Graph-Based Knowledge
Representation
Encoding relationships and dependencies as graphs so AI systems can reason over connected knowledge.
NLP & Vector Search
Make Your Unstructured Data Searchable
The Challenges We Solve
Valuable enterprise knowledge often exists across documents, transcripts, forms, reports, and other unstructured content that traditional data platforms and keyword search cannot fully utilize. Organizations need better ways to understand context, relationships, and meaning across this information so it can support enterprise search, knowledge retrieval, analytics, and AI applications.
Our Approach
InfoPeople starts by understanding the organization’s unstructured content, business context, and retrieval requirements. We then design the appropriate NLP, semantic search, knowledge retrieval, and RAG architecture to make enterprise information discoverable and usable by people, applications, and AI systems, with security, governance, and access controls incorporated into the solution.
Our NLP & Vector Search Capabilities
Graph-Based Knowledge
NLP &
Content Intelligence
Entity extraction, classification, and ontology development to structure unstructured content for AI use.
Embedding Model
Selection and
Fine-Tuning
Choosing and adapting embedding models so semantic similarity matches your domain, not a generic model’s assumptions.
Vector Database
Design and
Implementation
Architecture for Pinecone, Weaviate, pgvector, and other platforms, optimized for scale, latency, and consistency.
Semantic Search
Architecture
Query understanding and retrieval-ranked systems that find content by meaning, not keywords, with explainable results.
Insights and Resources
Exotic insurance platform transforms underwriting with AI-native valuation and risk intelligence
A unified AI-native valuation infrastructure enabled an exotic insurance provider to reduce underwriting latency, improve pricing precision, and strengthen capital resilience across exotic risk categories.
Global digital publisher strengthens revenue predictability with AI-powered RFP and forecasting engine
A unified AI-backed revenue intelligence platform helped a global publisher reduce forecast variance, improve RFP win rates, and align inventory allocation with probabilistic demand modeling.
FAQs
RAG is an architecture that retrieves relevant information from an organization’s own data, typically via vector search, and provides it to a large language model as context before generating a response, allowing the model to answer based on specific enterprise knowledge rather than only its training data.
We design and implement vector database architecture using Pinecone, Weaviate, pgvector, and other platforms, selecting the option that fits a client’s existing infrastructure and scale requirements.
No. Beyond search, this intelligence layer feeds downstream AI applications and data products directly, including agentic systems that need to retrieve and reason over enterprise content as part of a larger workflow.
Ready to Make Your Unstructured Data Work For You?
Build semantic search and retrieval systems that understand meaning, not just keywords.
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