# CogniSwitch Reviews
**Vendor:** CogniSwitch  
**Category:** [Emerging AI Software Software](https://www.g2.com/fr/categories/emerging-ai-software)
## About CogniSwitch
Enterprise AI has an adoption problem in regulated industries. This is not a model problem. It's a trust problem. Healthcare payers can't deploy AI agents that make coverage decisions without proving those decisions are traceable, consistent, and grounded in approved clinical protocols. Financial institutions can't automate compliance workflows without audit trails that satisfy regulators. The models work. The governance infrastructure doesn't exist. CogniSwitch fills that gap. We provide the compliance infrastructure layer that sits between enterprise data and AI agents, ensuring every decision is deterministic, auditable, and aligned to domain-specific standards. How it works: CogniSwitch ingests unstructured enterprise documents — SOPs, clinical guidelines, regulatory policies, treatment protocols — and transforms them into structured knowledge graphs. Unlike probabilistic vector retrieval (RAG), our neuro-symbolic engine uses domain ontologies (SNOMED-CT, ICD-10, RxNorm for healthcare; FIBO for financial services) to preserve semantic meaning and enforce logical relationships. When an AI agent queries the platform, it receives structured, deterministic data — not probabilistic chunks. The same query always returns the same answer, and every answer traces back to its source document. Production proof: CogniSwitch powers clinical quality assurance system is live in a 2,000+ provider telehealth network, monitoring 250,000–350,000 patient encounters annually. Results: 95%+ audit coverage (up from 3–5%), 99% accuracy alignment with clinical expert evaluation, $1M annual cost savings, and deployment in 60 days. Model-agnostic -works with any LLM.






- [View CogniSwitch pricing details and edition comparison](https://www.g2.com/fr/products/cogniswitch/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-21+21%3A12%3A29+-0500&secure%5Bsession_id%5D=d3fbdcf7-20cf-42c3-9d45-60a483fa65dd&secure%5Btoken%5D=1f4ef014f7679733718826b9a1e286646ec5d4e1ba440dff5c127822c524514d&format=llm_user)


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