diff --git a/ingest_templates/fisa_capitalcommitments.yml b/ingest_templates/fisa_capitalcommitments.yml
index a1bc0b8af1..712a5195ca 100644
--- a/ingest_templates/fisa_capitalcommitments.yml
+++ b/ingest_templates/fisa_capitalcommitments.yml
@@ -10,7 +10,7 @@ ingestion:
   source:
     type: s3
     bucket: edm-recipes
-    key: inbox/fisa_capitalcommitments/AICP_OREQ_CAPPLN_PJCP_20260218121103.asc
+    key: inbox/fisa_capitalcommitments/AICP_OREQ_CAPPLN_PJCP_20260218121103_fixed.asc
   file_format:
     type: csv
     encoding: ISO-8859-1
diff --git a/notebooks/marimo/lifecycle/build_qa/s3_compare.py b/notebooks/marimo/lifecycle/build_qa/s3_compare.py
new file mode 100644
index 0000000000..7cbdd76147
--- /dev/null
+++ b/notebooks/marimo/lifecycle/build_qa/s3_compare.py
@@ -0,0 +1,410 @@
+import marimo
+
+__generated_with = "0.23.1"
+app = marimo.App(width="full")
+
+with app.setup:
+    import marimo as mo
+    import pandas as pd
+
+    from dcpy.utils import s3
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    # S3 Build QA
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ## Directory Comparison
+
+    Explore and compare files across two S3 paths (e.g. a published draft vs. a new build).
+    """)
+    return
+
+
+@app.cell
+def _():
+    bucket_input = mo.ui.text(value="edm-publishing", label="Bucket")
+    path_a_input = mo.ui.text(
+        value="db-cpdb/draft/26prelim/2/",
+        label="Path A — baseline (e.g. published draft)",
+        full_width=True,
+    )
+    path_b_input = mo.ui.text(
+        value="db-cpdb/build/dm-cpdb-26prelim/",
+        label="Path B — new build",
+        full_width=True,
+    )
+    mo.vstack(
+        [
+            bucket_input,
+            mo.hstack([path_a_input, path_b_input], gap=2),
+        ],
+        align="center",
+    )
+    return bucket_input, path_a_input, path_b_input
+
+
+@app.cell
+def _(bucket_input, path_a_input, path_b_input):
+    def _fetch(prefix: str) -> pd.DataFrame:
+        objs = s3.list_objects(bucket_input.value, prefix)
+        if not objs:
+            return pd.DataFrame(columns=["filename", "size_bytes", "last_modified"])
+        return pd.DataFrame(
+            [
+                {
+                    "filename": o["Key"].removeprefix(prefix),
+                    "size_bytes": o["Size"],
+                    "last_modified": o["LastModified"],
+                }
+                for o in objs
+                if not o["Key"].endswith("/")
+            ]
+        )
+
+    with mo.status.spinner(title="Fetching objects from S3…"):
+        df_a = _fetch(path_a_input.value)
+        df_b = _fetch(path_b_input.value)
+    return df_a, df_b
+
+
+@app.cell
+def _(df_a, df_b, path_a_input, path_b_input):
+    _merged = pd.merge(
+        df_a[["filename", "size_bytes"]].rename(columns={"size_bytes": "size_a"}),
+        df_b[["filename", "size_bytes"]].rename(columns={"size_bytes": "size_b"}),
+        on="filename",
+        how="outer",
+        indicator=True,
+    )
+    _merged["in_a"] = _merged["_merge"].isin(["left_only", "both"])
+    _merged["in_b"] = _merged["_merge"].isin(["right_only", "both"])
+    _merged["size_diff_bytes"] = _merged["size_b"] - _merged["size_a"]
+    _merged["size_diff_pct"] = (
+        _merged["size_diff_bytes"] / _merged["size_a"] * 100
+    ).round(1)
+    comparison = _merged.drop(columns=["_merge"])
+
+    _n_only_a = comparison[~comparison["in_b"]].shape[0]
+    _n_only_b = comparison[~comparison["in_a"]].shape[0]
+    _n_both = comparison[comparison["in_a"] & comparison["in_b"]].shape[0]
+
+    mo.md(
+        f"""
+        ## Summary
+
+        | | Count |
+        |---|---|
+        | Files in **A** (`{path_a_input.value}`) | {len(df_a)} |
+        | Files in **B** (`{path_b_input.value}`) | {len(df_b)} |
+        | Only in A | {_n_only_a} |
+        | Only in B | {_n_only_b} |
+        | In both | {_n_both} |
+        """
+    )
+    return (comparison,)
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md("""
+    ## Only in A — missing from new build
+    """)
+    return
+
+
+@app.cell
+def _(comparison):
+    _df = comparison[~comparison["in_b"]][["filename", "size_a"]].reset_index(drop=True)
+    mo.ui.table(_df, selection=None) if len(_df) else mo.md(
+        "_None — all files from A are present in B._"
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md("""
+    ## Only in B — new files
+    """)
+    return
+
+
+@app.cell
+def _(comparison):
+    _df = comparison[~comparison["in_a"]][["filename", "size_b"]].reset_index(drop=True)
+    mo.ui.table(_df, selection=None) if len(_df) else mo.md(
+        "_None — no new files in B._"
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md("""
+    ## Files in both — size comparison
+    """)
+    return
+
+
+@app.cell
+def _(comparison):
+    _df = (
+        comparison[comparison["in_a"] & comparison["in_b"]][
+            ["filename", "size_a", "size_b", "size_diff_bytes", "size_diff_pct"]
+        ]
+        .sort_values("size_diff_pct", key=abs, ascending=False, na_position="last")
+        .reset_index(drop=True)
+    )
+    mo.ui.table(_df, selection=None)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md("""
+    ## Table Comparison
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(comparison):
+    _files_in_both = sorted(
+        comparison[comparison["in_a"] & comparison["in_b"]]["filename"].tolist()
+    )
+    table_selector = mo.ui.dropdown(
+        options=_files_in_both,
+        label="Select a file to compare",
+        searchable=True,
+    )
+    mo.vstack([table_selector], align="center")
+    return (table_selector,)
+
+
+@app.cell(hide_code=True)
+def _(bucket_input, path_a_input, path_b_input, table_selector):
+    from io import BytesIO
+
+    def _load_file(prefix: str, filename: str) -> pd.DataFrame:
+        key = prefix + filename
+        body = s3.get_file(bucket_input.value, key)
+        data = BytesIO(body.read())
+        ext = filename.rsplit(".", 1)[-1].lower()
+        if ext == "csv":
+            return pd.read_csv(data)
+        elif ext == "parquet":
+            return pd.read_parquet(data)
+        elif ext == "json":
+            return pd.read_json(data)
+        else:
+            raise ValueError(f"Unsupported file extension: .{ext}")
+
+    if table_selector.value:
+        with mo.status.spinner(title=f"Loading {table_selector.value}…"):
+            tbl_a = _load_file(path_a_input.value, table_selector.value)
+            tbl_b = _load_file(path_b_input.value, table_selector.value)
+    else:
+        tbl_a = tbl_b = None
+    return tbl_a, tbl_b
+
+
+@app.cell(hide_code=True)
+def _(table_selector, tbl_a, tbl_b):
+    mo.stop(
+        tbl_a is None or tbl_b is None,
+        mo.md("_Select a file above to load and compare._"),
+    )
+    _cols_only_a = sorted(set(tbl_a.columns) - set(tbl_b.columns))
+    _cols_only_b = sorted(set(tbl_b.columns) - set(tbl_a.columns))
+    _col_status = (
+        "Column sets match."
+        if not _cols_only_a and not _cols_only_b
+        else f"Only in A: `{'`, `'.join(_cols_only_a) or '—'}`  ·  Only in B: `{'`, `'.join(_cols_only_b) or '—'}`"
+    )
+    mo.vstack(
+        [
+            mo.md(f"""
+    | | A | B |
+    |---|---|---|
+    | Rows | {len(tbl_a):,} | {len(tbl_b):,} |
+    | Columns | {len(tbl_a.columns)} | {len(tbl_b.columns)} |
+
+    **Columns:** {_col_status}
+    """),
+            mo.ui.tabs(
+                {
+                    f"A — {table_selector.value}": mo.ui.table(tbl_a, selection=None),
+                    f"B — {table_selector.value}": mo.ui.table(tbl_b, selection=None),
+                }
+            ),
+        ]
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    _df = mo.sql(
+        """
+        select * from 
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md("""
+    ## Geospatial Comparison
+    """)
+    return
+
+
+@app.cell
+def _():
+    import openlayers as ol
+    from openlayers.basemaps import Carto, CartoBasemapLayer
+
+    return Carto, CartoBasemapLayer, ol
+
+
+@app.cell(hide_code=True)
+def _(comparison):
+    _zip_files = sorted(
+        comparison.loc[
+            comparison["in_a"]
+            & comparison["in_b"]
+            & comparison["filename"].str.endswith(".zip"),
+            "filename",
+        ].tolist()
+    )
+    mo.stop(not _zip_files, mo.md("_No shared `.zip` files found in both paths._"))
+    geo_selector = mo.ui.dropdown(
+        options=_zip_files,
+        label="Select a shapefile (.zip) to compare",
+        searchable=True,
+    )
+    mo.vstack([geo_selector], align="center")
+    return (geo_selector,)
+
+
+@app.cell(hide_code=True)
+def _(bucket_input, geo_selector, path_a_input, path_b_input):
+    import os
+    import tempfile
+
+    import geopandas as gpd
+
+    mo.stop(geo_selector.value is None, mo.md("_Select a shapefile above._"))
+
+    def _load_shapefile(prefix: str, filename: str) -> gpd.GeoDataFrame:
+        body = s3.get_file(bucket_input.value, prefix + filename)
+        with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as _tmp:
+            _tmp.write(body.read())
+            _tmp_path = _tmp.name
+        try:
+            _gdf = gpd.read_file(f"zip://{_tmp_path}")
+        finally:
+            os.unlink(_tmp_path)
+        return _gdf
+
+    with mo.status.spinner(title=f"Loading {geo_selector.value} from both paths…"):
+        geo_gdf_a = (
+            _load_shapefile(path_a_input.value, geo_selector.value)
+            .to_crs(4326)
+            .reset_index(drop=True)
+        )
+        geo_gdf_b = (
+            _load_shapefile(path_b_input.value, geo_selector.value)
+            .to_crs(4326)
+            .reset_index(drop=True)
+        )
+    return geo_gdf_a, geo_gdf_b
+
+
+@app.cell(hide_code=True)
+def _(geo_gdf_a, geo_selector, path_a_input):
+    _geom_col = geo_gdf_a.geometry.name
+    _attr_cols = [c for c in geo_gdf_a.columns if c != _geom_col]
+    geo_table_a = mo.ui.table(
+        geo_gdf_a[_attr_cols],
+        selection="multi",
+        page_size=20,
+        label=f"A — {path_a_input.value}{geo_selector.value} ({len(geo_gdf_a):,} features) · check rows to highlight on map",
+    )
+    geo_table_a
+    return (geo_table_a,)
+
+
+@app.cell(hide_code=True)
+def _(Carto, CartoBasemapLayer, geo_gdf_a, geo_table_a, ol):
+    _sel_idx = geo_table_a.value.index.tolist()
+    _plot_a = geo_gdf_a.loc[_sel_idx] if _sel_idx else geo_gdf_a
+    _style_a = ol.FlatStyle(
+        fill_color="rgba(70, 130, 180, 0.35)",
+        stroke_color="#4682b4",
+        stroke_width=2,
+        circle_radius=6,
+        circle_fill_color="rgba(70, 130, 180, 0.7)",
+        circle_stroke_color="#4682b4",
+        circle_stroke_width=1.5,
+    )
+    _layer_a = _plot_a.ol.to_layer(style=_style_a, fit_bounds=False)
+    _map_a = ol.MapWidget(
+        view=ol.View(center=[0, 0], zoom=1),
+        layers=[CartoBasemapLayer(Carto.LIGHT_ALL), _layer_a],
+    )
+    _map_a.fit_bounds(tuple(_plot_a.geometry.total_bounds))
+    _map_a.add_tooltip()
+    _map_a
+    return
+
+
+@app.cell(hide_code=True)
+def _(geo_gdf_b, geo_selector, path_b_input):
+    _geom_col = geo_gdf_b.geometry.name
+    _attr_cols = [c for c in geo_gdf_b.columns if c != _geom_col]
+    geo_table_b = mo.ui.table(
+        geo_gdf_b[_attr_cols],
+        selection="multi",
+        page_size=20,
+        label=f"B — {path_b_input.value}{geo_selector.value} ({len(geo_gdf_b):,} features) · check rows to highlight on map",
+    )
+    geo_table_b
+    return (geo_table_b,)
+
+
+@app.cell(hide_code=True)
+def _(Carto, CartoBasemapLayer, geo_gdf_b, geo_table_b, ol):
+    _sel_idx = geo_table_b.value.index.tolist()
+    _plot_b = geo_gdf_b.loc[_sel_idx] if _sel_idx else geo_gdf_b
+    _style_b = ol.FlatStyle(
+        fill_color="rgba(230, 57, 70, 0.35)",
+        stroke_color="#e63946",
+        stroke_width=2,
+        circle_radius=6,
+        circle_fill_color="rgba(230, 57, 70, 0.7)",
+        circle_stroke_color="#e63946",
+        circle_stroke_width=1.5,
+    )
+    _layer_b = _plot_b.ol.to_layer(style=_style_b, fit_bounds=False)
+    _map_b = ol.MapWidget(
+        view=ol.View(center=[0, 0], zoom=1),
+        layers=[CartoBasemapLayer(Carto.LIGHT_ALL), _layer_b],
+    )
+    _map_b.fit_bounds(tuple(_plot_b.geometry.total_bounds))
+    _map_b.add_tooltip()
+    _map_b
+    return
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/products/cpdb/data/sprints.sql b/products/cpdb/data/sprints.sql
index 22dcaec590..8e33059719 100644
--- a/products/cpdb/data/sprints.sql
+++ b/products/cpdb/data/sprints.sql
@@ -4570,7 +4570,6 @@ INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801RICONNECT
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801ROCKBLVD', NULL, NULL, 'DCP Sprint', '0104000020E61000000100000001010000003DB5856F387052C01D3A803AFC524440');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('056PO7990LOC', '3024650100', NULL, 'AD Sprint', '0104000020E610000001000000010100000000000099D77C52C0C8EB567C675A4440');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('126PV256DIMM', '1010340022', NULL, 'AD Sprint', '0106000020E610000001000000010300000001000000050000004B4D5B65577F52C072765B1B1661444026391C535A7F52C0EEB477140E614440A8B341D8607F52C01D236F91136144405A6B83EA5D7F52C03DBE57981B6144404B4D5B65577F52C072765B1B16614440');
-INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SANDRBCT', '3052570045', NULL, 'AD Sprint', '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');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801WATERSIDE', '1009910035', NULL, 'AD Sprint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
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('81948201040', NULL, NULL, 'DCP Sprint', NULL);
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('81948201102', NULL, NULL, 'AD Sprint', NULL);
@@ -4592,7 +4591,6 @@ INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SAND1106'
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SAND1108', '1009620100', NULL, 'AD Sprint', '0106000020E6100000010000000103000000010000004D00000015D4E4114C7E52C0249925ADB35E4440FD5E77114C7E52C0353FDFB0B35E4440A114F8DF4B7E52C00673D0EDB45E4440B9129F954B7E52C0BB85501FB65E44409F1F84334B7E52C000CFE140B75E44409F6918BB4A7E52C0619B424EB85E4440AC2E7B624A7E52C043494DE9B95E44403E3E932D4A7E52C04BA95492BB5E44403257441D4A7E52C068453342BD5E4440CA1080FA497E52C0F4E92C62C05E4440A2F707F3497E52C05F26070EC15E4440E715E6EB497E52C0DF4F1AC5C15E444054BEE3EB497E52C02C5E70C5C15E44406E618408487E52C0A8293AEDBF5E4440CBEB3D0F487E52C06C8A9140BF5E4440A9D96019487E52C0B2935657BE5E444034818524487E52C047FE7F74BD5E444016452D31487E52C0381E9E89BC5E444094C48B3D487E52C02DE2E5B5BB5E4440D3A37044487E52C0E8F67A46BB5E44407EB06B51487E52C0DF717A81BA5E444036CF21C5487E52C019227A1CB55E44405DF51306497E52C0B68157B8B25E4440BD4C7F80497E52C01199ECE6AE5E444065DA66AD497E52C087DFDAA8AD5E4440326428D0497E52C063DE03BEAC5E4440D411B0004A7E52C022C86E84AB5E4440E6F6FD3A4A7E52C0BF20871EAA5E4440B946C3754B7E52C0B0CA659DA35E444011D7EB3D4C7E52C0FE10781BA05E44401A3089524D7E52C0D8F6A6CB9B5E444069C1B75A4F7E52C093376FDA945E4440615148F6507E52C0586BB81E905E444061C0B150527E52C0BC48E2848C5E44409D11F676527E52C0F2E83F2F8C5E44401F50B190527E52C0B33AC9EA8B5E4440A09948AE527E52C0A61354878B5E4440A544CFC0527E52C0B68747388B5E4440C0778BCE527E52C0008C5DE48A5E4440012A29DB527E52C03981A77C8A5E44405D068EDF527E52C09D9AE9268A5E444063EA3CDF527E52C0B51660C5895E44400FE5FBE0527E52C02B30096C895E444036526FE7527E52C0ACD921FF885E4440D7F982F9527E52C0C138C178885E44407D325CFD527E52C04FB19E63885E4440E50DC527537E52C05546B3B7875E4440E6E2BF4F537E52C08FF98144875E444065A03BB6537E52C00D853D23865E4440AFC558E85C7E52C09BDC802E6C5E4440B3F1CA72627E52C08858B48A5C5E444056D12DBC627E52C0EA2DBDFC5B5E4440FD89EFCE627E52C01A542AE55B5E4440385892FD627E52C0EE478EAA5B5E44406C105047637E52C06E3191685B5E44408BF400AC637E52C0C24DC0335B5E444089C7A21F647E52C0DBC68D1F5B5E444049372886647E52C042BA86305B5E4440EC81CAD2647E52C03500B2535B5E444043CEE016657E52C08514957F5B5E44400F276BC7687E52C0265687915E5E4440706E667A8D7E52C05263901A7D5E444079ED8296717E52C095C35DAFC95E44405C1DE6B1697E52C0301D6008C35E4440D08E2BA46C7E52C02A4E5CF1BA5E444061959539597E52C021740294AA5E4440FB0599BF5A7E52C0B5662B65A65E444065F4BC335A7E52C0B30A49EFA55E444090D48CBA517E52C01143FACA9E5E44405B233C80517E52C0BD60166B9F5E4440F9EDFEDC507E52C02A1E80E19E5E44404C1DF47E507E52C0B055B1E39F5E444058F7FB4E4F7E52C017B77CE39E5E4440FE4444AE4E7E52C04757D464A15E444084750C774E7E52C0A0438A5CA25E4440DCFF46804D7E52C006CF95AFA65E444015D4E4114C7E52C0249925ADB35E4440');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SAND6703', '1013730050', NULL, 'AD Sprint', '0106000020E61000000100000001030000000100000005000000C28A25F72D7C52C0E02BA79EBA624440D50101284D7C52C06269D09467624440691F3B6C657C52C04D29BA6C7D6244404943893B467C52C08C1CC676D0624440C28A25F72D7C52C0E02BA79EBA624440');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SAND6706', '1013730050', NULL, 'AD Sprint', '0106000020E61000000100000001030000000100000005000000C28A25F72D7C52C0E02BA79EBA624440D50101284D7C52C06269D09467624440691F3B6C657C52C04D29BA6C7D6244404943893B467C52C08C1CC676D0624440C28A25F72D7C52C0E02BA79EBA624440');
-INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('801SANDBCT', '3052570045', NULL, 'AD Sprint', '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');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('841TFD503M24', NULL, NULL, 'DCP Sprint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
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('806BVIEW', '2028610001', NULL, 'AD Sprint', '0104000020E6100000010000000101000000420EAE76817A52C0D6AAC71C426C4440');
 INSERT INTO sprints (maprojid, bbl, bin, geomsource, geom) VALUES ('841TFD503X21', NULL, NULL, 'DCP Sprint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
diff --git a/products/cpdb/recipe.yml b/products/cpdb/recipe.yml
index 67b3a21c23..5c1b3d33a3 100644
--- a/products/cpdb/recipe.yml
+++ b/products/cpdb/recipe.yml
@@ -1,6 +1,6 @@
 name: CPDB
 product: db-cpdb
-version: 25adopt
+version: 26prelim
 inputs:
   missing_versions_strategy: find_latest
   datasets:
diff --git a/products/cpdb/seeds/dcp_id_bin_map.csv b/products/cpdb/seeds/dcp_id_bin_map.csv
index a1ec62f91c..aaf534a69a 100644
--- a/products/cpdb/seeds/dcp_id_bin_map.csv
+++ b/products/cpdb/seeds/dcp_id_bin_map.csv
@@ -102,3 +102,5 @@ maprojid,bin
 126PV176-REP,2116695
 850PV176-WHC,2116709
 846P-312DMRR,1075616
+801SANDBCT,3398756
+801SANDRBCT,3398756