Vector-Semantic AI · Toronto, Canada

Intelligence embedded. Retrieved with precision.

IntelliVector architects production-grade RAG systems, semantic search engines, and vector infrastructure for Canadian enterprises that demand sub-100ms retrieval at scale.

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High-dimensional vector embedding space visualization

1M+Vectors indexed

<100msRetrieval latency

12Industries served

Since 2019Canadian operations

RAG Solutions

Production-ready retrieval packages

End-to-end vector-semantic systems with transparent CAD pricing, Canadian data residency options, and enterprise SLA support.

Enterprise RAG Pipeline

$85,000 CAD

Full ingestion, chunking, embedding, and retrieval stack with guardrails, evaluation harness, and production deployment on Canadian cloud regions.

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Semantic Search Engine

$42,000 CAD

Hybrid dense-sparse retrieval with relevance tuning, faceted filters, and analytics dashboard for knowledge bases up to 500K documents.

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Document Intelligence Suite

$56,000 CAD

Intelligent parsing, layout-aware chunking, metadata extraction, and vector indexing for contracts, policies, and technical documentation.

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Vector Index Pro

$28,500 CAD

Managed vector database setup with HNSW tuning, shard strategy, backup policies, and monitoring integrated into your existing stack.

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OpenAI Embeddings pgvector Pinecone Weaviate Qdrant Milvus LangChain LlamaIndex Azure AI Search AWS Bedrock

Why IntelliVector

Precision at every node in the retrieval graph

We combine deep vector engineering with Canadian regulatory awareness to deliver systems that perform under real enterprise load.

Sub-100ms Retrieval

HNSW index tuning, caching layers, and query planning optimised for your corpus size and concurrency profile.

PIPEDA-Aligned Design

Data residency in Canadian regions, consent-aware ingestion pipelines, and audit trails built into every vector store.

Evaluation-First RAG

Golden-set benchmarking, hallucination detection, and continuous relevance scoring before and after deployment.

Hybrid Search Mastery

Dense embeddings combined with BM25 sparse retrieval and cross-encoder reranking for maximum recall and precision.

Multi-Modal Ready

Text, image, and structured data embeddings unified in a single semantic index with namespace isolation.

Canadian Support

Toronto-based engineering team with on-site workshops available across major Canadian tech hubs.

Abstract node mesh representing vector connections

Transform unstructured knowledge into queryable intelligence

From pilot to production, IntelliVector embeds your enterprise knowledge into high-dimensional space — then retrieves answers with surgical precision.

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Core Services

Vector infrastructure for every stage

Vector Database Setup

Architecture, provisioning, and performance tuning for pgvector, Pinecone, Weaviate, and Qdrant deployments.

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Embedding Optimisation

Model selection, fine-tuning guidance, dimensionality analysis, and drift monitoring for sustained retrieval quality.

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Copilot Integration

Microsoft 365 Copilot, custom GPT, and internal assistant layers connected to your authoritative vector index.

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Client Feedback

Retrieval results that speak for themselves

> query_latency_p95 = "87ms";
> recall_at_10 = 0.94;
> hallucination_rate = "<2%";
// IntelliVector rebuilt our legal search from keyword matching to semantic precision.

— General Counsel, national financial services firm, Toronto

> vectors_indexed = 1_240_000;
> ingestion_pipeline = "automated";
> pipeda_compliant = true;
// Their RAG pipeline handles our policy corpus with Canadian data residency.

— VP Engineering, healthcare technology, Vancouver

> copilot_connected = true;
> source_attribution = "enabled";
> user_satisfaction = "91%";
// Copilot integration finally surfaces answers from our internal knowledge base.

— Director of IT, professional services, Montreal

Ready to index your enterprise knowledge?

Book a consultation with our Toronto vector engineering team. We will assess your corpus, recommend an architecture, and provide a scoped proposal in CAD.

Build your vector index