FAQ
Frequently asked questions
Clear answers about our vector intelligence advisory, embedding benchmarks and what we do not provide.
Our FAQ covers the questions product leaders, data architects and engineering managers ask most often when evaluating embedding strategy, RAG pipeline design or semantic search migration.
If your question is not answered here, contact our Hastings Street studio — we respond within one business day.
Is CogniVector a web agency, training provider or IT outsourcing without AI?
No. CogniVector is an AI vector intelligence agency — we architect embedding models, RAG pipelines and semantic search systems from our studio at 675 West Hastings Street, Vancouver. We do not provide web design, online courses or general IT outsourcing.
What embedding models do you evaluate?
We benchmark open-weight and commercial embedding families against your document sample — measuring recall@k, indexing latency, dimensionality costs and multilingual performance where applicable. Model shortlists depend on corpus characteristics, deployment constraints and total cost of ownership.
How do you measure RAG quality?
We establish held-out query sets with human-reviewed ground truth, then track retrieval precision, answer faithfulness, citation accuracy and hallucination rates. Metrics are packaged in an evaluation harness your engineering team can rerun after embedding model updates or index refreshes.
What does a typical engagement timeline look like?
Vector audits complete within one week. Embedding strategy sprints run two weeks. Full RAG pipeline blueprints typically span four to six weeks. Enterprise accelerator programmes extend twelve weeks with weekly checkpoints.
Do you implement systems or only advise?
CogniVector is an advisory studio. We produce architecture documents, benchmark reports, evaluation harnesses and vendor evaluation support — but software implementation remains with your engineering team or integration partners.
What do you NOT guarantee?
We do not guarantee specific model accuracy figures, regulatory approvals, vendor lead times, infrastructure uptime or business outcomes from deployed retrieval systems. Production results depend on corpus quality, engineering execution and ongoing operations.
How do you handle personal information (PIPEDA)?
CogniVector Inc. collects contact details and project information solely to deliver advisory services and respond to enquiries. Data is stored on Canadian infrastructure and never sold to third parties. See our Privacy Policy for full details.
Can you work with our existing vector database?
Yes. We advise across managed vector services, self-hosted engines and hybrid search platforms. Engagements often begin with an audit of your current index configuration before recommending changes. We remain vendor-neutral and do not accept referral fees.
FAQ content covers enterprise vector intelligence advisory for informational purposes. Benchmarks and infrastructure estimates reflect illustrative assumptions — individual results vary with corpus quality, query patterns and operating decisions.
Still have questions?
Our Vancouver team is ready to discuss your embedding strategy and RAG architecture timeline.
Contact us