RAG · Semantic search
Strategy Sync AI
MSc coursework · Information Retrieval
An information retrieval system that measures how well an organisation's day-to-day action plans line up with its long-term strategy, and explains where they don't.
- Role
- AI / IR Engineer
- Year
- 2026
- Focus
- RAG
- Stack
- 5 technologies
Top-K
Semantic retrieval per strategy
3 tiers
Strong · Medium · Weak alignment
Live
Hosted on Hugging Face Spaces
The problem
Strategic plans are abstract, and action plans are operational. They use different words for the same intent, so keyword matching misses real links, and manual alignment reviews are subjective and slow on large documents.
Overview
Strategy Sync uses semantic similarity instead of keyword overlap. Every strategy and action is embedded, each strategy retrieves its closest actions from a vector database, and the system scores how strongly each strategy is supported.
An optional RAG step uses an LLM to write improvement recommendations, and a deterministic fallback keeps runs reproducible. The results appear in a Streamlit dashboard, available as a live Hugging Face Space, with JSON and CSV export.
Architecture
Pipeline
- 01
Ingest
JSON / PDFStrategic and action plans are parsed, and PDFs are converted to structured JSON.
- 02
Preprocess
Titles, descriptions, and KPIs become clean sentences for better embeddings.
- 03
Embed
all-MiniLM-L6-v2Sentence-Transformers turns each item into a semantic vector.
- 04
Retrieve
ChromaDBA persistent vector store returns the top-K most similar actions for each strategy.
- 05
Score
Top-3 average similarity per strategy, labelled Strong (≥ 0.75), Medium (≥ 0.55), or Weak, plus overall score and coverage.
- 06
Recommend
RAGLLM-backed or deterministic suggestions for weakly supported strategies.
- 07
Visualise
StreamlitDashboard, strategy–action graph, and downloadable reports.
Key features
- Overall alignment score from 0 to 100, with coverage: strategies backed by at least two strong actions
- Per-strategy breakdown that points out weak or unsupported goals
- Evaluation against ground truth with Precision@K, Recall@K, MAP, and NDCG
- Ontology documentation (TTL) ready to extend into a knowledge graph