AI Apps

QuackStack AI Apps

Solutions

Natural-language answers and scheduled narratives over your trusted institutional datasets.

Details ▼

Problem: Leaders wait on static reports while teams re-export the same cuts of data every week.
Solution: Governed semantic layer plus LLM-generated summaries with citations back to source rows.
Outcome: Faster decisions with fewer ad hoc requests and a clear audit trail.
Tech stack: Vector store + RAG, role-based filters, SQL/semantic APIs, export to PDF/Power BI

Semantic discovery across library catalogues, reading lists, and full-text where licensed.

Details ▼

Problem: Keyword search misses concepts students describe in their own words across editions.
Solution: Embeddings over metadata and permitted extracts, with safe query expansion and filters.
Outcome: Students find relevant texts faster without overwhelming librarians.
Tech stack: OpenSearch / Azure AI Search, OAI-PMH, MARC21, entitlement-aware retrieval

Find tracks and scores by mood, instrument, era, or rights profile for teaching use.

Details ▼

Problem: Audio catalogues are hard to browse when rights and curriculum tags are inconsistent.
Solution: Multimodal tagging pipeline plus conversational refinement with licence checks.
Outcome: Staff build playlists and assessments without manual trawling of repositories.
Tech stack: Audio embeddings, rights registry integration, streaming APIs where available

Match employers, partners, and placement hosts to programmes with explainable fit scores.

Details ▼

Problem: CRM and placement spreadsheets disagree on company identity and contact history.
Solution: Entity resolution across sources with conversational query and saved shortlists.
Outcome: Work-placement teams coordinate outreach with a single verified company graph.
Tech stack: Graph store, dedupe models, CRM connectors, webhook notifications