Overview / Description
NerqonPro is an enterprise AI search platform tool that eliminates hallucinations in RAG (Retrieval-Augmented Generation) applications through a hybrid tri-source search architecture and built-in confidence scoring, built for production development teams. Rather than guessing when results are uncertain, NerqonPro uses a Confidence Engine with Platt-scaled calibration to detect low-confidence answers and trigger deterministic fallbacks — returning an honest "I don't know" instead of a fabricated response.
The platform combines three parallel search methods — FAISS vector search, BM25 keyword matching, and a semantic Knowledge Graph — with adaptive query fusion that the homepage claims delivers 3x better recall than single-method approaches. The Knowledge Graph encodes typed relationships such as prerequisite, supersedes, and contradicts, enabling multi-hop reasoning across connected documents.
NerqonPro is engineered for production reliability: search latency is under 5ms, uptime is backed by a 99.9% SLA, and the system includes performance guardrails such as rate limiting, circuit breakers, and graceful degradation. It supports GPU acceleration and product quantization for billion-vector search workloads. Embedding flexibility covers 384, 768, and 1536+ dimensions with any embedding model.
Governance and compliance features are also built in — document versioning works like git branching with rollback and full audit trails, and multi-tenancy is enforced through namespace isolation with SSO, SAML, LDAP, and OIDC support. The platform is SOC 2 Type II certified and uses AES-256 encryption. Developers integrate through streaming APIs (SSE), an MCP server, LangChain and LlamaIndex connectors, and Kubernetes Helm charts. A 30-day free trial requires no credit card.
Used For
Enterprise development teams building production RAG applications who need hallucination elimination through hybrid tri-source search and confidence-scored deterministic fallbacks.
Pricing
Pros & Cons
Pros
- Confidence Engine uses Platt-scaled calibration to detect low-confidence results and return a deterministic fallback instead of hallucinating — eliminating a core RAG failure mode
- Tri-source hybrid search (FAISS vector, BM25 keyword, Knowledge Graph) with adaptive query fusion claims 3x better recall than single-method search
- Sub-5ms search latency with a 99.9% uptime SLA, circuit breakers, and graceful degradation for production workloads
- Document versioning with git-like branching, rollback, diff, and full audit trails for compliance and governance
- Multi-tenancy with namespace isolation and SSO/SAML/LDAP/OIDC support, plus SOC 2 Type II certification and AES-256 encryption
Cons
- Exact pricing for Standard and Enterprise tiers is not publicly listed — requires contacting sales or signing up to see rates
- Primarily built for enterprise RAG use cases; likely over-engineered and costly for simple or small-scale search needs
- GPU acceleration and billion-vector search features assume significant infrastructure; smaller teams may not benefit from these capabilities
- No self-hosted or open-source version evident from the homepage — customers depend on NidhiTek's hosted platform
Questions & Answers
Alternatives
Elasticsearch, Weaviate, Pinecone, Azure AI Search, Vertex AI Search