Build your vector index

Professional Services

Specialist vector engineering services delivered by our Toronto team — from initial architecture through production deployment and ongoing optimisation.

Service 01

Vector Database Setup

We design, provision, and tune vector databases tailored to your corpus size, query patterns, and compliance requirements. Whether you choose pgvector, Pinecone, Weaviate, Qdrant, or Milvus, we handle index configuration, sharding, replication, and disaster recovery.

  • HNSW and IVF index parameter optimisation
  • Canadian cloud region deployment (AWS ca-central-1, Azure Canada Central)
  • Backup, monitoring, and alerting integration
  • Migration from legacy search or existing vector stores
  • Performance benchmarking with your actual query load
Discuss your database needs
Vector database schema and index architecture
Service 02

RAG Implementation

End-to-end retrieval-augmented generation pipelines that connect your enterprise knowledge to large language models with accuracy, attribution, and guardrails. We build production systems — not proof-of-concept demos.

  • Ingestion pipelines for PDF, HTML, SharePoint, and API sources
  • Chunking strategy design with overlap and metadata preservation
  • Cross-encoder reranking and context window optimisation
  • Hallucination detection and citation enforcement
  • Evaluation harness with golden-set benchmarking
Start your RAG project
RAG implementation architecture diagram
Service 03

Semantic Search

Replace keyword-only search with meaning-aware retrieval that understands intent, synonyms, and conceptual relationships. Our hybrid search implementations combine dense vector similarity with BM25 sparse retrieval for optimal recall and precision.

  • Query understanding and expansion
  • Faceted navigation and filter integration
  • Relevance tuning dashboard for non-technical administrators
  • Search analytics and zero-result query analysis
  • API and frontend integration support
Upgrade your search
Semantic search user interface
Service 04

Document Intelligence

Transform unstructured documents into structured, searchable vector representations. We handle complex layouts, tables, headers, footnotes, and multi-column formats that break naive text extraction approaches.

  • Layout-aware parsing with OCR fallback
  • Intelligent chunking respecting document structure
  • Metadata extraction (author, date, section, classification)
  • Table and figure handling with contextual embedding
  • Batch processing pipelines with error recovery
Process your documents
Document intelligence chunking workflow
Service 05

Embedding Optimisation

The quality of your retrieval system depends on embedding model selection, dimensionality, and ongoing drift monitoring. We analyse your corpus and query patterns to recommend and implement the optimal embedding strategy.

  • Model comparison benchmarking on your data
  • Domain-specific fine-tuning guidance
  • Dimensionality reduction analysis for cost-performance trade-offs
  • Embedding drift detection and re-indexing schedules
  • Multi-lingual embedding support for Canadian bilingual requirements
Optimise your embeddings
Embedding quality metrics dashboard

Not sure which service fits your project?

Our Toronto team offers complimentary 45-minute vector assessments to map your requirements to the right service combination.

Book a vector assessment