Presential
Nakato is a privacy layer that sits between a company and any AI model, converting sensitive data into contextually faithful "semantic twins" that preserve task-relevant meaning while removing identity. The platform runs inside the customer's own VPC or on-premises environment, so real data never leaves the premises, and every substitution is reversible and auditable. It is benchmarked across 500,000 financial-services test cases, holding 99.3% of baseline accuracy on DROP numerical reasoning.
- Artificial Intelligence
- Cybersecurity
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Organizations with regulated data face a fundamental blocker when adopting AI: sharing sensitive records with models risks exposing personally identifiable information, while redacting or masking that data strips away the context and signals models need to reason accurately. Traditional approaches like placeholder masking or tokenization preserve privacy but destroy downstream utility, and pattern-matching detection misses indirectly identifying information.
Solution
Nakato sits as a layer between a company and any AI model, converting sensitive details into semantic twins — fictional but contextually faithful values that preserve the characteristics essential to the task. The system reads entire documents with an LLM-based detection approach to find direct and indirect identifiers, generates twins at query time, and reverses every substitution when the answer returns. Nakato operates inside the customer's own VPC or on-premises environment, so real data never leaves the environment; only twins are ever exposed. The platform is designed to preserve meaning across tasks including credit analysis, numerical reasoning, multi-hop reasoning, and summarization, enabling models to reason on realistic records while maintaining full auditability of every swap.
Target Audience
Primary customers are regulated industries, particularly financial services, where credit decisions and other tasks require full-context reasoning on sensitive records, plus data providers who need to sell twinned datasets that protect data subjects, and AI programme leads who need security sign-off and compliance assurance for model deployments.
Features
- Semantic twin generation that preserves task-relevant attributes: age bands, valid financial formats (sort codes), balance within 2%, gender, and name origin while fictionalizing identity
- Context-aware detection across 50+ entity types, catching indirectly identifying information that pattern matching misses, with a 95% confidence gate that fails closed
- Reversible pipeline where every substitution is logged and auditable, allowing answers to be traced back through the swaps that produced them
- Runs entirely within the customer's VPC or on-premises infrastructure, ensuring sensitive data never leaves the environment
- Benchmarked across 500,000 financial-services test cases with downstream utility scoring; holds 99.3% of baseline accuracy on DROP numerical reasoning and 96% on HotpotQA multi-hop reasoning
- Active extraction attack leak rate lower than open-source alternatives: 7.8% leak rate for GLiNER and 22% for Presidio under the same attack