Pathway E1
Embedding Strategy
Model comparison, dimensionality trade-offs and chunking policy design for your document corpus and multilingual requirements.
View embedding sprint →
Independent vector advisory · Vancouver, BC
Ground your knowledge systems in measured retrieval — not demo hype.
Embedding strategy, RAG pipeline architecture and semantic search programmes for Canadian enterprises — documented assumptions, benchmarked on your corpus.
120+RAG reviews
34Programmes
C$1.8MAvg. scope modelled
BN on file · 675 West Hastings Street · Pacific Time
“Knowledge systems deserve the same rigour as production ML pipelines — measured embedding quality, documented retrieval assumptions and a clear path from raw documents to grounded answers.” CogniVector · Vector Charter
Capability lattice
Four pathways through vector intelligence decisions
Each pathway maps to a distinct stage of your knowledge-system roadmap — from first embedding evaluation through enterprise RAG deployment.
Pathway R2
RAG Pipeline Design
Retrieval architecture, reranking layers, grounding policies and evaluation harnesses for production-grade answer generation.
Explore RAG blueprint →
Pathway S3
Semantic Search
Hybrid retrieval blueprints combining dense vectors, sparse lexical signals and metadata filters.
Launch plan →
Pathway K4
Knowledge Graph
Entity linking, graph-augmented retrieval and structured context for regulated knowledge domains.
Graph integration →
Vendor-neutral from day one
We accept no referral fees from vector database or embedding API vendors. Recommendations follow your benchmark results — not our margin on a licence sale.
Advisory method
Five phases from corpus audit to grounded retrieval
Each phase produces documented deliverables your engineering team can implement or hand to integration partners.
01
Corpus audit
Document taxonomy, access controls and update cadence
02
Embedding bench
Model shortlist with recall@k on held-out queries
03
Index architecture
Vector store selection, sharding and latency targets
04
RAG integration
Prompt templates, grounding rules and guardrails
05
Operations handoff
Monitoring dashboards, reindex playbooks and drift alerts
Who we serve
Vancouver advisory for teams navigating embedding procurement, RAG launch and semantic search migration.
CogniVector helps product teams, data leaders and enterprise architects make defensible vector intelligence decisions. We translate vendor documentation into comparable metrics: chunking strategy, index latency, recall benchmarks and realistic infrastructure expenditure timelines.
From our studio at 675 West Hastings Street, we model retrieval performance using your document corpus, embedding dimension targets and query patterns. Whether you are evaluating a managed vector store or planning an on-premise semantic search deployment, we provide structured comparison frameworks grounded in Canadian privacy expectations and PIPEDA-aligned data handling.
Every engagement begins with a corpus audit — quantifying document types, metadata richness and access-control boundaries before recommending an embedding model family or chunking policy.
Track record
Measured advisory across Canadian enterprises
120+
RAG architectures reviewed
34
Vector programmes delivered
C$1.8M
Avg. infrastructure spend modelled
Programmes
Structured engagements with documented outputs
Embedding benchmarks, RAG architecture diagrams or semantic search rollout plans — scoped to your corpus size and compliance requirements.
Programme P1
View programme
Embedding Strategy Sprint
Two-week model comparison with chunking experiments and recall benchmarks on your sample corpus.
C$6,500–C$12,000
Programme P2
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RAG Pipeline Blueprint
Full retrieval architecture with reranking design, evaluation harness and grounding policy documentation.
C$14,000–C$28,000
Programme P3
View programme
Semantic Search Launch
Hybrid search rollout plan with index sizing, query analytics framework and migration timeline.
C$9,800–C$19,500
Studio sessions
Vector intelligence in practice
Common questions
Vector intelligence — quick answers
Is CogniVector a generic AI chatbot vendor, consumer app marketplace or data brokerage?
No. CogniVector is an enterprise vector intelligence advisory studio — we design embedding strategies, RAG pipelines and semantic search architectures from our Vancouver studio. We do not sell consumer chatbot subscriptions, operate an app marketplace or broker personal data.
What embedding models do you evaluate?
We benchmark open-weight and commercial embedding families against your corpus — measuring recall@k, latency and total cost of ownership. Recommendations depend on language mix, document length distribution and deployment constraints.
How do you measure RAG quality?
We establish held-out query sets with human-reviewed ground truth, then track retrieval precision, answer faithfulness and hallucination rates. Metrics are documented in an evaluation harness your team can rerun after model or index updates.
Where is your studio located?
Our vector lab sits at 675 West Hastings Street, Suite 1400, Vancouver, BC — in the downtown financial district. We welcome scheduled visits for architecture reviews and embedding strategy workshops by appointment.
Start here
Ready to benchmark your embedding strategy?
Book a vector audit or walk through our RAG readiness framework with your document corpus and infrastructure budget.
Book a vector audit
Our content covers enterprise vector intelligence advisory for informational purposes. Performance benchmarks and infrastructure estimates reflect illustrative assumptions — individual outcomes vary with corpus quality, query patterns and operating decisions.