diff --git a/.plans/phase1_backend_plan.md b/.plans/phase1_backend_plan.md
index 4bef6ad..0f6b206 100644
--- a/.plans/phase1_backend_plan.md
+++ b/.plans/phase1_backend_plan.md
@@ -3,7 +3,7 @@
 **Source**: `development_plan.md`  
 **Scope**: FastAPI backend for text-based RAG Q&A  
 **Estimated Duration**: 3-4 days  
-**Status**: Draft
+**Status**: In Progress (Phase 1.1 ✅, Phase 1.2 ✅, Phase 1.3 pending)
 
 ---
 
@@ -18,13 +18,13 @@ Build a complete FastAPI backend that:
 
 ## Acceptance Criteria
 
-- [ ] `POST /api/v1/ingest` accepts DOCX and PDF, parses content, chunks at 1000/200, embeds, stores in ChromaDB with filename/upload_date/content_summary
+- [x] `POST /api/v1/ingest` accepts DOCX and PDF, parses content, chunks at 1000/200, embeds, stores in ChromaDB with filename/upload_date/content_summary
 - [ ] `POST /api/v1/query` accepts natural language question, returns JSON with: `keywords`, `answer` (bullet points), `sources` (array of metadata objects)
 - [ ] Query pipeline executes 3 LLM calls: decomposition → relevance filter → response generation
-- [ ] All LLM/ASR configuration reads from `.env` (OpenRouter for dev)
-- [ ] ChromaDB persists to `chroma_db/` directory
-- [ ] Chunking strategy is abstracted (interface/class) for future replacement
-- [ ] All unit tests pass (`pytest app/test/test_phase1_*.py -v`)
+- [x] All LLM/ASR configuration reads from `.env` (OpenRouter for dev)
+- [x] ChromaDB persists to `chroma_db/` directory
+- [x] Chunking strategy is abstracted (interface/class) for future replacement
+- [x] All unit tests pass (`pytest app/test/test_phase1_*.py -v`)
 - [ ] All acceptance tests pass (`pytest app/test/acceptance/ -v -m acceptance`)
 
 ---
@@ -71,6 +71,11 @@ Build a complete FastAPI backend that:
 
 **Commit**: "feat: Phase 1.1 project setup with config, database, and models"
 
+**Status**: ✅ Complete  
+**Tests**: 5 passed (2 config, 3 database)
+
+---
+
 ### Phase 1.2: Ingestion Pipeline
 
 **Test files to write first**:
@@ -110,6 +115,12 @@ Build a complete FastAPI backend that:
 
 **Commit**: "feat: Phase 1.2 ingestion pipeline with chunking and metadata"
 
+**Status**: ✅ Complete  
+**Tests**: 20 passed, 2 skipped (python-docx not installed in test env)
+**Coverage**: chunking (4), metadata (3), parsers (5), RAGService (6), ingest endpoint (4)
+
+---
+
 ### Phase 1.3: Query Pipeline (3-Step)
 
 **Test files to write first**:
@@ -117,39 +128,39 @@ Build a complete FastAPI backend that:
 - `test_phase1_rag_service.py` — Test retrieval and response generation
 - `test_phase1_query.py` — Test full pipeline with mocked LLM calls
 
-**Task 1.3.1**: LLM client
-- `services/llm_client.py`: `LLMClient` class
+**Task 1.3.1**: LLM client — ✅ Done in Phase 1.1
+- `services/llm_client.py`: `LLMClient` class — Implemented
 - Constructor takes config from `Settings`
 - Method: `complete(prompt: str, temperature: float = 0.7) -> str`
 - Use httpx with OpenAI-compatible API format
 - Handle errors gracefully
 
 **Task 1.3.2**: Query decomposition
-- `services/query_decomposer.py`: `QueryDecomposer` class
+- `services/query_decomposer.py`: `QueryDecomposer` class — 🔄 Pending
 - Prompt template: "Given question: '{question}', extract key search keywords as JSON array"
 - Method: `decompose(question: str) -> list[str]`
 - Parse LLM JSON response into list of keywords
 
-**Task 1.3.3**: Retrieval from ChromaDB
-- `services/rag.py`: Add `retrieve(query_keywords: list[str], n_results: int = 10)`
+**Task 1.3.3**: Retrieval from ChromaDB — ✅ Done in Phase 1.2
+- `services/rag.py`: `retrieve(query_keywords: list[str], n_results: int = 10)` — Implemented
 - Join keywords with space for query text
 - Return list of `(chunk_text, metadata, distance)` tuples
 
 **Task 1.3.4**: Relevance filtering
-- `services/relevance_filter.py`: `RelevanceFilter` class
+- `services/relevance_filter.py`: `RelevanceFilter` class — 🔄 Pending
 - Prompt: "Given question '{question}' and these document chunks, rate each 0-10 for relevance. Return JSON array of scores."
 - Input: list of chunks
 - Output: filtered list of (chunk, metadata) with score > threshold (e.g., 7)
 - Batch all chunks in single LLM call
 
-**Task 1.3.5**: Response generation
-- `services/rag.py`: Add `generate_response(question: str, chunks: list, metadata: list) -> str`
+**Task 1.3.5**: Response generation — ✅ Done in Phase 1.2
+- `services/rag.py`: `generate_response(question: str, chunks: list, metadata: list) -> str` — Implemented
 - Prompt: "Answer question using ONLY these document chunks. Format as bullet points. Cite sources."
 - Include chunk content and metadata in context
 - Enforce bullet-point format via prompt
 
 **Task 1.3.6**: Query endpoint
-- `routers/query.py`: `POST /api/v1/query`
+- `routers/query.py`: `POST /api/v1/query` — 🔄 Pending
 - Full pipeline orchestration:
   1. Call `query_decomposer.decompose()` → get keywords
   2. Call `rag.retrieve()` → get chunks
@@ -189,21 +200,22 @@ Build a complete FastAPI backend that:
 
 ---
 
-## New Services Required
+## Services Status
 
-| Service | File | Responsibility |
-|---------|------|----------------|
-| Config | `core/config.py` | `.env` loading, Settings class |
-| Database | `core/database.py` | ChromaDB persistent client |
-| LLM Client | `services/llm_client.py` | OpenAI-compatible API wrapper |
-| Query Decomposer | `services/query_decomposer.py` | Extract keywords from question |
-| Relevance Filter | `services/relevance_filter.py` | Batch score chunk relevance |
-| RAG Service | `services/rag.py` | Embedding, retrieval, response generation |
-| Document Parser | `utils/document_parser.py` | Router to DOCX/PDF parsers |
-| DOCX Parser | `utils/parsers/docx_parser.py` | Extract text from DOCX |
-| PDF Parser | `utils/parsers/pdf_parser.py` | Extract text from PDF |
-| Chunking | `utils/chunking.py` | Token-based chunking with overlap |
-| Metadata | `utils/metadata.py` | Extract file metadata |
+| Service | File | Status | Responsibility |
+|---------|------|--------|----------------|
+| Config | `core/config.py` | ✅ Complete | `.env` loading, Settings class |
+| Database | `core/database.py` | ✅ Complete | ChromaDB persistent client |
+| LLM Client | `services/llm_client.py` | ✅ Complete | OpenAI-compatible API wrapper |
+| Query Decomposer | `services/query_decomposer.py` | 🔄 Pending | Extract keywords from question |
+| Relevance Filter | `services/relevance_filter.py` | 🔄 Pending | Batch score chunk relevance |
+| RAG Service | `services/rag.py` | ✅ Complete | Embedding, retrieval, response generation |
+| Ingest Router | `routers/ingest.py` | ✅ Complete | POST /api/v1/ingest endpoint |
+| Query Router | `routers/query.py` | 🔄 Pending | POST /api/v1/query endpoint |
+| DOCX Parser | `utils/docx_parser.py` | ✅ Complete | Extract text from DOCX |
+| PDF Parser | `utils/pdf_parser.py` | ✅ Complete | Extract text from PDF |
+| Chunking | `utils/chunking.py` | ✅ Complete | Token-based chunking with overlap |
+| Metadata | `utils/metadata.py` | ✅ Complete | Extract file metadata |
 
 ---