First-Party Empirical Data

Original Research & Engineering Benchmarks

We publish reproducible, first-party data and insights computed directly from our 25 production codebases and continuous integration test suites. Zero fabricated claims, zero sponsored bias.

Our First-Party Research Integrity Code

Zero Fabrication

We never generate fake surveys, artificial customer reviews, or synthetic pricing trends. Every metric is computed from real code and infrastructure.

Transparent Methodology

Every published report discloses its sample size, input manifests, testbed hardware, time period, mathematical formulas, and operational limitations.

Privacy by Design

All platform analytics are aggregated and anonymized. No customer-level PII, private keys, or confidential client records are ever exposed.

Sample Size Gates

Research studies with insufficient statistical samples are strictly gated as unindexed drafts to prevent premature or misleading claims.

Published Empirical Studies

Peer-audited research papers available for citation, review, and raw dataset export.

3 Published Reports
Software Architecture & Engineering PracticesSample: 25 verified production repositoriesOctober 2026

State of Production Software Architecture: Language, Database, and Framework Distribution Across 25 Enterprise Codebases

An empirical examination of programming languages, machine learning frameworks, and architectural paradigms across 25 production-grade software repositories at Shrayu Technologies.

Audited Repositories
25 Systems
Unique Technologies
52 Libs & Frameworks
Leading Language Ecosystem
Python (48.0%)
Full-Stack Runtime
JavaScript / Node (32.0%)
Source: First-Party Platform Data
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Applied Artificial Intelligence & Machine LearningSample: 4 benchmarked machine learning systemsOctober 2026

Empirical Accuracy Benchmarks in Applied AI: Multi-Horizon Energy Forecasting, Vision Inference, and Anomaly Detection

Empirical benchmark observations recorded across 4 specialized machine learning production models, measuring real-world MAPE error, inference latency, and computational efficiency.

Energy Forecasting MAPE
2.84%
SOC Triage Latency
12.4 ms
Path Planning Run Time
42.0 ms
Radiography Sensitivity
94.2%
Source: First-Party Platform Data
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Engineering Economics & Project Delivery ModelsSample: 9 core engineering service disciplinesOctober 2026

Software Engineering Delivery Economics: Milestone Scopes vs Dedicated Sprint Pods in Full-Stack Product Scaling

An analysis of delivery timelines, intellectual property transfer models, and automated test coverage gates across 9 core software engineering disciplines.

Audited Disciplines
9 Disciplines
Sprint Cadence
14 Days
MVP Delivery Window
4 - 8 Weeks
IP Ownership Transfer
100%
Source: First-Party Platform Data
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Sample Size Safeguard in Action: Non-Published Study

Study: Early Consumer Shopping Preferences & Omnichannel Restocking Behavior in Indian Retail Tech

In strict adherence to our Research Quality Code, this pilot inquiry currently has an observed sample of only 42 pilot survey responses, which is below our required significance threshold of 500 respondents. Therefore, this study is marked with status: "insufficient_data", blocked from search engine indexation, and withheld from general publication until sufficient empirical data is gathered.

Open Data & Academic Citation Policy

All metrics published by Shrayu Technologies Research are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt our research provided that appropriate attribution is given to Shrayu Technologies.