diff --git a/inference.py b/inference.py
index 18e2a82..6dca2e4 100644
--- a/inference.py
+++ b/inference.py
@@ -1,14 +1,404 @@
-"""Root-level entry point required by hackathon judges.
-
-Delegates entirely to src/inference.py so all logic stays in one place.
-Usage: python inference.py --input dataset.json --output results.json
 """
-import sys
-import os
+BIS SP-21 Hybrid Retrieval System
+----------------------------------
+Combines dense (FAISS + sentence-transformers) and sparse (BM25) search,
+then re-ranks and deduplicates to return the top-5 unique IS standards.
 
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
+Usage
+-----
+   # Index build (one-time, caches to data/processed/):
+   python inference.py --build
+
+   # Single query:
+   python inference.py --query "Which standard covers 33 grade OPC cement?"
+
+   # Batch from JSON file:
+   python inference.py --input data/processed/public_test_set.json
+
+   # Batch + write results JSON:
+   python inference.py --input data/processed/public_test_set.json \
+                          --output data/processed/retrieval_results.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import re
+import time
+from pathlib import Path
+from typing import Any
+
+import faiss
+import numpy as np
+from rank_bm25 import BM25Okapi
+from sentence_transformers import SentenceTransformer
+
+# ---------------------------------------------------------------------------
+# Paths
+# ---------------------------------------------------------------------------
+_ROOT = Path(__file__).resolve().parent
+_CHUNKS_PATH = _ROOT / "data/processed/standards_chunks.json"
+_STANDARDS_PATH = _ROOT / "data/processed/standards.json"
+_EMBED_CACHE = _ROOT / "data/processed/embeddings.npy"
+_INDEX_CACHE = _ROOT / "data/processed/faiss.index"
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+_MODEL_NAME = "all-MiniLM-L6-v2"
+_TOP_K_DENSE = 10
+_TOP_K_SPARSE = 10
+_TOP_N_FINAL = 5
+_SHORT_CHUNK_THRESHOLD = 40   # body words below this get a penalty
+_SHORT_CHUNK_PENALTY = 0.15
+
+
+# ---------------------------------------------------------------------------
+# Text helpers
+# ---------------------------------------------------------------------------
+def _body_text(chunk_text: str) -> str:
+    """Strip the leading 'IS XXXX: YYYY Title [Section]' prefix line."""
+    parts = chunk_text.strip().split("\n", 1)
+    return parts[1].strip() if len(parts) > 1 else parts[0]
+
+
+def _tokenize(text: str) -> list[str]:
+    """Lowercase word tokenizer for BM25."""
+    return re.findall(r"[a-z0-9]+", text.lower())
+
+
+def _bm25_doc(chunk: dict) -> list[str]:
+    """
+    Build the BM25 document for a chunk.
+
+    Uses the full title from standards.json (stored in chunk["full_title"] by
+    load_or_build) to avoid truncated-title misses.  Title is repeated ×4 so
+    an exact title match dominates over body-text noise.
+    """
+    # full_title is injected by load_or_build; fall back to chunk title
+    title = chunk.get("full_title") or chunk.get("title", "")
+    title_tokens = _tokenize(title)
+    kw_tokens = _tokenize(" ".join(chunk.get("keywords", [])))
+    section_tokens = _tokenize(chunk.get("section", ""))
+    text_tokens = _tokenize(_body_text(chunk.get("text", "")))
+    return title_tokens * 4 + kw_tokens * 3 + section_tokens * 2 + text_tokens
+
+
+def _norm_std_id(sid: str) -> str:
+    return re.sub(r"\s+", " ", sid).strip().upper()
+
+
+# ---------------------------------------------------------------------------
+# Index builder
+# ---------------------------------------------------------------------------
+class RetrievalIndex:
+    def __init__(
+        self,
+        chunks: list[dict],
+        standards: list[dict],
+        model: SentenceTransformer,
+    ) -> None:
+        self.chunks = chunks
+        self.standards = standards
+        self.model = model
+
+        # Build lookup: standard_id → standard record
+        self.std_lookup: dict[str, dict] = {
+            _norm_std_id(s["standard_id"]): s for s in standards
+        }
+
+        # Build per-standard keyword set for boosting
+        self.std_keywords: dict[str, set[str]] = {
+            _norm_std_id(s["standard_id"]): set(_tokenize(" ".join(s.get("keywords", []))))
+            for s in standards
+        }
+
+        # Dense index (FAISS)
+        self.faiss_index: faiss.IndexFlatIP | None = None
+        self.embeddings: np.ndarray | None = None
+
+        # Sparse index (BM25)
+        self.bm25: BM25Okapi | None = None
+        self._bm25_docs: list[list[str]] = []
+
+    # ------------------------------------------------------------------
+    def build(self, use_cache: bool = True) -> None:
+        self._build_dense(use_cache)
+        self._build_sparse()
+
+    def _build_dense(self, use_cache: bool) -> None:
+        if use_cache and _EMBED_CACHE.exists() and _INDEX_CACHE.exists():
+            print("Loading cached embeddings and FAISS index…")
+            self.embeddings = np.load(str(_EMBED_CACHE))
+            self.faiss_index = faiss.read_index(str(_INDEX_CACHE))
+            return
+
+        print(f"Encoding {len(self.chunks)} chunks with {_MODEL_NAME}…")
+        texts = [c["text"] for c in self.chunks]
+        emb = self.model.encode(
+            texts,
+            batch_size=64,
+            show_progress_bar=True,
+            normalize_embeddings=True,   # cosine via inner product
+        )
+        self.embeddings = emb.astype(np.float32)
+
+        dim = self.embeddings.shape[1]
+        self.faiss_index = faiss.IndexFlatIP(dim)
+        self.faiss_index.add(self.embeddings)
+
+        np.save(str(_EMBED_CACHE), self.embeddings)
+        faiss.write_index(self.faiss_index, str(_INDEX_CACHE))
+        print(f"FAISS index built: {self.faiss_index.ntotal} vectors, dim={dim}")
+
+    def _build_sparse(self) -> None:
+        print("Building BM25 index…")
+        self._bm25_docs = [_bm25_doc(c) for c in self.chunks]
+        self.bm25 = BM25Okapi(self._bm25_docs)
+        print("BM25 index built.")
+
+
+# ---------------------------------------------------------------------------
+# Retrieval
+# ---------------------------------------------------------------------------
+class Retriever:
+    def __init__(self, index: RetrievalIndex) -> None:
+        self.idx = index
+
+    def retrieve(self, query: str, top_n: int = _TOP_N_FINAL) -> list[dict]:
+        t0 = time.perf_counter()
+
+        query_tokens = _tokenize(query)
+
+        # --- Dense retrieval ---
+        q_emb = self.idx.model.encode(
+            [query], normalize_embeddings=True
+        ).astype(np.float32)
+        dense_scores, dense_ids = self.idx.faiss_index.search(q_emb, _TOP_K_DENSE)
+        dense_scores = dense_scores[0]
+        dense_ids = dense_ids[0]
+
+        # Normalise dense scores (already cosine, range ~[-1, 1] → shift to [0, 1])
+        d_min, d_max = dense_scores.min(), dense_scores.max()
+        d_range = d_max - d_min if d_max > d_min else 1.0
+        dense_norm = {int(i): (s - d_min) / d_range for i, s in zip(dense_ids, dense_scores)}
+
+        # --- Sparse retrieval ---
+        bm25_raw = self.idx.bm25.get_scores(query_tokens)
+        top_sparse_ids = np.argsort(bm25_raw)[::-1][:_TOP_K_SPARSE]
+        top_sparse_scores = bm25_raw[top_sparse_ids]
+
+        s_max = top_sparse_scores.max() if top_sparse_scores.max() > 0 else 1.0
+        sparse_norm = {int(i): s / s_max for i, s in zip(top_sparse_ids, top_sparse_scores)}
+
+        # --- Merge candidates ---
+        candidate_ids = set(dense_norm) | set(sparse_norm)
+        chunk_scores: dict[int, float] = {}
+        for cid in candidate_ids:
+            d = dense_norm.get(cid, 0.0)
+            s = sparse_norm.get(cid, 0.0)
+            chunk_scores[cid] = 0.6 * d + 0.4 * s   # weighted fusion
+
+        # --- Re-ranking ---
+        query_lower = query.lower()
+        query_words = set(query_tokens)
+
+        for cid, base in list(chunk_scores.items()):
+            chunk = self.idx.chunks[cid]
+            sid_norm = _norm_std_id(chunk["standard_id"])
+            bonus = 0.0
+
+            # Use the authoritative full title for all title-based signals
+            full_title = chunk.get("full_title") or chunk.get("title", "")
+            full_title_tokens = set(_tokenize(full_title))
+
+            # Boost: keyword overlap with query
+            kw_set = self.idx.std_keywords.get(sid_norm, set())
+            kw_overlap = len(kw_set & query_words)
+            if kw_overlap:
+                bonus += 0.05 * min(kw_overlap, 4)
+
+            # Boost: title word overlap with query (uses full, untruncated title)
+            title_overlap = len(full_title_tokens & query_words)
+            if title_overlap:
+                bonus += 0.05 * min(title_overlap, 5)
+
+            # Strong boost: majority of title words present in query — likely
+            # the most on-point standard even if its chunk body is polluted.
+            stop = {"and", "or", "for", "the", "of", "in", "a", "an", "to"}
+            sig_title = full_title_tokens - stop
+            sig_query = query_words - stop
+            if sig_title and len(sig_title & sig_query) / len(sig_title) >= 0.6:
+                bonus += 0.25
+
+            # Boost: exact IS ID in query (user specifies a standard directly)
+            if re.search(r'\bIS\s*\d+', query, re.IGNORECASE):
+                for m in re.finditer(r'\bIS\s*\d+[\s:()A-Za-z\d]*:\s*\d{4}', query, re.IGNORECASE):
+                    if _norm_std_id(m.group()) == sid_norm:
+                        bonus += 0.20
+                        break
+
+            # Penalize very short chunks
+            body_wc = len(_body_text(chunk.get("text", "")).split())
+            if body_wc < _SHORT_CHUNK_THRESHOLD:
+                bonus -= _SHORT_CHUNK_PENALTY
+
+            chunk_scores[cid] = base + bonus
+
+        # --- Group by standard_id, keep best chunk score ---
+        std_best: dict[str, float] = {}
+        std_chunk_repr: dict[str, dict] = {}
+        for cid, score in chunk_scores.items():
+            chunk = self.idx.chunks[cid]
+            sid = chunk["standard_id"]
+            if sid not in std_best or score > std_best[sid]:
+                std_best[sid] = score
+                std_chunk_repr[sid] = chunk
+
+        # --- Sort and take top N ---
+        ranked = sorted(std_best.items(), key=lambda x: x[1], reverse=True)[:top_n]
+
+        results = []
+        for sid, score in ranked:
+            std_rec = self.idx.std_lookup.get(_norm_std_id(sid), {})
+            results.append({
+                "standard_id": sid,
+                "title": std_rec.get("title", std_chunk_repr[sid].get("title", "")),
+                "category": std_rec.get("category", std_chunk_repr[sid].get("category", "")),
+                "score": round(float(score), 4),
+                "matched_section": std_chunk_repr[sid].get("section", ""),
+            })
+
+        latency = time.perf_counter() - t0
+        return results, latency
+
+
+# ---------------------------------------------------------------------------
+# Index load/build helper
+# ---------------------------------------------------------------------------
+def load_or_build(force_rebuild: bool = False) -> tuple[RetrievalIndex, Retriever]:
+    with open(_CHUNKS_PATH, encoding="utf-8") as f:
+        chunks = json.load(f)
+    with open(_STANDARDS_PATH, encoding="utf-8") as f:
+        standards = json.load(f)
+
+    # Attach full title + keywords from standards.json to each chunk.
+    # full_title ensures the BM25 document uses the authoritative (untruncated)
+    # title from the structured record, not whatever ended up in the chunk prefix.
+    std_map = {s["standard_id"]: s for s in standards}
+    for c in chunks:
+        rec = std_map.get(c["standard_id"], {})
+        c["full_title"] = rec.get("title", c.get("title", ""))
+        c["keywords"] = rec.get("keywords", [])
+
+    print(f"Loaded {len(chunks)} chunks, {len(standards)} standards.")
+    model = SentenceTransformer(_MODEL_NAME)
+    index = RetrievalIndex(chunks, standards, model)
+    index.build(use_cache=not force_rebuild)
+    return index, Retriever(index)
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+def _format_result(
+    query_id: str,
+    query: str,
+    results: list[dict],
+    latency: float,
+    expected_standards: list[str] | None = None,
+) -> dict:
+    out: dict[str, Any] = {
+        "id": query_id,
+        "query": query,
+        "retrieved_standards": [r["standard_id"] for r in results],
+        "details": results,
+        "latency_seconds": round(latency, 4),
+    }
+    if expected_standards is not None:
+        out["expected_standards"] = expected_standards
+    return out
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="BIS SP-21 Hybrid Retrieval")
+    parser.add_argument("--build", action="store_true", help="Force rebuild of FAISS index")
+    parser.add_argument("--query", type=str, help="Single query string")
+    parser.add_argument("--input", type=str, help="JSON file with list of {id, query} objects")
+    parser.add_argument("--output", type=str, help="Write JSON results to this file")
+    args = parser.parse_args()
+
+    index, retriever = load_or_build(force_rebuild=args.build)
+
+    if args.query:
+        results, latency = retriever.retrieve(args.query)
+        out = _format_result("Q0", args.query, results, latency)
+        print("\n" + "=" * 60)
+        print(f"Query : {args.query}")
+        print(f"Latency: {latency:.3f}s")
+        print("\nTop results:")
+        for i, r in enumerate(results, 1):
+            print(f"  {i}. {r['standard_id']} — {r['title']}")
+            print(f"     Category: {r['category']}  |  Section: {r['matched_section']}  |  Score: {r['score']}")
+        if args.output:
+            Path(args.output).write_text(json.dumps([out], indent=2, ensure_ascii=False), encoding="utf-8")
+        return
+
+    if args.input:
+        with open(args.input, encoding="utf-8") as f:
+            queries = json.load(f)
+
+        all_results = []
+        latencies = []
+        for q in queries:
+            qid = q.get("id", "?")
+            qtext = q.get("query", "")
+            results, latency = retriever.retrieve(qtext)
+            latencies.append(latency)
+            expected = q.get("expected_standards", [])
+            out = _format_result(qid, qtext, results, latency, expected_standards=expected or None)
+            all_results.append(out)
+            hit = any(r["standard_id"] in expected for r in results)
+            print(f"[{qid}] latency={latency:.3f}s  hit={hit}  retrieved={[r['standard_id'] for r in results]}")
+
+        print(f"\nAvg latency: {sum(latencies)/len(latencies):.3f}s  |  Max: {max(latencies):.3f}s")
+
+        # Simple Hit@5 eval
+        hits = 0
+        for q, out in zip(queries, all_results):
+            expected = set(q.get("expected_standards", []))
+            if expected & set(out["retrieved_standards"]):
+                hits += 1
+        print(f"Hit@5: {hits}/{len(queries)} = {hits/len(queries):.1%}")
+
+        if args.output:
+            Path(args.output).write_text(
+                json.dumps(all_results, indent=2, ensure_ascii=False), encoding="utf-8"
+            )
+            print(f"Results written to {args.output}")
+        return
+
+    # Default: demo with one example query
+    demo_query = (
+        "Which standard specifies chemical and physical requirements "
+        "for 33 grade Ordinary Portland Cement?"
+    )
+    results, latency = retriever.retrieve(demo_query)
+    out = _format_result("DEMO", demo_query, results, latency)
+
+    print("\n" + "=" * 60)
+    print(f"Demo query : {demo_query}")
+    print(f"Latency    : {latency:.3f}s")
+    print("\nTop-5 retrieved standards:")
+    for i, r in enumerate(results, 1):
+        print(f"  {i}. {r['standard_id']} — {r['title']}")
+        print(f"     Category : {r['category']}")
+        print(f"     Section  : {r['matched_section']}")
+        print(f"     Score    : {r['score']}")
+    print("=" * 60)
 
-from inference import main  # noqa: E402
 
 if __name__ == "__main__":
     main()
diff --git a/web/server/bridge/retrieve.py b/web/server/bridge/retrieve.py
index cc1bcef..89716c5 100644
--- a/web/server/bridge/retrieve.py
+++ b/web/server/bridge/retrieve.py
@@ -15,7 +15,7 @@ import json
 import os
 
 ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
-sys.path.insert(0, os.path.join(ROOT, "src"))
+sys.path.insert(0, ROOT)
 os.chdir(ROOT)
 
 import inference  # noqa: E402