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Up to 24% Faster: Ramping Up ML, Data, and DevOps with AI

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12 Highlights from Microsoft Build 2025 Keynote 

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Speedup in Software Engineering: How AI Boosts Front-End and Back-End Development at Janea Systems

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AI-Assisted Software Development Across the SDLC 

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Why 3D Tiles Change the Game for Massive 3D Scenes

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Janea Systems Team is Attending Microsoft Build 2025 

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Implementing Lab Workflow Automation in the Pharma Industry

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Porting Redis to z/OS

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Location Intelligence for Business Success with Robert Cheetham

In partnership with Microsoft Open Technologies, Inc., we embarked on a strategic initiative to create interoperable ecosystems that offer clients even greater choice and opportunity to bridge Microsoft and non-Microsoft technologies. This project enabled core interoperability scenarios between Microsoft and Open Source technologies, spanning developer tools (Azure, Linux, Java, Python), plug-ins, SDK’s to big data, cloud interop & web. This included:

- **HTTP/2.0 Enhancement**: Developed C# and Node.js-based HTTP/2.0 tools for Azure VM to advance web protocol standards.
- **HTML5 to Mobile App Tool**: Created a C# HTML5 conversion tool for mobile app project generation across major IDEs.
- **Contributing to Web Standards**: Prototyped new browser pointer events and DOM enhancements with C++ in Chrome on Windows 8.
- **HTTP/2.0 Control Flow Prototype**: Engineered a C# command-line client for Apache Web server to optimize HTTP/2.0 control flow.
- **Linux Performance Benchmarking**:Optimized Linux for web services, benchmarking SPDY protocols within Azure VM environments.

Our sustained work with Microsoft Open Technology has helped shape cutting-edge solutions that serve the developer community and empower the industry at large.

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Enabling Dev Tool Interoperability

Our involvement with the Query Annotation Service (QAS) project for Bing Geocoding was a significant undertaking aimed at enhancing the search engine's capacity to process user inputs and accurately identify physical locations and entities. This function is vital for search engines, assisting in handling the millions of location-based queries made by users daily.

**Key Areas of Focus:**
- **Automation of User Complaints:** Developed an Azure DevOps pipeline to automate the submission and resolution of user complaints. This automation was pivotal in streamlining the process of addressing geocoding errors and updating databases.
- **Test Dataset Enlargement:** Utilized a PowerShell script to extract malformed queries and geographic entities, significantly reducing the workload for project managers. This script was notably effective, identifying addresses in 71% of closed/resolved geocoding feedback data and matching 82% with valid addresses.
- **Database Updating:** Created a script to upload malformed query reports/data back into the DSAT database. This ensured that the regression framework database, which powers entity identification, had accurate data, thereby reducing errors.
- **Entity Set Resolution (ESR) Geocoding Tool Optimization:** Enhanced the performance and data richness of the ESR Geocoding Tool. This included addressing broken repository initialization, generating more detailed performance data, optimizing local cache generation, and ensuring the runMeasurement script in LocalPerfTest accounted for the current cache.

The overall impact of our work on the QAS project was significant. We streamlined processes, saving time for project managers and ensuring more accurate and reliable geocoding results. This project not only automated but also future-proofed the entire process, enhancing the efficiency and accuracy of the AI powering geographic entity identification."

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Enhancing Bing's Geocoding Capabilities

Classifying extensive dining options with precision.

Implementing AI Classification

 Curating personalized dining lists from vast choices

Creating Dynamic and Static Lists to Serve Recommendations

Integrating complex data streams into a user-friendly app interface.

Developing Tailored REST APIs

Trofimo

Delivered an intelligent recommendation engine pivotal for the app's core functionality. 

Enhanced User Engagement

Supported Trofimo's accelerated market entry, aligning with their strategic business timelines. 

Expedited Go-to-Market

Provided a scalable framework to incorporate future enhancements and functionalities 

Intelligent Feature Development

João

Senior Software Engineer

Trofimo enlisted our AI and cloud computing expertise to bring their vision for 'Dynery' to life – a social dining app designed to modernize the culinary journey for users worldwide. 

Discover how Janea Systems transformed Trofimo's 'Dynery' app with AI, delivering tailored dining experiences and accelerating market entry.

“Dynery” App: Serving AI-Powered Dining Recommendations  

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Serving AI-Powered Dining Recommendations

Ensuring core dependencies function correctly on the Windows platform.	

Dependency Stability

Extending Node.js support to ARM64 architecture for Windows.

ARM64 Support

Navigating open-source community dynamics for cohesive development.

Community Collaboration

Building a robust platform for corporate adoption.

Reliability for Enterprises

Addressing speed discrepancies for a seamless experience.

Enhancing Performance

Microsoft

Successfully eliminated frequent bugs and crashes, enhancing Node.js's stability on Windows.

Stability Achieved

Achieved a 40% improvement in file I/O speeds through our optimization efforts.

Performance Breakthrough 

Ensured core dependencies work reliably, supporting a wide range of Windows-specific applications and services.

Dependable Dependencies

Established Node.js as a reliable and preferred solution in corporate environments, broadening its reach.

Enterprise Adoption

Users can rely on native Node.js capabilities like ARM64 compatibility. 

ARM64 Adoption

Lead Software Engineer

Stefan

Senior Software Engingeer

Alexis

Founder & Chairman of the Board

When Node.js emerged as a leading server-side JavaScript runtime, it was primarily optimized for Linux and macOS environments, leaving Windows users grappling with stability and performance issues. Realizing the potential of Node.js, Microsoft demanded its high-performance and reliability on Windows to ensure widespread adoption. 
 

Find out why Microsoft had trusted Janea Systems to maintain Node.js on Windows for over a decade.

Maintaining Node.js on Windows Since 2013

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Node.js

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Ensuring a Smooth Node.js Experience on Windows 

Migrate and adapt an enterprise platform to a client-managed environment. 

Infrastructure Migration 

Transition from Okta SSO to Ping Federate with a complete authentication logic rewrite. 

Authentication System Revamp 

Upgrading & refactoring an inherited mix of JS, TypeScript, CoffeeScript, Python, and Terraform codebase. 

Technical Debt Management 

Fixing limitations in Signals Notebook that affect data processing speed. 

Functionality Gaps  

Full CFR 21 Part 11 and GXP compliance within less than a year, enabling regulatory approval for continued use. 

GXP Compliance within a Year

Reducing reliance on Excel macros and second-person reviews by automating calculations. 

6,000+ Scientists Benefited 

Scientists gained the ability to create custom workflows using the External Data Connector and Jupyter Notebooks. 

Workflow Customization 

Enhancing the pace of pharmaceutical research by implementing scalable data processing solutions for large datasets.

R&D Acceleration

Supported the creation of a dedicated division for scientific workflow automation and facilitated large-scale data processing projects. 

Long-Term Strategic Impact 

Filip

Vedran 

Dragos 

Discover how Janea Systems facilitated R&D platform integration for a biopharmaceutical company, setting standards in scientific research and development.



R&D Platform Integration for Pharmaceutical Research

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Platform Integration & Infrastructure Optimization for Pharmaceutical R&D 

Bing engineers were manually reviewing and updating geolocation error reports to feed them back to the geocoding model.

Manual Report Processing

The TensorFlow implementation in Bing Maps' DeepCAL model was 10x slower than PyTorch.

Deep Learning Bottlenecks

Fixed encoding length in TensorFlow's implementation increased file sizes by 3-4x, which slowed batch processing.

Batching Limitations

A training algorithm in an internal modeling tool was suboptimal.

Algorithm Inefficiency

The TensorFlow input pipeline worked slower than its PyTorch counterpart.

TensorFlow Latency

We eliminated manual query correction efforts by automating Bing’s geocoding query processing pipeline.

100% Automated Error Correction

TensorFlow implementation became 50x faster than its original state.

50x faster TensorFlow

Breaking up large files resulted in enhanced efficiency on 2 GPUs.

2x speedup in batch processing

The underperforming algorithm was reworked for 7x acceleration.

7x faster training runs

TensorFlow and PyTorch input pipelines reached performance parity, with TensorFlow gaining +30% speed on dual CPU.

30% speedup on dual GPU

50x Faster AI and 7x Faster Training: Optimizing Bing Maps’ Deep Learning

OtoNexus

Implemented QR code encoding and decoding for efficient data collection and retrieval. 

Patient Data Scanning

Integrated accelerometer to detect device movement and enable automated system responses. 

Device Motion Detection 

Optimized real-time analysis within the embedded C++ environment. 

On-Device Data Analysis 

Designed efficient power management protocols to extend battery life through automated standby mode and optimized charging cycles. 

Battery Management 

Developed state machines for transitions between charging, low battery, and standby modes. 

State Machines 

Enabled clear and responsive state displays, providing intuitive feedback during device operations. 

GUI Improvements 

Branimir

Janea Systems helped accelerate clinical assessment tool from prototype to production by implementing real-time data processing and battery management.

On-Device Analysis and Data Management for OtoNexus

On-Device Analysis & Data Management for Ear Infection Assessment

Training custom AI models requires significant investment and computing resources, making them impractical for a prototype.

Cost Constraints

Publicly available LLMs hallucinate in 40 to 60% of cases*, are non-deterministic, and are resource-intensive, which carries an environmental cost.

Reliability and Transparency

Core prototype built in 3 months, enabling BigFilter to test key business and technical assumptions.

Rapid Development

Delivered a functional, testable prototype at a significantly lower cost.

Cost-effective Prototype

Provided insights into key technological gaps, innovation opportunities, future costs, infrastructure requirements, and the performance of off-the-shelf LLMs relative to custom models.

Python

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