.t_init) / (t0 * k_init)
+
+    if frac_before_gradient >= 1:
+        retention_time = t0 * (1 + k_init)
+        return retention_time, _isocratic_peak_width(n_theoretical, t0, k_init)
+
+    cumulative = 0.0
+    cumulative_width = 0.0
+
+    for segment, slope in enumerate(gradient.slopes):
+        phi_i = gradient.phi[segment]
+        phi_next = gradient.phi[segment + 1]
+        t_i = gradient.time[segment]
+        t_next = gradient.time[segment + 1]
+
+        k_i = _retention_factor(k0, s_value, phi_i)
+        k_next = _retention_factor(k0, s_value, phi_next)
+
+        if slope == 0:
+            add = (t_next - t_i) / (t0 * k_i)
+            add_w = ((t_next - t_i) * (1 + k_i) ** 2) / (k_i**3)
+        else:
+            add = (1 / (t0 * s_value * slope)) * ((1 / k_next) - (1 / k_i))
+            add_w = (1 / (s_value * slope)) * (
+                (1 / 3) * ((1 / k_next**3) - (1 / k_i**3))
+                + (1 / k_next**2 - 1 / k_i**2)
+                + (1 / k_next - 1 / k_i)
+            )
+
+        if cumulative + add + frac_before_gradient < 1:
+            cumulative += add
+            cumulative_width += add_w
+            continue
+
+        if slope == 0:
+            tr = (
+                dwell_time
+                + gradient.t_init
+                + t0
+                + t_i
+                + t0 * k_i * (1 - frac_before_gradient - cumulative)
+            )
+            cumulative_width += (
+                (tr - t0 - dwell_time - gradient.t_init - t_i) * (1 + k_i) ** 2
+            ) / (k_i**3)
+            g = _gradient_compression_factor(
+                cumulative_width, k_i, k_init, dwell_time, t0, gradient.t_init
+            )
+            return tr, _gradient_peak_width(n_theoretical, t0, k_i, g)
+
+        tr = t0 + dwell_time + gradient.t_init + t_i + (1 / (s_value * slope)) * log(
+            1 + t0 * s_value * slope * k_i * (1 - frac_before_gradient - cumulative)
+        )
+        phi_elute = phi_i + slope * (tr - t0 - dwell_time - gradient.t_init - t_i)
+        k_elute = _retention_factor(k0, s_value, phi_elute)
+        cumulative_width += (1 / (s_value * slope)) * (
+            (1 / 3) * ((1 / k_elute**3) - (1 / k_i**3))
+            + (1 / k_elute**2 - 1 / k_i**2)
+            + (1 / k_elute - 1 / k_i)
+        )
+        g = _gradient_compression_factor(
+            cumulative_width, k_elute, k_init, dwell_time, t0, gradient.t_init
+        )
+        return tr, _gradient_peak_width(n_theoretical, t0, k_elute, g)
+
+    phi_last = gradient.phi[-1]
+    k_last = _retention_factor(k0, s_value, phi_last)
+    t_last = gradient.time[-1]
+    tr = (
+        dwell_time
+        + gradient.t_init
+        + t0
+        + t_last
+        + t0 * k_last * (1 - frac_before_gradient - cumulative)
+    )
+    cumulative_width += ((tr - t0 - dwell_time - gradient.t_init - t_last) * (1 + k_last) ** 2) / (
+        k_last**3
+    )
+    g = _gradient_compression_factor(
+        cumulative_width, k_last, k_init, dwell_time, t0, gradient.t_init
+    )
+    return tr, _gradient_peak_width(n_theoretical, t0, k_last, g)
+
+
+def compute_chromatogram(
+    parameters: RetentionParameters,
+    gradient: GradientProgram,
+    t0: float,
+    dwell_time: float,
+    n_theoretical: float,
+) -> Chromatogram:
+    """Compute and sort retention times and peak widths for all analytes."""
+    retention_times = np.zeros_like(parameters.k0)
+    peak_widths = np.zeros_like(parameters.k0)
+
+    for i, (k0, s_val) in enumerate(zip(parameters.k0, parameters.s, strict=True)):
+        tr, width = retention_time_and_width(
+            k0=k0,
+            s_value=s_val,
+            t0=t0,
+            dwell_time=dwell_time,
+            gradient=gradient,
+            n_theoretical=n_theoretical,
+        )
+        retention_times[i] = tr
+        peak_widths[i] = width
+
+    ordering = np.argsort(retention_times)
+    return Chromatogram(
+        retention_times=retention_times[ordering],
+        peak_widths=peak_widths[ordering],
+    )
diff --git a/chromatogram_handover/tests/test_model.py b/chromatogram_handover/tests/test_model.py
new file mode 100644
index 0000000..670a0b9
--- /dev/null
+++ b/chromatogram_handover/tests/test_model.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+from chromatogram_handover import GradientProgram, RetentionParameters, compute_chromatogram
+from rm_code.retention_model import compute_chromatogram as legacy_compute_chromatogram
+
+
+def test_refactor_matches_legacy_implementation():
+    params = RetentionParameters.from_iterables(
+        [5.80, 5.85, 6.25, 6.29], [16.2, 24.0, 18.9, 19.7]
+    )
+    gradient = GradientProgram(phi=(0.1, 0.2, 0.5, 0.8), time=(0.0, 5.0, 20.0, 35.0), t_init=0.5)
+
+    new = compute_chromatogram(
+        parameters=params,
+        gradient=gradient,
+        t0=0.8,
+        dwell_time=0.12,
+        n_theoretical=25000,
+    )
+    old_tr, old_w = legacy_compute_chromatogram(
+        k0_list=params.k0,
+        S_list=params.s,
+        t_0=0.8,
+        t_D=0.12,
+        t_init=0.5,
+        phi_list=list(gradient.phi),
+        t_list=list(gradient.time),
+        N=25000,
+    )
+
+    np.testing.assert_allclose(new.retention_times, old_tr)
+    np.testing.assert_allclose(new.peak_widths, old_w)
+
+
+def test_invalid_gradient_raises():
+    try:
+        GradientProgram(phi=(0.1, 0.2), time=(0.0, 0.0))
+    except ValueError as exc:
+        assert "strictly increasing" in str(exc)
+    else:
+        raise AssertionError("Expected ValueError for non-increasing gradient time")