CLAIMS & BENCHMARKS GOVERNANCE STANDARD
Public Methodology & Proof Framework
In enterprise data infrastructure, quantified performance numbers are only as good as the methodology behind them. This document provides the public testing protocols, sampling windows, hardware environments, and replication criteria for all metrics published across Fraoula's properties.
Claims Taxonomy Standard (FRA-005)
Every quantitative claim on Fraoula is categorized into one of three strict tiers: [Internal Benchmark] (reproducible lab tests on controlled hardware), [Client Outcome] (aggregated telemetry from enterprise customer environments), or [Contractual SLA] (enforceable legal commitments backed by our MSA and DPA).
[Internal Benchmark]
Real-Time In-Memory Payload Interception
Evaluates the end-to-end processing latency of the Fraoula Data Auditor engine: from initial TLS 1.3 packet arrival, through SIMD-accelerated JSON deserialization and schema invariant assertion, to cryptographic hash generation and volatile memory eviction.
Benchmark Type
Synthetic Stream Validation
Throughput Load
100,000 events / sec
Test Hardware
AWS c6i.2xlarge (8 vCPU, 16 GB)
Latency Metric
p99: 140ms (p50: 45ms)
Test Protocol: Synthetic Apache Kafka message batches containing 1,024-byte complex JSON payloads (nested arrays, ISO 8601 timestamps, monetary floats, and UUID identifiers) were streamed into Fraoula Data Auditor worker processes (v2026.03). Over 10 million test messages on AWS c6i.2xlarge (8 vCPU, 16 GB RAM) hardware at 100,000 events/sec, end-to-end stream validation latency measured p50: 45ms, p95: 110ms, and p99: 140ms (p99 < 150ms). Individual in-memory rule evaluations executed in <500 μs per invariant assertion. Zero unencrypted payload content was written to storage volumes during or after assertion execution.
[Client Outcome]
Accelerated Compliance Audit Readiness & Evidence Assembly
Measures the operational efficiency gains in assembling historical schema evolution logs, pipeline integrity proofs, and data contract lineage reports during enterprise security reviews by automating continuous in-memory audit logging.
Sample Population
Production Deployments
Evaluation Window
Q3 2025 – Q1 2026 (9 Months)
Industry Verticals
Fintech Payments & Healthtech EHR
Baseline Hours
180 hrs → 32 hrs avg / cycle
Methodology: Baseline hours were derived from historical self-reported audit logs of data engineering teams manually querying warehouse change data capture (CDC) tables and reconstructing broken data migrations. In customer deployments running Fraoula Data Auditor, automated tamper-proof SHA-256 invariant logs provided instant verification, eliminating manual reconstruction work.
[Client Outcome]
High-Impact Pipeline Incident Cost Averted
Quantifies the direct engineering salvage, incident response, and downstream remediation expenses eliminated by intercepting schema corruption in volatile RAM before warehouse insertion.
Economic Model
Gartner & IDC Pipeline Downtime
Team Size Baseline
Core Engineering Teams
Mutation Frequency
3.4 Silent Failures / Year Avg
Remediation Time
72 hrs avg Backfill & Restoral
Cost Calculation Formula: Incident Cost = (Blended Senior Hourly Rate × 12 Engineers × 72 Hours) + Downstream Cloud Compute Re-execution ($48,000) + Regulatory Remediation Penalty Risk ($180,000) = avoidable costs+. Fraoula halts silent schema mutations at the edge stream before database tables are contaminated, reducing incident restoral cycles from days to minutes.
[Internal Benchmark]
Deterministic First-Party Conversion Attribution Matching
Evaluates first-party conversion matching fidelity of the Fraoula Data Intelligence Platform using server-side telemetry and client-side SHA-256 cryptographic digests, maintaining signal durability without reliance on third-party tracking cookies.
Benchmark Mechanism
Dual-Ingest Telemetry Harness
Event Volume
500,000 Webhook Transactions
Tracking Model
Server-to-Server SHA-256 Hashes
Ground Truth Delta
91.4% Match to Stripe Invoices
Protocol: Tested against dual production ground-truth billing event records. Browser-side cookie scripts captured 68% of confirmed purchases due to aggressive ad-blocking and cross-device session fragmentation. Fraoula's server-to-server cryptographic hashing matched 91.4% of all verified purchase receipts directly back to the original acquisition funnel.
[Client Outcome]
14% Ad Spend Reclaimed via Clean Conversion Signal
Calculates capital efficiency gains realized by ad bidding algorithms when fed clean, deterministic server-side attribution signals instead of degraded cookie events.
Portfolio Spend
$2.4M Aggregated Ad Capital
Observation Window
90 Days Post-Deployment
Platforms Audited
Google Ads & Meta CAPI
Reclaimed ROAS
+$336,000 Effective Budget
Analysis: By restoring lost conversion telemetry via server-side cryptographic webhooks, algorithmic bidding engines exited degraded learning phases and eliminated capital allocation to underperforming ad channels.
[Contractual SLA]
Zero Persistent Payload Storage On-Disk
Fraoula contractually commits under its Enterprise MSA and BAA that raw enterprise customer payloads, PHI records, and financial transaction bodies are never written to persistent disk storage, object stores, or long-term backup archives.
Legal Framework
Enterprise MSA & HIPAA BAA
Runtime Memory
Volatile RAM Execution Only
Eviction Time
Immediate upon Invariant Output
Retention Audit
Continuous Kernel Syscall Audit
Verification & Governance: Enforced through Linux kernel seccomp sandboxing and eBPF tracing that strictly disallows filesystem write calls to storage devices during the payload inspection lifecycle. Read full details in our Security & Compliance Trust Center and Master Terms of Service.
[Benchmark Standard]
Performance & Latency Benchmark Glossary
To prevent ambiguity across marketing and technical documentation, Fraoula enforces strict benchmark consistency. A latency claim is never published without explicitly declaring the measured operation, statistical percentile, payload or corpus size, hardware environment, and software release version.
| Measured Operation |
Target Metric & Percentile |
Payload / Corpus Size |
Hardware Environment |
Version |
| Stream Schema Invariant Validation (Data Auditor) |
p99 < 150ms
(p50: 45ms, p99: 140ms) |
2 KB – 64 KB JSON/Avro stream payloads; 100K msg/sec |
AWS c6i.2xlarge (8 vCPU, 16 GB RAM, zero-disk RAM buffer) |
v2026.03 |
| Hybrid Semantic & Lexical Retrieval (Fraoula AI) |
p95 < 800ms
(p50: 420ms, p95: 780ms) |
10,000,000+ vectorized chunks (1536-dim embeddings + BM25) |
Distributed Qdrant / AWS r6i.xlarge cluster |
v2026.02 |
| In-Memory Cache & Similarity Index Lookup (Demo & Portal) |
p95 < 15ms
(Typical: 11.2ms – 16.5ms) |
Pre-computed vector similarity cache (real-time CPU memory lookup) |
In-memory hashmap / Redis edge cache |
v2026.04 |
| End-to-End Generative Synthesis & Citation Assembly |
p95: 3.2s – 4.8s
(TTFT [Time-to-First-Token]: < 650ms) |
500 generated output tokens + multi-document citation verification |
Dedicated Sovereign VPC LLM cluster |
v2026.03 |
Architectural Rule: Demo consoles and interactive widgets simulate Operation C: In-Memory Cache Lookup (11.2ms) for real-time responsiveness. Full RAG pipelines follow Operation B (<800ms retrieval) and Operation D (3.2s generation).
Benchmark Replication & Enterprise Proof of Concept
Interested in replicating these performance benchmarks against your own Kafka, AWS Kinesis, or database CDC streams? Schedule a technical architecture session with our engineering team.
