Years of tick data, trade records, and client history. Growing every day. A target every day.
RNDA keeps the pattern intelligence and eliminates the raw data. Storage costs crater. The data attackers want stops existing.
Request a Financial Services POC →The Problem
Financial institutions store years of tick data, order books, and market signals — primarily for pattern detection and regulatory compliance. The decompression overhead at query time creates latency. The retention creates liability.
How RNDA Solves It
911x compression on full annual reports
Complete 10-K annual reports (700K+ characters) encoded to a single 256-byte signature. The larger the document, the higher the compression. 10-Qs at 12x, 10-Ks at 911x — the entire document archive in kilobytes.
Query in any language
RNDA multilingual encoding means the same financial concept in English, French, German, Spanish, Mandarin, or Japanese scores 0.999 similarity. EU clients query in French. Asian partners query in Mandarin. Same results.
Market pattern matching — 0.99 discrimination gap
Submit any 30-day return pattern and find the most similar historical market conditions from 5 years of real S&P 500 data. Discrimination gap 0.99 — similar patterns cluster, different periods clearly separate.
Market regime detection
Find historical periods most similar to current conditions. Identify recurring patterns in volatility, drawdown profiles, and sector rotation — without retaining raw market data.
M&A target pattern analysis
Encode a target company's return pattern. Find the most similar historical acquisition targets and see how those deals resolved. Deal intelligence without deal data retention.
Regulatory surveillance without raw data
Find unusual similarity between trading accounts that shouldn't be coordinating. Cross-institutional pattern matching — the pattern is the evidence, not the underlying trades.
How RNDA Applies
Storage Elimination
Complete 10-K annual reports at 911x compression, S&P 500 time-series at 11,153x. A 1,000 TB financial firm reduces its storage bill from $276K/year to under $25 — eliminating cold-storage infrastructure for tick data, order books, and regulatory archives almost entirely.
Privacy Protection
Sensitive customer transaction records and market signal data are encoded into 256-byte signatures at ingest. Raw PII, trading strategies, and proprietary signals cannot be reconstructed — protecting both client data and institutional IP at the storage layer.
Compliance Management
SEC, FINRA, and MiFID II mandate multi-year retention of trade records. RNDA-compressed audit trails remain fully queryable for regulatory review without proportional storage cost growth. Compressed archives pass audit queries without decompressing raw data.
Intelligent Retrieval
Submit any 30-day market pattern and find the most similar historical conditions from 5 years of S&P 500 data in 21ms. Discrimination gap 0.99 — similar periods cluster, different regimes clearly separate. Market regime detection, M&A target analysis, and regulatory surveillance all run directly on signatures.
Collaborative Intelligence
Trading desks and partner institutions share compressed time-series and document signatures across organizational boundaries without exposing raw proprietary signals. Multilingual querying proven — same financial concept in six languages scores 0.999 similarity.
Storage Impact
Industry stat: NYSE alone captures 1 terabyte of trading data per day (~365 TB/year per exchange); a mid-to-large financial institution manages 500–2,000 TB/year (Investopedia / TRG Screen)
1,000 TB × 20% × $276/TB ÷ 11,153x compression
1,000 TB financial firm saves ~$276K/year — 11,153x compression on real S&P 500 OHLCV data
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Real OHLCV — 50 S&P 500 tickers, 5 years
What Becomes Possible
"Current market conditions are encoded and immediately discarded. RNDA queries 5 years of real S&P 500 history and returns the 10 most similar historical 30-day periods in milliseconds — with their outcomes. For M&A: encode a target company's return pattern, find the most similar historical acquisition targets, see how those deals resolved. Zero raw data retained at any point."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request a Financial Services POC →