diff --git a/README.md b/README.md
index 9a9c9a6..82bc916 100644
--- a/README.md
+++ b/README.md
@@ -174,6 +174,11 @@ output:
 - Policy discussion analysis
 - Keyword and topic-based filtering
 
+### Overton
+- Government publications
+- UK and international searches
+- Keyword search with LLM validation
+
 ## 📊 Outputs
 
 ### Generated Files
diff --git a/discovery_mission_radar/config/llm_prompts.yaml b/discovery_mission_radar/config/llm_prompts.yaml
index 3dd1ebd..f00bf08 100644
--- a/discovery_mission_radar/config/llm_prompts.yaml
+++ b/discovery_mission_radar/config/llm_prompts.yaml
@@ -37,6 +37,21 @@ missions:
       - The description is generic energy/research text without a clear category link
       - Content better fits another configured category
       Default to 'no' if uncertain or ambiguous.
+    overton: |
+      Mark 'yes' only if the policy document clearly relates to the configured category in the correct sense (energy technology/solution) and contains at least one concrete cue such as:
+      - A policy, regulation, programme, target, deployment, funding, evidence, performance or implementation point linked to the category
+      - A technology, intervention, component, system, cost/benefit or pathway explicitly tied to the category
+
+      Consider the source/series/year metadata to disambiguate generic mentions:
+      - Source type should typically be government or IGO; venue should be relevant public bodies
+      - Series like Transcript/Press Release are not relevant
+      - Use snippet/highlights as match context; prefer substantive documents
+
+      Mark 'no' if any of the following:
+      - The term appears only in passing, list-like, or generic policy text without a clear link to the category
+      - The usage is a different meaning/sense than the configured category
+      - Content better fits another configured category
+      Default to 'no' if uncertain or ambiguous.
     default: |
       Mark 'yes' only if the text explicitly mentions the configured category in the correct sense AND includes at least one
       nearby contextual cue evidencing a real connection (example, measure, implementation, funding, regulation, target, performance).
@@ -79,6 +94,20 @@ missions:
       - The description is generic health/food/research text without a clear category link
       - Content better fits another configured category
       Default to 'no' if uncertain or ambiguous.
+    overton: |
+      Mark 'yes' only if the policy document clearly relates to the configured category in the correct sense (as defined by the topic scope) and includes at least one concrete cue such as:
+      - An intervention, service, technology, programme, regulation, funding, outcome/measurement, implementation or performance point explicitly tied to the category
+
+      Consider the source/series/year metadata to disambiguate generic mentions:
+      - Source type should typically be government or IGO; venue should be relevant public bodies
+      - Series like Transcript/Press Release are not relevant
+      - Use snippet/highlights as match context; prefer substantive documents
+
+      Mark 'no' if any of the following:
+      - The term appears only in passing, list-like, or generic policy/health text without a clear link to the category
+      - The usage is a different meaning/sense than the configured category
+      - Content better fits another configured category
+      Default to 'no' if uncertain or ambiguous.
     default: |
       Mark 'yes' only if the text explicitly mentions the configured category in the correct sense AND includes at least one
       nearby contextual cue evidencing a real connection (example, measure, implementation, funding, regulation, target, performance).
diff --git a/discovery_mission_radar/config/pipeline.yaml b/discovery_mission_radar/config/pipeline.yaml
index e4fa71a..9a7c892 100644
--- a/discovery_mission_radar/config/pipeline.yaml
+++ b/discovery_mission_radar/config/pipeline.yaml
@@ -132,6 +132,33 @@ data_sources:
         "weight"
       ]
 
+  overton:
+    enabled: true
+    # Only configurable knob; all other behaviour is fixed in code
+    window_months: 60
+    # Shared core UK policy sources used across missions
+    core_sources:
+      - govuk
+      - ukparliament_select
+      - ukparliament
+      - legislationgovuk
+      - onsgovuk
+      - scottishgovernment
+      - govwales
+      - northernirelandgovuk
+    # Mission-specific additions (merged with core_sources in implementation)
+    mission_sources:
+      ASF: 
+        - theccc
+      AHL:
+        - nice
+        - nhsengland
+        - foodgovuk
+    international:
+      enabled_per_mission:
+        AHL: true
+        ASF: false
+
 # Categories for cross-topic radar charts
 categories_to_show:
   ASF:
@@ -149,6 +176,7 @@ categories_to_show:
     - "food_retail_analytics"
     - "health_food_meal_replacement"
     - "advanced_food_production"
+    - "glp_adjacent"
 
 # Output settings  
 output:
diff --git a/discovery_mission_radar/pipeline/analysis/__init__.py b/discovery_mission_radar/pipeline/analysis/__init__.py
index cba7019..6bc45f3 100644
--- a/discovery_mission_radar/pipeline/analysis/__init__.py
+++ b/discovery_mission_radar/pipeline/analysis/__init__.py
@@ -12,6 +12,7 @@
 from .crunchbase_analysis import CrunchbaseAnalysisModule
 from .gtr_analysis import GtrAnalysisModule
 from .hansard_analysis import HansardAnalysisModule
+from .overton_analysis import OvertonAnalysisModule
 
 # Import aggregation and consolidation functions
 from .aggregation import produce_radar_charts
@@ -25,6 +26,7 @@
     'CrunchbaseAnalysisModule',
     'GtrAnalysisModule', 
     'HansardAnalysisModule',
+    'OvertonAnalysisModule',
     
     # Aggregation functions
     'produce_radar_charts',
diff --git a/discovery_mission_radar/pipeline/analysis/consolidation.py b/discovery_mission_radar/pipeline/analysis/consolidation.py
index 280c7a0..085cacc 100644
--- a/discovery_mission_radar/pipeline/analysis/consolidation.py
+++ b/discovery_mission_radar/pipeline/analysis/consolidation.py
@@ -33,6 +33,8 @@ def consolidate_all_topics(topic_results: List[Dict[str, Any]], output_dir: Path
     ukri_stats, ukri_stats_quarterly, ukri_projects = _consolidate_gtr_data(topic_results, mission)
     
     hansard_stats, hansard_stats_quarterly, hansard_speeches = _consolidate_hansard_data(topic_results, mission)
+
+    overton_stats, overton_stats_quarterly = _consolidate_overton_data(topic_results, mission)
     
     # Define data to save with their corresponding filenames
     data_to_save = [
@@ -47,7 +49,9 @@ def consolidate_all_topics(topic_results: List[Dict[str, Any]], output_dir: Path
         (ukri_projects, "ukri_projects.csv"),
         (hansard_stats, "hansard_stats.csv"),
         (hansard_stats_quarterly, "hansard_stats_quarterly.csv"),
-        (hansard_speeches, "hansard_speeches.csv")
+        (hansard_speeches, "hansard_speeches.csv"),
+        (overton_stats, "overton_stats.csv"),
+        (overton_stats_quarterly, "overton_stats_quarterly.csv")
     ]
     
     # Save each non-empty dataframe
@@ -66,6 +70,7 @@ def consolidate_all_topics(topic_results: List[Dict[str, Any]], output_dir: Path
                 cb_stats, cb_stats_quarterly, cb_companies, cb_funding, cb_ipos, cb_acquisitions,
                 ukri_stats, ukri_stats_quarterly, ukri_projects,
                 hansard_stats, hansard_stats_quarterly, hansard_speeches,
+                overton_stats, overton_stats_quarterly,
                 config.google_sheets_id
             )
         except Exception as e:
@@ -217,11 +222,57 @@ def _consolidate_hansard_data(topic_results: List[Dict[str, Any]], mission: str)
         pd.concat(all_speeches, ignore_index=True) if all_speeches else pd.DataFrame()
     )
 
+def _consolidate_overton_data(topic_results: List[Dict[str, Any]], mission: str) -> tuple:
+    """Consolidate Overton data (yearly and quarterly counts) from all topics.
+    
+    Reads ts_yearly.csv and ts_quarterly.csv from per-topic overton directories (UK and International)
+    and stacks them with topic and pass labels for upload/aggregation.
+    """
+    all_yearly = []
+    all_quarterly = []
+    base_outputs = Path("outputs") / mission
+    for topic_result in topic_results:
+        topic_name = topic_result.get('topic_name')
+        if not topic_name:
+            continue
+        for label in ["overton_uk", "overton_international"]:
+            pass_name = "uk" if label.endswith("uk") else "international"
+            topic_dir = base_outputs / topic_name / label
+            yearly_fp = topic_dir / "ts_yearly.csv"
+            quarterly_fp = topic_dir / "ts_quarterly.csv"
+            try:
+                if yearly_fp.exists():
+                    dfy = pd.read_csv(yearly_fp)
+                    if not dfy.empty and set(["year", "count"]).issubset(dfy.columns):
+                        dfy["topic"] = topic_name
+                        dfy["pass"] = pass_name
+                        all_yearly.append(dfy)
+            except Exception as e:
+                logger.warning(f"Failed to read Overton yearly for {topic_name}/{pass_name}: {e}")
+            try:
+                if quarterly_fp.exists():
+                    dfq = pd.read_csv(quarterly_fp)
+                    # accept either (year,quarter,count) or (quarter,count)
+                    if not dfq.empty:
+                        if "quarter" not in dfq.columns and {"year", "quarter"}.issubset(dfq.columns):
+                            dfq["quarter"] = dfq.apply(lambda r: f"{int(r['year'])}-Q{int(r['quarter'])}", axis=1)
+                            dfq = dfq[["quarter", "count"]]
+                        if set(["quarter", "count"]).issubset(dfq.columns):
+                            dfq["topic"] = topic_name
+                            dfq["pass"] = pass_name
+                            all_quarterly.append(dfq[["quarter", "count", "topic", "pass"]])
+            except Exception as e:
+                logger.warning(f"Failed to read Overton quarterly for {topic_name}/{pass_name}: {e}")
+    yearly_out = pd.concat(all_yearly, ignore_index=True) if all_yearly else pd.DataFrame()
+    quarterly_out = pd.concat(all_quarterly, ignore_index=True) if all_quarterly else pd.DataFrame()
+    return yearly_out, quarterly_out
+
 def _upload_to_google_sheets(
     cb_stats: pd.DataFrame, cb_stats_quarterly: pd.DataFrame, cb_companies: pd.DataFrame,
     cb_funding: pd.DataFrame, cb_ipos: pd.DataFrame, cb_acquisitions: pd.DataFrame,
     ukri_stats: pd.DataFrame, ukri_stats_quarterly: pd.DataFrame, ukri_projects: pd.DataFrame,
     hansard_stats: pd.DataFrame, hansard_stats_quarterly: pd.DataFrame, hansard_speeches: pd.DataFrame,
+    overton_stats: pd.DataFrame, overton_stats_quarterly: pd.DataFrame,
     sheet_id: str
 ) -> None:
     """Upload consolidated DataFrames to Google Sheets.
@@ -251,6 +302,8 @@ def _upload_to_google_sheets(
         # TODO: bug - sheet upload error - input contains more than the maximum of 50000 characters in a single cell
         # upload manually
         #'hansard_speeches': hansard_speeches 
+        'overton_stats': overton_stats,
+        'overton_stats_quarterly': overton_stats_quarterly,
     }
     
     # Add non-empty dataframes to the dataframes dict
diff --git a/discovery_mission_radar/pipeline/analysis/overton_analysis.py b/discovery_mission_radar/pipeline/analysis/overton_analysis.py
new file mode 100644
index 0000000..e604193
--- /dev/null
+++ b/discovery_mission_radar/pipeline/analysis/overton_analysis.py
@@ -0,0 +1,186 @@
+"""Overton Analysis Module.
+
+Provides output writing for Overton results, separating concerns from the
+getter/data source. Mirrors the folder structure and artefacts expected
+by the pipeline: UK and International outputs side-by-side.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
+
+import json
+import pandas as pd
+from discovery_utils.utils import charts
+import altair as alt
+from .base import BaseAnalysisModule
+
+try:  # Optional typing/import safety
+    from discovery_utils.getters.overton import OvertonGetter  # type: ignore
+except Exception:  # pragma: no cover
+    OvertonGetter = object  # type: ignore
+
+
+class OvertonAnalysisModule(BaseAnalysisModule[OvertonGetter]):
+    """Overton analysis/output writer, aligned with BaseAnalysisModule.
+
+    Overton fetching, filtering and selection happen in the data source; this
+    module focuses on formatting outputs (CSV/JSON) and simple charts. The
+    BaseAnalysisModule abstract methods are implemented minimally to satisfy
+    the interface, but the pipeline calls `write_outputs` directly.
+    """
+
+    def __init__(self, mission: str):
+        super().__init__("overton", mission)
+
+    def _create_default_getter(self) -> OvertonGetter:  # type: ignore[override]
+        try:
+            from discovery_utils.getters.overton import OvertonGetter as _OG  # type: ignore
+            return _OG()
+        except Exception:
+            return OvertonGetter()  # type: ignore
+
+    def _process_topic_data(self, topic_data: Dict[str, Any], getter: OvertonGetter) -> Dict[str, pd.DataFrame]:  # type: ignore[override]
+        # Not used in current flow; return empty placeholders.
+        return {
+            'ts_yearly': pd.DataFrame(),
+            'ts_quarterly': pd.DataFrame(),
+        }
+
+    def _generate_custom_stats(self, analysis_results: Dict[str, pd.DataFrame], topic_data: Dict[str, Any]) -> Dict[str, Any]:  # type: ignore[override]
+        return {}
+
+    def _create_source_charts(self, analysis_results: Dict[str, pd.DataFrame], charts_dir: Path, category_name: str, scale_factor: int):  # type: ignore[override]
+        return []
+
+    def write_outputs(self, topic_cfg: Dict[str, Any], mission: str, uk_df: pd.DataFrame,
+                      intl_df: Optional[pd.DataFrame], uk_facets: Dict[str, Any],
+                      intl_facets: Dict[str, Any], uk_summary: Dict[str, Any],
+                      intl_summary: Dict[str, Any]) -> None:
+        category_name = (topic_cfg.get("search_recipe", {}) or {}).get("category_name", "topic")
+        topic_slug = self._slugify(category_name)
+        base = Path("outputs") / mission / topic_slug
+
+        uk_dir = base / "overton_uk"
+        uk_dir.mkdir(parents=True, exist_ok=True)
+        self._write_outputs_for(uk_dir, uk_df, uk_facets, uk_summary, category_name)
+
+        if intl_df is not None and not intl_df.empty:
+            intl_dir = base / "overton_international"
+            intl_dir.mkdir(parents=True, exist_ok=True)
+            self._write_outputs_for(intl_dir, intl_df, intl_facets, intl_summary, category_name)
+
+    def _slugify(self, text: str) -> str:
+        import re
+        t = (text or "").strip().lower()
+        if not t:
+            return "topic"
+        slug = re.sub(r"[^a-z0-9]+", "_", t)
+        slug = re.sub(r"_+", "_", slug).strip("_")
+        return slug or "topic"
+
+    def _write_outputs_for(self, outdir: Path, df: pd.DataFrame, facets: Dict[str, Any], summary: Dict[str, Any], category_name: str) -> None:
+        outdir.mkdir(parents=True, exist_ok=True)
+
+        # selected_documents.csv
+        cols = [
+            "id",
+            "title",
+            "abstract",
+            "publication_year",
+            "venue",
+            "source_country",
+            "source_type",
+            "overton_policy_document_series",
+            "overton_url",
+            "similarity_score",
+        ]
+        if df is not None and not df.empty:
+            available_cols = [c for c in cols if c in df.columns]
+            if available_cols:
+                df[available_cols].to_csv(outdir / "selected_documents.csv", index=False)
+            else:
+                pd.DataFrame(columns=[c for c in cols if c != "similarity_score"]).to_csv(outdir / "selected_documents.csv", index=False)
+        else:
+            pd.DataFrame(columns=[c for c in cols if c != "similarity_score"]).to_csv(outdir / "selected_documents.csv", index=False)
+
+        # facets.json
+        try:
+            with (outdir / "facets.json").open("w", encoding="utf-8") as f:
+                json.dump(facets or {}, f, ensure_ascii=False, indent=2)
+        except Exception:
+            with (outdir / "facets.json").open("w", encoding="utf-8") as f:
+                json.dump({}, f, ensure_ascii=False, indent=2)
+
+        # summary.json
+        with (outdir / "summary.json").open("w", encoding="utf-8") as f:
+            json.dump(summary or {}, f, ensure_ascii=False, indent=2)
+
+        # Time series and charts
+        yearly_df, quarterly_df = self._write_timeseries(df, outdir)
+        self._write_charts(yearly_df, quarterly_df, outdir, category_name)
+
+    def _write_timeseries(self, df: pd.DataFrame, outdir: Path) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        yearly = pd.DataFrame(columns=["year", "count"])  # default empty
+        quarterly = pd.DataFrame(columns=["quarter", "count"])  # default empty
+        # Yearly
+        if df is not None and not df.empty and "publication_year" in df.columns:
+            yearly = (
+                pd.DataFrame({"year": pd.to_numeric(df["publication_year"], errors="coerce")})
+                .dropna()
+                .astype({"year": "Int64"})
+                .groupby("year").size().reset_index(name="count")
+                .sort_values("year")
+            )
+            yearly.to_csv(outdir / "ts_yearly.csv", index=False)
+        else:
+            pd.DataFrame(columns=["year", "count"]).to_csv(outdir / "ts_yearly.csv", index=False)
+
+        # Quarterly
+        date_col = None
+        if df is not None and not df.empty:
+            for c in ["published_on", "publication_date", "published_date", "added_on", "date"]:
+                if c in df.columns:
+                    date_col = c
+                    break
+        if date_col is not None:
+            dt = pd.to_datetime(df[date_col], errors="coerce")
+            qdf = (
+                pd.DataFrame({"year": dt.dt.year, "q": dt.dt.quarter})
+                .dropna()
+                .astype({"year": "Int64", "q": "Int64"})
+            )
+            qdf["quarter"] = qdf.apply(lambda r: f"{int(r['year'])}-Q{int(r['q'])}", axis=1)
+            quarterly = (
+                qdf.groupby(["quarter"]).size().reset_index(name="count").sort_values(["quarter"])
+            )
+            quarterly.to_csv(outdir / "ts_quarterly.csv", index=False)
+        else:
+            pd.DataFrame(columns=["quarter", "count"]).to_csv(outdir / "ts_quarterly.csv", index=False)
+        return yearly, quarterly
+
+    def _write_charts(self, yearly_df: pd.DataFrame, quarterly_df: pd.DataFrame, outdir: Path, category_name: str) -> None:
+        # Match BaseAnalysisModule behaviour: temporarily set default theme
+        current_theme = alt.themes.active
+        alt.themes.enable('default')
+        try:
+            scale_factor = 2
+            # Yearly docs chart
+            if isinstance(yearly_df, pd.DataFrame) and not yearly_df.empty:
+                fig = charts.ts_bar(yearly_df, variable="count", variable_title="Number of policy documents")
+                fig = charts.configure_plots(fig, chart_title=f"Policy documents per year ({category_name})")
+                fig.save(str(outdir / "ts_yearly.png"), scale_factor=scale_factor)
+            # Quarterly docs chart
+            if isinstance(quarterly_df, pd.DataFrame) and not quarterly_df.empty:
+                fig = charts.ts_bar(quarterly_df, variable="count", variable_title="Number of policy documents", time_column="quarter")
+                fig = charts.configure_plots(fig, chart_title=f"Policy documents per quarter ({category_name})")
+                fig.save(str(outdir / "ts_quarterly.png"), scale_factor=scale_factor)
+        except Exception:
+            # Never fail pipeline due to chart errors
+            pass
+        finally:
+            # Restore original theme
+            alt.themes.enable(current_theme)
+
+
diff --git a/discovery_mission_radar/pipeline/config_manager.py b/discovery_mission_radar/pipeline/config_manager.py
index 0b8b0e4..85b8b1e 100644
--- a/discovery_mission_radar/pipeline/config_manager.py
+++ b/discovery_mission_radar/pipeline/config_manager.py
@@ -104,7 +104,7 @@ def _validate_mission_exclusions(self):
     def _validate_data_sources(self):
         """Validate data source configuration and warn about potential issues"""
         data_sources = self._config.get('data_sources', {})
-        required_sources = ['crunchbase', 'gtr', 'hansard']
+        required_sources = ['crunchbase', 'gtr', 'hansard', 'overton']
         
         for source in required_sources:
             if source not in data_sources:
@@ -122,6 +122,12 @@ def _validate_data_sources(self):
                 logger.info(f"Crunchbase native categories configured for {self._mission}: {native_count} categories")
         else:
             logger.info(f"No Crunchbase native categories configured for mission {self._mission}")
+
+        # Validate Overton minimal config
+        overton_cfg = data_sources.get('overton', {})
+        if overton_cfg.get('enabled', False):
+            if 'window_months' not in overton_cfg:
+                logger.warning("Overton enabled but 'window_months' not configured. Defaulting to 60.")
     
     def _validate_date_ranges(self):
         """Validate date range configuration"""
@@ -197,6 +203,15 @@ def _get_default_config(self) -> Dict[str, Any]:
                     'enabled': True, 
                     'run_llm_check': False,
                     'excluded_topics': {'ASF': [], 'AHL': []}
+                },
+                'overton': {
+                    'enabled': False,
+                    'window_months': 60,
+                    'core_sources': [],
+                    'mission_sources': {},
+                    'international': {
+                        'enabled_per_mission': False
+                    }
                 }
             },
             'output': {
@@ -390,6 +405,32 @@ def to_dict(self) -> Dict[str, Any]:
         """Export configuration as dictionary"""
         return self._config.copy()
 
+    # Overton helpers
+    def get_overton_window_months(self) -> int:
+        """Return Overton window in months (default 60)."""
+        try:
+            return int(self._config.get('data_sources', {}).get('overton', {}).get('window_months', 60))
+        except Exception:
+            return 60
+
+    def get_overton_core_sources(self) -> list:
+        """Return Overton core source slugs (list)."""
+        return list(self._config.get('data_sources', {}).get('overton', {}).get('core_sources', []) or [])
+
+    def get_overton_mission_sources(self) -> dict:
+        """Return mission-specific Overton sources mapping."""
+        return dict(self._config.get('data_sources', {}).get('overton', {}).get('mission_sources', {}) or {})
+
+    def get_overton_international_map(self) -> Dict[str, bool]:
+        """Return per-mission international enablement map, defaults to {}."""
+        val = self._config.get('data_sources', {}).get('overton', {}).get('international', {}).get('enabled_per_mission', {})
+        if isinstance(val, dict):
+            return {k.upper(): bool(v) for k, v in val.items()}
+        # Back-compat if it's a boolean flag in older configs: apply to all missions
+        if isinstance(val, bool):
+            return {m: val for m in self.get_available_missions()}
+        return {}
+
 # Global config functions - mission now required
 def get_pipeline_config(mission: str) -> PipelineConfig:
     """Get pipeline configuration instance for specific mission"""
diff --git a/discovery_mission_radar/pipeline/data_sources/__init__.py b/discovery_mission_radar/pipeline/data_sources/__init__.py
index 6c63ddc..9b3a7ac 100644
--- a/discovery_mission_radar/pipeline/data_sources/__init__.py
+++ b/discovery_mission_radar/pipeline/data_sources/__init__.py
@@ -11,6 +11,7 @@
 from .crunchbase import CrunchbaseDataSource
 from .gtr import GtrDataSource
 from .hansard import HansardDataSource
+from .overton import OvertonDataSource
 
 __all__ = [
     # Base classes and utilities
@@ -22,4 +23,5 @@
     'CrunchbaseDataSource',
     'GtrDataSource',
     'HansardDataSource'
+    , 'OvertonDataSource'
 ] 
\ No newline at end of file
diff --git a/discovery_mission_radar/pipeline/data_sources/crunchbase.py b/discovery_mission_radar/pipeline/data_sources/crunchbase.py
index 2a844d2..d78c5c8 100644
--- a/discovery_mission_radar/pipeline/data_sources/crunchbase.py
+++ b/discovery_mission_radar/pipeline/data_sources/crunchbase.py
@@ -77,7 +77,7 @@ def _run_relevance_check(self, companies_df: pd.DataFrame, config: Dict,
     def _get_mission_specific_instructions(self, mission: str) -> str:
         """Get mission/source instructions from central YAML; no in-code prompts."""
         try:
-            base_config_dir = Path(__file__).parent.parent / "config"
+            base_config_dir = Path(__file__).parent.parent.parent / "config"
             prompts = load_llm_prompts(base_config_dir)
             key = mission if mission in prompts else "default"
             mission_prompts = prompts.get(key, {})
diff --git a/discovery_mission_radar/pipeline/data_sources/gtr.py b/discovery_mission_radar/pipeline/data_sources/gtr.py
index e8865ef..ea70e61 100644
--- a/discovery_mission_radar/pipeline/data_sources/gtr.py
+++ b/discovery_mission_radar/pipeline/data_sources/gtr.py
@@ -134,7 +134,7 @@ def _run_relevance_check(self, projects_df: pd.DataFrame, config: Dict,
     def _get_mission_specific_instructions(self, mission: str) -> str:
         """Get mission/source instructions from central YAML; no in-code prompts."""
         try:
-            base_config_dir = Path(__file__).parent.parent / "config"
+            base_config_dir = Path(__file__).parent.parent.parent / "config"
             prompts = load_llm_prompts(base_config_dir)
             key = mission if mission in prompts else "default"
             mission_prompts = prompts.get(key, {})
diff --git a/discovery_mission_radar/pipeline/data_sources/hansard.py b/discovery_mission_radar/pipeline/data_sources/hansard.py
index fd3f859..8706e92 100644
--- a/discovery_mission_radar/pipeline/data_sources/hansard.py
+++ b/discovery_mission_radar/pipeline/data_sources/hansard.py
@@ -138,7 +138,7 @@ def _compose_context(row):
     def _get_mission_specific_instructions(self, mission: str) -> str:
         """Get mission/source instructions from central YAML; no in-code prompts."""
         try:
-            base_config_dir = Path(__file__).parent.parent / "config"
+            base_config_dir = Path(__file__).parent.parent.parent / "config"
             prompts = load_llm_prompts(base_config_dir)
             key = mission if mission in prompts else "default"
             mission_prompts = prompts.get(key, {})
diff --git a/discovery_mission_radar/pipeline/data_sources/overton.py b/discovery_mission_radar/pipeline/data_sources/overton.py
new file mode 100644
index 0000000..5421bf6
--- /dev/null
+++ b/discovery_mission_radar/pipeline/data_sources/overton.py
@@ -0,0 +1,608 @@
+"""Overton policy data source (Step 1).
+
+Implements UK-focused boolean and semantic passes with optional targeted
+source restrictions, plus an optional international strict boolean pass.
+
+This step focuses on search logic, post-filtering, deduplication, facets,
+and summary creation. Output writing is delegated to the analysis module.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import date, timedelta
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import json
+import logging
+import os
+
+import pandas as pd
+
+from discovery_mission_radar.pipeline.data_sources.utils import run_llm_relevance_check
+
+try:
+    from discovery_utils.getters.overton import OvertonGetter, OvertonAPIError
+except Exception:  # pragma: no cover - import guarded for environments without dependency
+    OvertonGetter = None  # type: ignore
+    OvertonAPIError = Exception  # type: ignore
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class OvertonResult:
+    """Container for Overton search results for a single topic.
+
+    Attributes:
+        uk_df (pd.DataFrame): UK results after post-filters and dedupe.
+        intl_df (Optional[pd.DataFrame]): International results if enabled.
+        uk_facets (Dict): Facets snapshot for UK query strategies.
+        intl_facets (Dict): Facets snapshot for international (may be empty).
+        uk_summary (Dict): Summary stats for UK results.
+        intl_summary (Dict): Summary stats for international results.
+    """
+
+    uk_df: pd.DataFrame
+    intl_df: Optional[pd.DataFrame]
+    uk_facets: Dict[str, Any]
+    intl_facets: Dict[str, Any]
+    uk_summary: Dict[str, Any]
+    intl_summary: Dict[str, Any]
+
+
+class OvertonDataSource:
+    """Overton policy search data source.
+
+    Step-1 implementation provides:
+    - Boolean query builder (AND-of-sets with optional OR-all fallback)
+    - Semantic prompt builder
+    - UK boolean and semantic passes with fixed filters
+    - Optional targeted source pass per mission
+    - Optional international strict boolean pass
+    - Title/abstract post-filter for boolean strategies
+    - Doc-series exclusions and Hansard exclusion
+    - Dedupe by Overton `id`
+    - Facets and summary authoring
+    - Output writing is handled by the analysis module, not here
+    """
+
+    # Fallback threshold for switching to OR-all keywords if AND-across-sets yields very few
+    BOOLEAN_FALLBACK_THRESHOLD = 20
+
+    # UK semantic minimum similarity
+    UK_MIN_SIMILARITY = 0.40
+
+    # International (strict) semantic default: OFF in step-1
+    INTL_SEMANTIC_ENABLED_DEFAULT = False
+    INTL_MIN_SIMILARITY = 0.50
+
+    DOC_SERIES_EXCLUDE = {"Transcript", "Press Release"}
+
+    def __init__(
+        self,
+        core_sources: Optional[List[str]] = None,
+        mission_sources: Optional[Dict[str, List[str]]] = None,
+        international_enabled_for: Optional[Dict[str, bool]] = None,
+    ) -> None:
+        """Initialise the data source.
+
+        Args:
+            core_sources (Optional[List[str]]): Optional set of source slugs for a UK targeted pass.
+            mission_sources (Optional[Dict[str, List[str]]]): Optional per-mission source slugs.
+            international_enabled_for (Optional[Dict[str, bool]]): Per-mission flag for international strict pass.
+        """
+        self.core_sources = core_sources or []
+        self.mission_sources = mission_sources or {}
+        self.international_enabled_for = international_enabled_for or {}
+
+    def fetch_topic(
+        self,
+        topic_cfg: Dict[str, Any],
+        mission: str,
+        window_months: int,
+        cache_dir: Optional[Path] = None,
+        pipeline_config: Optional[Dict[str, Any]] = None,
+    ) -> OvertonResult:
+        """Fetch Overton results for a topic.
+
+    Args:
+            topic_cfg (Dict[str, Any]): Topic configuration dictionary.
+            mission (str): Mission key, e.g. "AHL" or "ASF".
+            window_months (int): Backwards window in months (default 60) for date filter.
+
+    Returns:
+            OvertonResult: Result container with data frames, facets and summaries.
+        """
+        if not os.getenv("OVERTON_API_KEY"):
+            logger.warning("OVERTON_API_KEY is not set. Skipping Overton for this run.")
+            empty = pd.DataFrame()
+            return OvertonResult(
+                uk_df=empty,
+                intl_df=None,
+                uk_facets={},
+                intl_facets={},
+                uk_summary=self._summarise_df(empty, tag="uk"),
+                intl_summary={},
+            )
+
+        if OvertonGetter is None:
+            logger.warning("discovery_utils OvertonGetter not available. Skipping.")
+            empty = pd.DataFrame()
+            return OvertonResult(
+                uk_df=empty,
+                intl_df=None,
+                uk_facets={},
+                intl_facets={},
+                uk_summary=self._summarise_df(empty, tag="uk"),
+                intl_summary={},
+            )
+
+        getter = OvertonGetter()
+
+        recipe = topic_cfg.get("search_recipe", {}) or {}
+        keyword_sets = self._extract_keyword_sets(recipe)
+        scope_statements = self._extract_scope_statements(recipe)
+
+        boolean_query_and = self._build_boolean_query(keyword_sets)
+        boolean_query_or = self._build_or_all_keywords(keyword_sets)
+        semantic_prompt = self._build_semantic_prompt(scope_statements)
+
+        published_after = self._compute_published_after(window_months)
+
+        # --- UK passes ---
+        uk_frames: List[pd.DataFrame] = []
+        uk_facets: Dict[str, Any] = {}
+
+        # Boolean (AND-of-sets) with fallback OR-all if very few
+        primary_boolean_query = boolean_query_and or boolean_query_or
+        uk_boolean_df = self._run_uk_boolean_pass(
+            getter,
+            primary_boolean_query,
+            keyword_sets,
+            published_after,
+        )
+        # Fallback to OR-all if low results and OR query exists
+        if boolean_query_or and len(uk_boolean_df) < self.BOOLEAN_FALLBACK_THRESHOLD and boolean_query_or != primary_boolean_query:
+            uk_boolean_or_df = self._run_uk_boolean_pass(
+                getter,
+                boolean_query_or,
+                keyword_sets,
+                published_after,
+            )
+            if len(uk_boolean_or_df) > len(uk_boolean_df):
+                uk_boolean_df = uk_boolean_or_df
+        if not uk_boolean_df.empty:
+            uk_frames.append(uk_boolean_df)
+        # Facets for boolean
+        try:
+            if primary_boolean_query:
+                uk_facets["boolean"] = getter.get_facets(query=primary_boolean_query)
+        except OvertonAPIError:
+            uk_facets["boolean"] = {}
+
+        # Targeted mission/core sources (boolean)
+        targeted_sources = self._get_targeted_sources(mission)
+        if targeted_sources and primary_boolean_query:
+            targeted_frames: List[pd.DataFrame] = []
+            for source_slug in targeted_sources:
+                try:
+                    part = getter.search_documents(
+                        query=primary_boolean_query,
+                        semantic_search=False,
+                        source=source_slug,
+                        source_country="UK",
+                        source_type="government",
+                        published_after=published_after,
+                    )
+                except Exception:
+                    part = None
+                if part is not None and not part.empty:
+                    part = self._post_filter_boolean(part, keyword_sets)
+                    part = self._exclude_series_and_hansard(part)
+                    targeted_frames.append(part)
+            if targeted_frames:
+                uk_frames.append(pd.concat(targeted_frames, ignore_index=True))
+
+        # Semantic
+        if semantic_prompt:
+            uk_semantic_df = self._run_uk_semantic_pass(
+                getter,
+                semantic_prompt,
+                published_after,
+            )
+            if not uk_semantic_df.empty:
+                uk_frames.append(uk_semantic_df)
+            try:
+                uk_facets["semantic"] = getter.get_facets(query=semantic_prompt)
+            except OvertonAPIError:
+                uk_facets["semantic"] = {}
+
+        uk_df = self._dedupe_concat(uk_frames)
+        # LLM relevance check (UK)
+        try:
+            uk_df = self._apply_llm_filter(
+                uk_df, topic_cfg, mission, cache_dir, pipeline_config, source_label="overton_uk"
+            )
+        except Exception as e:
+            logger.warning(f"Overton UK LLM relevance check skipped due to error: {e}")
+        uk_summary = self._summarise_df(uk_df, tag="uk")
+
+        # --- International strict (boolean only by default) ---
+        intl_df: Optional[pd.DataFrame] = None
+        intl_facets: Dict[str, Any] = {}
+        intl_summary: Dict[str, Any] = {}
+
+        if self._is_international_enabled(mission) and primary_boolean_query:
+            intl_frames: List[pd.DataFrame] = []
+            for src_type in ["government", "igo"]:
+                try:
+                    part = getter.search_documents(
+                        query=primary_boolean_query,
+                        semantic_search=False,
+                        source_type=src_type,
+                        source_country="OECD members",  # mapped to region by getter
+                        published_after=published_after,
+                    )
+                except Exception:
+                    part = None
+                if part is not None and not part.empty:
+                    part = self._post_filter_boolean(part, keyword_sets)
+                    part = self._exclude_series_and_hansard(part)
+                    intl_frames.append(part)
+            intl_df = self._dedupe_concat(intl_frames)
+            # LLM relevance check (International)
+            try:
+                intl_df = self._apply_llm_filter(
+                    intl_df, topic_cfg, mission, cache_dir, pipeline_config, source_label="overton_international"
+                )
+            except Exception as e:
+                logger.warning(f"Overton International LLM relevance check skipped due to error: {e}")
+            # Cross-pass dedupe: remove any documents already included in UK pass
+            try:
+                if intl_df is not None and not intl_df.empty and uk_df is not None and not uk_df.empty:
+                    if "id" in intl_df.columns and "id" in uk_df.columns:
+                        uk_ids = set(uk_df["id"].tolist())
+                        intl_df = intl_df[~intl_df["id"].isin(uk_ids)]
+            except Exception:
+                # Be defensive: never fail the run due to cross-dedupe issues
+                pass
+            intl_summary = self._summarise_df(intl_df, tag="international")
+            # Facets (optional in step-1)
+            try:
+                intl_facets["boolean"] = getter.get_facets(query=primary_boolean_query)
+            except OvertonAPIError:
+                intl_facets["boolean"] = {}
+
+        return OvertonResult(
+            uk_df=uk_df,
+            intl_df=intl_df,
+            uk_facets=uk_facets,
+            intl_facets=intl_facets,
+            uk_summary=uk_summary,
+            intl_summary=intl_summary,
+        )
+
+    # ------------------------ LLM relevance filter ------------------------
+    def _apply_llm_filter(
+        self,
+        df: pd.DataFrame,
+        topic_cfg: Dict[str, Any],
+        mission: str,
+        cache_dir: Optional[Path],
+        pipeline_config: Optional[Dict[str, Any]],
+        source_label: str,
+    ) -> pd.DataFrame:
+        if df is None or df.empty:
+            return df
+        # Respect optional run_llm_check flag if present in pipeline config; default to True
+        try:
+            run_llm = pipeline_config.get("data_sources", {}).get("overton", {}).get("run_llm_check", True) if pipeline_config else True
+        except Exception:
+            run_llm = True
+        if not run_llm:
+            return df
+
+        topic_name = (topic_cfg.get("search_recipe", {}) or {}).get("category_name", "topic")
+        # Compose richer LLM text: concise metadata header + best-available content + match context
+        items = df.copy()
+        if "id" not in items.columns:
+            return df
+
+        def _first_n(values: Any, n: int) -> str:
+            try:
+                if isinstance(values, list):
+                    return ", ".join([str(v) for v in values[:n]])
+            except Exception:
+                pass
+            return str(values) if values is not None else ""
+
+        def _select_body(row: Dict[str, Any]) -> str:
+            for field in ["content", "abstract", "snippet"]:
+                val = row.get(field)
+                if isinstance(val, str) and val.strip():
+                    return val.strip()
+            return ""
+
+        def _compose_text(row):
+            title = str(row.get("title", "") or "").strip()
+            venue = str(row.get("venue", "") or "").strip()
+            src_type = str(row.get("source_type", "") or "").strip()
+            src_country = str(row.get("source_country", "") or "").strip()
+            series = str(row.get("overton_policy_document_series", "") or "").strip()
+            year = str(row.get("publication_year", "") or row.get("published_on", "") or "").strip()
+            topics = _first_n(row.get("topics", []), 6)
+            classes = _first_n(row.get("classifications", []), 6)
+            url = str(row.get("overton_url", "") or "").strip()
+            url_ctx = str(row.get("overton_url_with_context", "") or "").strip()
+
+            header_lines = []
+            if title:
+                header_lines.append(f"Title: {title}")
+            meta_parts = []
+            if venue:
+                meta_parts.append(f"Venue: {venue}")
+            if src_type or src_country:
+                meta_parts.append(f"Source: {src_type} ({src_country})".strip())
+            if series:
+                meta_parts.append(f"Series: {series}")
+            if meta_parts:
+                header_lines.append(" | ".join(meta_parts))
+            info_parts = []
+            if year:
+                info_parts.append(f"Year: {year}")
+            if topics:
+                info_parts.append(f"Topics: {topics}")
+            if classes:
+                info_parts.append(f"Classifications: {classes}")
+            if info_parts:
+                header_lines.append(" | ".join(info_parts))
+            if url or url_ctx:
+                header_lines.append(f"URL: {url_ctx or url}")
+
+            body = _select_body(row)
+            # Append explicit match context if available
+            snip = str(row.get("snippet", "") or "").strip()
+            highlights = row.get("highlights", [])
+            ctx_parts = []
+            if snip:
+                ctx_parts.append(f"Snippet: {snip}")
+            if isinstance(highlights, list) and highlights:
+                ctx_parts.append("Highlights: " + " | ".join([str(h) for h in highlights[:3]]))
+
+            sections = ["\n".join(header_lines)]
+            if body:
+                sections.append(body)
+            if ctx_parts:
+                sections.append("\n".join(ctx_parts))
+            return "\n\n".join([s for s in sections if s])
+
+        items["text"] = items.apply(_compose_text, axis=1)
+        # Fallbacks if entirely empty text
+        if items["text"].fillna("").str.len().sum() == 0:
+            if "overton_url" in items.columns:
+                items["text"] = items["overton_url"].astype(str)
+
+        # Determine cache directory
+        safe_cache_dir = cache_dir or (Path("outputs") / ".cache" / mission.lower())
+        safe_cache_dir.mkdir(parents=True, exist_ok=True)
+
+        # Run LLM relevance check
+        matching_ids = run_llm_relevance_check(
+            items_df=items[["id", "text"]],
+            config=topic_cfg,
+            cache_dir=safe_cache_dir,
+            topic_name=topic_name,
+            source_name=source_label,
+            mission=mission,
+            pipeline_config=pipeline_config,
+            id_column="id",
+            text_column="text",
+            custom_instructions="",
+        )
+        if not matching_ids:
+            return df.iloc[0:0]
+        return df[df["id"].isin(matching_ids)]
+
+    # ------------------------ Internals ------------------------
+    def _extract_keyword_sets(self, recipe: Dict[str, Any]) -> List[List[str]]:
+        sets_cfg = recipe.get("keyword_sets", []) or []
+        keyword_sets: List[List[str]] = []
+        for ks in sets_cfg:
+            kws = ks.get("keywords", []) or []
+            clean = [str(k).strip() for k in kws if k and str(k).strip()]
+            keyword_sets.append(clean)
+        return keyword_sets
+
+    def _extract_scope_statements(self, recipe: Dict[str, Any]) -> List[str]:
+        scopes = recipe.get("scope_statements", []) or []
+        return [str(s).strip() for s in scopes if s and str(s).strip()]
+
+    def _build_boolean_query(self, keyword_sets: List[List[str]]) -> Optional[str]:
+        groups: List[str] = []
+        for kws in keyword_sets:
+            if not kws:
+                continue
+            terms = [f'"{k}"' if " " in k else k for k in kws]
+            groups.append("( " + " OR ".join(terms) + " )")
+        if not groups:
+            return None
+        return " AND ".join(groups)
+
+    def _build_or_all_keywords(self, keyword_sets: List[List[str]]) -> Optional[str]:
+        all_kws = [k for group in keyword_sets for k in group]
+        if not all_kws:
+            return None
+        terms = [f'"{k}"' if " " in k else k for k in all_kws]
+        return "( " + " OR ".join(terms) + " )"
+
+    def _build_semantic_prompt(self, scope_statements: List[str]) -> Optional[str]:
+        cleaned = [s for s in scope_statements if s]
+        if not cleaned:
+            return None
+        return " ".join(cleaned)
+
+    def _compute_published_after(self, window_months: int) -> str:
+        days = int(round(window_months * 30.4167))
+        return (date.today() - timedelta(days=days)).isoformat()
+
+    def _post_filter_boolean(self, df: pd.DataFrame, keyword_sets: List[List[str]]) -> pd.DataFrame:
+        if df is None or df.empty:
+            return df
+        # Exclude transcripts/press releases
+        df = self._exclude_series_and_hansard(df)
+        # Title/abstract contains at least one keyword (case-insensitive)
+        all_keywords = [k.strip('"').lower() for group in keyword_sets for k in group]
+        if not all_keywords:
+            return df
+
+        def _contains_kw(text: Any) -> bool:
+            t = str(text or "").lower()
+            for kw in all_keywords:
+                if kw and kw in t:
+                    return True
+            return False
+
+        has_title = "title" in df.columns
+        has_abs = "abstract" in df.columns
+        if has_title or has_abs:
+            mask = False
+            if has_title:
+                mask = df["title"].apply(_contains_kw)
+            if has_abs:
+                mask = mask | df["abstract"].apply(_contains_kw)
+            df = df[mask]
+        # Dedupe by id if present
+        if "id" in df.columns:
+            df = df.drop_duplicates(subset=["id"])  # keep first
+        return df
+
+    def _exclude_series_and_hansard(self, df: pd.DataFrame) -> pd.DataFrame:
+        if df is None or df.empty:
+            return df
+        if "overton_policy_document_series" in df.columns:
+            df = df[~df["overton_policy_document_series"].isin(self.DOC_SERIES_EXCLUDE)]
+        if "source" in df.columns:
+            df = df[df["source"] != "hansard_uk"]
+        return df
+
+    def _run_uk_boolean_pass(
+        self,
+        getter: Any,
+        query_text: Optional[str],
+        keyword_sets: List[List[str]],
+        published_after: str,
+    ) -> pd.DataFrame:
+        if not query_text:
+            return pd.DataFrame()
+        try:
+            df = getter.search_documents(
+                query=query_text,
+                semantic_search=False,
+                source_country="UK",
+                source_type="government",
+                published_after=published_after,
+            )
+        except Exception:
+            return pd.DataFrame()
+        if df is None or df.empty:
+            return pd.DataFrame()
+        df = self._post_filter_boolean(df, keyword_sets)
+        return df
+
+    def _run_uk_semantic_pass(
+        self,
+        getter: Any,
+        prompt: Optional[str],
+        published_after: str,
+    ) -> pd.DataFrame:
+        if not prompt:
+            return pd.DataFrame()
+        try:
+            df = getter.search_documents(
+                query=prompt,
+                semantic_search=True,
+                source_country="UK",
+                source_type="government",
+                published_after=published_after,
+            )
+        except Exception:
+            return pd.DataFrame()
+        if df is None or df.empty:
+            return pd.DataFrame()
+        # Exclude series and Hansard, apply similarity threshold if available
+        df = self._exclude_series_and_hansard(df)
+        if "similarity_score" in df.columns:
+            df = df[df["similarity_score"] >= self.UK_MIN_SIMILARITY]
+        if "id" in df.columns:
+            df = df.drop_duplicates(subset=["id"])  # keep first
+        return df
+
+    def _dedupe_concat(self, frames: List[pd.DataFrame]) -> pd.DataFrame:
+        frames = [f for f in frames if f is not None and not f.empty]
+        if not frames:
+            return pd.DataFrame()
+        combined = pd.concat(frames, ignore_index=True)
+        if "id" in combined.columns:
+            combined = combined.drop_duplicates(subset=["id"])  # keep first
+        return combined
+
+    def _summarise_df(self, df: pd.DataFrame, tag: str) -> Dict[str, Any]:
+        if df is None or df.empty:
+            return {"tag": tag, "num_results": 0, "source_type_counts": {}, "source_country_counts": {}, "year_span": {"min": None, "max": None}}
+        st_counts = df["source_type"].value_counts(dropna=False).to_dict() if "source_type" in df.columns else {}
+        sc_counts = df["source_country"].value_counts(dropna=False).to_dict() if "source_country" in df.columns else {}
+        def _to_native(value):
+            try:
+                import numpy as np  # noqa: F401
+                import math
+                if hasattr(value, "item"):
+                    value = value.item()
+                if isinstance(value, float) and (math.isnan(value)):
+                    return None
+            except Exception:
+                pass
+            return value
+        years_min = None
+        years_max = None
+        if "publication_year" in df.columns:
+            years = pd.to_numeric(df["publication_year"], errors="coerce")
+            if not years.dropna().empty:
+                years_min = _to_native(years.min())
+                years_max = _to_native(years.max())
+        st_counts_native = {str(k): _to_native(v) for k, v in st_counts.items()}
+        sc_counts_native = {str(k): _to_native(v) for k, v in sc_counts.items()}
+        return {
+            "tag": tag,
+            "num_results": int(len(df)),
+            "source_type_counts": st_counts_native,
+            "source_country_counts": sc_counts_native,
+            "year_span": {"min": years_min, "max": years_max},
+        }
+
+    def _get_targeted_sources(self, mission: str) -> List[str]:
+        targeted = list(self.core_sources)
+        targeted += self.mission_sources.get(mission, [])
+        # Preserve order and uniqueness
+        seen = set()
+        uniq: List[str] = []
+        for s in targeted:
+            if s and s not in seen:
+                seen.add(s)
+                uniq.append(s)
+        return uniq
+
+    def _is_international_enabled(self, mission: str) -> bool:
+        return bool(self.international_enabled_for.get(mission, False))
+
+    def _slugify(self, text: str) -> str:
+        import re
+        t = (text or "").strip().lower()
+        if not t:
+            return "topic"
+        slug = re.sub(r"[^a-z0-9]+", "_", t)
+        slug = re.sub(r"_+", "_", slug).strip("_")
+        return slug or "topic"
+
diff --git a/discovery_mission_radar/pipeline/runner.py b/discovery_mission_radar/pipeline/runner.py
index 5452d0c..ca6afe8 100644
--- a/discovery_mission_radar/pipeline/runner.py
+++ b/discovery_mission_radar/pipeline/runner.py
@@ -9,9 +9,9 @@
 import yaml
 
 from discovery_utils.getters import crunchbase as cb_getters, gtr as gtr_getters, hansard as hansard_getters
-from discovery_mission_radar.pipeline.data_sources import CrunchbaseDataSource, GtrDataSource, HansardDataSource
+from discovery_mission_radar.pipeline.data_sources import CrunchbaseDataSource, GtrDataSource, HansardDataSource, OvertonDataSource
 from discovery_mission_radar.pipeline.analysis import (
-    CrunchbaseAnalysisModule, GtrAnalysisModule, HansardAnalysisModule
+    CrunchbaseAnalysisModule, GtrAnalysisModule, HansardAnalysisModule, OvertonAnalysisModule
 )
 from discovery_mission_radar.pipeline.analysis import consolidation, aggregation
 from discovery_mission_radar.pipeline.config_manager import get_pipeline_config
@@ -56,7 +56,8 @@ def __init__(self, mission: str, config_dir: Path = None, output_dir: Path = Non
         self.analysis_modules = {
             'crunchbase': CrunchbaseAnalysisModule(self.mission),
             'gtr': GtrAnalysisModule(self.mission),
-            'hansard': HansardAnalysisModule(self.mission)
+            'hansard': HansardAnalysisModule(self.mission),
+            'overton': OvertonAnalysisModule(self.mission)
         }
         
         logger.info(f"Initialized MissionRadarRunner with mission={self.mission}, config_dir={config_dir}, output_dir={self.output_dir}")
@@ -80,6 +81,7 @@ def _initialize_getters(self):
         if self.pipeline_config.is_source_enabled('hansard'):
             logger.info("Initialising Hansard getter")
             self.getters['hansard'] = hansard_getters.HansardGetter()
+        # Overton uses discovery_utils getter internally; no global init here
         
         logger.info("All getters initialized successfully")
     
@@ -97,6 +99,17 @@ def _initialize_data_sources(self):
             
         if self.pipeline_config.is_source_enabled('hansard'):
             self.data_sources['hansard'] = HansardDataSource()
+        if self.pipeline_config.is_source_enabled('overton'):
+            # Prepare Overton data source with config-driven options
+            core_sources = self.pipeline_config.get_overton_core_sources()
+            mission_sources = self.pipeline_config.get_overton_mission_sources()
+            # International enablement is per-mission map
+            intl_map = self.pipeline_config.get_overton_international_map()
+            self.data_sources['overton'] = OvertonDataSource(
+                core_sources=core_sources,
+                mission_sources=mission_sources,
+                international_enabled_for=intl_map,
+            )
         
         logger.info("All data sources initialized successfully")
     
@@ -192,6 +205,48 @@ def run_topic_end_to_end(self, topic_name: str) -> Dict[str, Any]:
                 logger.info("Skipping Hansard data source (no config available)")
         else:
             logger.info("Skipping Hansard data source (excluded or disabled)")
+
+        # Overton data source
+        if self.pipeline_config.should_run_source_for_topic(topic_name, 'overton'):
+            if has_config:
+                logger.info("Processing Overton data source")
+                # window_months from config; default 60 handled in config manager
+                window_months = self.pipeline_config.get_overton_window_months()
+                # International enablement already embedded in data source at init for current mission
+                try:
+                    import os as _os
+                    if not _os.getenv('OVERTON_API_KEY'):
+                        logger.warning("OVERTON_API_KEY not set; skipping Overton for this run")
+                    else:
+                        result = self.data_sources['overton'].fetch_topic(
+                            topic_cfg=topic_config,
+                            mission=self.mission,
+                            window_months=window_months,
+                            cache_dir=self.cache_dir,
+                            pipeline_config=self.pipeline_config.to_dict(),
+                        )
+                        self.analysis_modules['overton'].write_outputs(
+                            topic_config,
+                            self.mission,
+                            result.uk_df,
+                            result.intl_df,
+                            result.uk_facets,
+                            result.intl_facets,
+                            result.uk_summary,
+                            result.intl_summary,
+                        )
+                        results['overton'] = {
+                            'stats': {
+                                'uk': result.uk_summary,
+                                'international': result.intl_summary,
+                            }
+                        }
+                except Exception as e:
+                    logger.warning(f"Overton processing failed for {topic_name}: {e}")
+            else:
+                logger.info("Skipping Overton data source (no config available)")
+        else:
+            logger.info("Skipping Overton data source (excluded or disabled)")
         
         if has_config:
             results['config'] = topic_config
diff --git a/pyproject.toml b/pyproject.toml
index 99a2fd2..2cb3cb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,10 @@ dependencies = [
     "structlog>=22.0.0",
     "vl-convert-python>=0.2.2",
     "discovery-utils>=0.1.1",
+    "datasets>=2.20.0",
+    "sentence-transformers>=3.4.1",
+    "pyarrow>=21.0.0",
+    "langfuse==2.51.3",
 ]
 
 [project.urls]
diff --git a/uv.lock b/uv.lock
index ad3b830..23b1cf9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,7 +10,7 @@ resolution-markers = [
 
 [[package]]
 name = "accelerate"
-version = "1.10.0"
+version = "1.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
@@ -21,9 +21,9 @@ dependencies = [
     { name = "safetensors" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261 }
+sdist = { url = "https://files.pythonhosted.org/packages/b1/72/ff3961c19ee395c3d30ac630ee77bfb0e1b46b87edc504d4f83bb4a89705/accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8", size = 392446 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718 },
+    { url = "https://files.pythonhosted.org/packages/5f/a0/d9ef19f780f319c21ee90ecfef4431cbeeca95bec7f14071785c17b6029b/accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11", size = 374909 },
 ]
 
 [[package]]
@@ -277,15 +277,15 @@ wheels = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.13.4"
+version = "4.13.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "soupsieve" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/e4/0c4c39e18fd76d6a628d4dd8da40543d136ce2d1752bd6eeeab0791f4d6b/beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195", size = 621067 }
+sdist = { url = "https://files.pythonhosted.org/packages/85/2e/3e5079847e653b1f6dc647aa24549d68c6addb4c595cc0d902d1b19308ad/beautifulsoup4-4.13.5.tar.gz", hash = "sha256:5e70131382930e7c3de33450a2f54a63d5e4b19386eab43a5b34d594268f3695", size = 622954 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285 },
+    { url = "https://files.pythonhosted.org/packages/04/eb/f4151e0c7377a6e08a38108609ba5cede57986802757848688aeedd1b9e8/beautifulsoup4-4.13.5-py3-none-any.whl", hash = "sha256:642085eaa22233aceadff9c69651bc51e8bf3f874fb6d7104ece2beb24b47c4a", size = 105113 },
 ]
 
 [[package]]
@@ -309,30 +309,30 @@ wheels = [
 
 [[package]]
 name = "boto3"
-version = "1.40.13"
+version = "1.40.17"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore" },
     { name = "jmespath" },
     { name = "s3transfer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e3/f2/3f7e2b2d866cdce0fceca7aa19baba939291200b7087defc6063b183d7cc/boto3-1.40.13.tar.gz", hash = "sha256:7ff74221328868c5f7dcd7a9b959e7b882c1ab1b04cc9df868ea70de7d8bd004", size = 111961 }
+sdist = { url = "https://files.pythonhosted.org/packages/71/b1/0f91a07dc87485a791f2d31df1c2dc75dc5d302fbf002e5446ffa04e8e32/boto3-1.40.17.tar.gz", hash = "sha256:e115dc87d5975d32dfa0ebaf19c39e360665317a350004fa94b03200fe853f2e", size = 112054 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/63/43a2eaad011b0025ce12a2ffa6d1fea9a34d06b7d33bf32c54cc1eb2df65/boto3-1.40.13-py3-none-any.whl", hash = "sha256:b43110b6ad30e683fab769b4c5659b6b0606afa535627de831f7181306009d5e", size = 140075 },
+    { url = "https://files.pythonhosted.org/packages/91/b5/1973ed8c107beb2fe5a510d5609603537a4e18a920ead021bed5699c5437/boto3-1.40.17-py3-none-any.whl", hash = "sha256:2cacecd689cb51d81fbf54f84b64d0e6e922fbc18ee513c568b9f61caf4221e0", size = 140076 },
 ]
 
 [[package]]
 name = "botocore"
-version = "1.40.13"
+version = "1.40.17"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jmespath" },
     { name = "python-dateutil" },
     { name = "urllib3" },
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+sdist = { url = "https://files.pythonhosted.org/packages/55/87/92b520e7f4ac2a64c6e68bedc1dc3c1335a5aa1efb89a4247b45e34e40ac/botocore-1.40.17.tar.gz", hash = "sha256:769cd04a6a612f2d48b5f456c676fd81733fab682870952f7e2887260ea6a2bc", size = 14375996 }
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+    { url = "https://files.pythonhosted.org/packages/67/fd/bcc913de90d6f0549a04ee40caaf7d4e76efb5a622fc6cc47379f686e50f/botocore-1.40.17-py3-none-any.whl", hash = "sha256:603951935c1a741ae70236bf15725c5293074f28503e7029ad0e24ece476a342", size = 14035937 },
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