Coherany
Coherany provides a pattern-matching data pipeline that clusters unlabeled data into natural patterns, lets subject-matter experts approve the relevant ones, and then applies those approved patterns to new records in milliseconds. By replacing LLM generation with human-approved matches, the PAR pipeline eliminates hallucinations, offers deterministic results, and delivers a full audit trail for regulated sectors like healthcare, finance, and telecom.
- Enterprise Software
- Artificial Intelligence
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Organizations in regulated industries face a choice between slow, manual data matching performed by subject-matter experts and fast but unreliable LLM-based automation that risks hallucinations, non-deterministic outputs, and missing audit trails. This forces companies to either bottleneck their experts on repetitive work or accept AI decisions they cannot fully explain or defend to auditors and regulators.
Solution
Coherany provides a Pattern-Approved Retrieval (PAR) pipeline that clusters unlabeled data into natural patterns, lets domain experts review and approve the patterns that match their actual workflows, and then applies those approved patterns to new records in milliseconds. The platform uses an LLM coach to help experts define each pattern, while a deterministic engine executes every decision in production at up to 400,000 decisions per second. Because the system matches against human-approved patterns rather than generating new text, it produces identical outputs for identical inputs with zero hallucination risk. Every decision is traceable to a named approver, providing a complete audit trail that meets the demands of regulated sectors. The pipeline shares 60% of its technical stack with RAG—the same embeddings, indexes, and retrieval—but replaces the LLM generation step with deterministic pattern matching.
Target Audience
Primary customers are regulated enterprises in healthcare, insurance, telecom, banking, and financial services that need high-volume, auditable data matching decisions. The platform is designed for organizations where subject-matter experts currently perform repetitive matching work manually and where compliance requirements demand explainable AI.
Features
- Auto-discovery clustering that identifies natural patterns in unlabeled data without requiring manual labeling
- Deterministic matching engine that processes up to 400,000 decisions per second with identical output for identical input
- LLM coach that guides subject-matter experts through defining and approving each pattern
- Complete audit trail on every match, traceable to a named approver, with full revision history and approval workflows
- Runs in the customer's own cloud environment with no vendor lock-in; SOC 2 in progress with HIPAA and NAIC compliant architecture
- Cost-efficient at scale: approximately $6K/month at 10 million records versus $62K/month for comparable LLM-based processing