Industry: Market Intelligence / Talent & Company Sourcing
Built for investor and recruiting teams to surface high-potential companies and professionals from multi-million record datasets without manual filtering.
You can view the product here: https://siftscore.ai/
You can view the product here: https://siftscore.ai/
Problem
The manual sorting of massive datasets and reliance on traditional keyword filters created significant bottlenecks in strategic sourcing and market mapping.
- Information Scale: Processing and ranking tens of millions of talent and company records was manually impossible.
- Filter Limitations: Standard database filters often missed relevant targets that didn't match exact keywords or categories.
- Inconsistent Discovery: Traditional research across disparate sources led to slow and fragmented market maps.
- Sourcing Bottlenecks: Strategic teams spent excessive time on manual validation rather than high-level analysis.
Solution
The platform utilizes a vector-based data architecture and LLM scoring logic to enable natural language discovery across all talent and company pools.
- Semantic Data Ingestion: Developed a backend to ingest and enrich large datasets with vector representations (embeddings).
- Similarity Search Engine: Implemented high-speed vector search to identify targets based on semantic meaning rather than just keywords.
- LLM-Assisted Scoring: Built logic that uses LLMs to grade and rank profiles and companies based on specific user intent.
- Market Mapping Tools: Created automated systems for generating real-time Talent Pools and Market Maps through natural language queries.
Tech Stack
- Backend Framework: FastAPI.
- Database: PostgreSQL with pgvector for efficient similarity search.
- Infrastructure: Azure Cloud.
- AI Logic: Embeddings and LLM-powered grading orchestration.
Results
- Scalable Intelligence: Enabled real-time querying and relevance scoring across a multi-million record dataset.
- Precision Sourcing: Provided investor teams with ranked outputs that identify highly relevant targets missed by traditional methods.
- Product Innovation: Powered the launch of a next-generation SaaS product for automated talent and company discovery.
- Performance Optimization: Strengthened data architecture to support both internal strategic tools and high-traffic external usage.