IBM AI technology page Top Builders

Explore the top contributors showcasing the highest number of IBM AI technology page app submissions within our community.

IBM

IBM (International Business Machines Corporation), founded in 1911, is a global leader in technology and consulting. With headquarters in Armonk, New York, IBM has a rich history of pioneering advancements in computing and information technology, continually transforming industries with its innovative solutions.

General
Company	IBM
Founded	1911
Repository	https://github.com/IBM

Start building with IBM's products

IBM offers a wide range of innovative products and services that drive digital transformation for businesses of all sizes. From cloud computing and AI to quantum computing and blockchain, IBM's technologies empower developers and organizations to create and deploy powerful applications. Explore the possibilities with IBM's solutions and see what you can create during lablab.ai hackathons.

watsonx.ai

This is an enterprise AI studio that supports the entire AI lifecycle, allowing users to train, validate, tune, and deploy AI models. It includes tools for generative AI, machine learning, and foundation models like IBM Granite and third-party models from Hugging Face and Meta’s Llama 3. Watsonx.ai also offers capabilities such as the Prompt Lab for prompt engineering, Tuning Studio for model adaptation, and a Flows engine for seamless AI deployment.

watsonx.data

This component provides a robust data store built on an open lake house architecture, supporting both on-premises and multi-cloud environments. It facilitates data engineering, data virtualization, and cost optimization for data warehouses, allowing businesses to modernize their data lakes and streamline data pipelines.

watsonx.governance

A toolkit for AI governance, ensuring transparency, accountability, and ethical AI practices throughout the AI model lifecycle. It helps manage risks, monitor model performance, and ensure compliance with regulatory standards, making AI deployments more responsible and explainable.

watsonx Assistant: A conversational AI application for creating chatbots and virtual agents, enhancing customer service with natural language processing.
watsonx Orchestrate: An automation solution that uses AI to streamline workflows and automate repetitive tasks across various business domains like HR, sales, and procurement.
watsonx Code Assistant: A tool to aid developers by generating code based on natural language inputs, improving productivity and reducing coding complexity.

Granite Models

Granite is IBM's flagship series of LLM foundation models based on decoder-only transformer architecture. Granite language models are trained on trusted enterprise data spanning internet, academic, code, legal and finance.

👉 Try Granite on watsonx.ai

AI Models:

Granite 13b chat
Granite 13b instruct
Granite multilingual
Granite Japanese

Embedding Models:

The slate.125m.english.rtrvr and slate.30m.english.rtrvr models are bi-encoder sentence transformers that generate embeddings for various inputs like queries, passages, or documents. Both models are trained to maximize the cosine similarity between pairs of texts (e.g., a query and a passage), producing sentence embeddings that can be compared using cosine similarity.

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IBM AI technology page Hackathon projects

Discover innovative solutions crafted with IBM AI technology page, developed by our community members during our engaging hackathons.

Procurement for Public Sector Connectivity

UniSphere: Transforming Public Sector Procurement with AI UniSphere is an AI solution revolutionizing government procurement for connectivity projects through automated RFP analysis and bid evaluation. The Problem Government procurement suffers from inefficient, error-prone processes that lead to delays, wasted funds, and poor vendor selection. Creating comprehensive RFPs and objectively evaluating bids remains challenging for procurement teams. Our Solution UniSphere's procurement-specific Language Intelligence Model (LIM) processes complex documents with precision, built on open-source technologies for transparency and flexibility. Key Features RFP Analysis: Automatic requirements and criteria extraction. Technical specification identification. Gap detection in vendor proposals. Bid Evaluation: Objective bid scoring against requirements. Detailed strengths/weaknesses analysis. Best practices integration. Technology Built using Llama 3.1, IBM Granite, Hugging Face, Docker, PyTorch, and FastAPI—ensuring security, scalability, and seamless integration. Benefits for Users. Procurement Officers: Faster, more efficient processes. Technical Evaluators: Consistent, objective evaluations. Security Officers: Secure, compliant implementations. Challenges and Roadmap Current Focus: Security through secure self-hosting. Developing robust procurement datasets. Creating continuous improvement mechanisms. Future Plans: Enhanced security features. Risk prediction and sentiment analysis. Human-in-the-loop accountability. Ongoing model refinement. Conclusion UniSphere transforms public procurement by automating critical processes, helping governments save time, reduce costs, and improve decision-making. Our open-source approach ensures transparency and adaptability, building a foundation for more efficient, accountable procurement practices in connectivity projects.

Network Optimizar

Network Optimizer is an AI-powered solution designed to enhance digital connectivity by optimizing network resources in real-time. It addresses common challenges in network utilization, such as weak signal areas, underutilized infrastructure, and weather-related disruptions. Using AI and real-time data analysis, the system identifies connectivity gaps, predicts potential signal issues, and provides actionable recommendations for telecom providers and policymakers. The interactive dashboard, built with Streamlit, offers visual insights on network performance, helping stakeholders improve service reliability and optimize infrastructure investments. Future enhancements include predictive ML models, AI-driven user support, and 5G network optimization, making Network Optimizer a cutting-edge tool for digital connectivity.

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

Use of AI and open source to optimize networks

The project is focusing on track 3. We are optimizing resource allocation by integration open-source especially python and MySQL with AI technologies from IBM Cloud and Huawei Cloud. The problem is that schools in the public sector may not be conversant with monitoring telecommunication infrastructure such as determining signal coverage or even locating possible WI-FI faults. Additionally, their is lack of an optimization framework in underserved regions for how to use the internet. Our solution relates to creation of AI intelligence assistants together with data platforms, the goals for providing insights of factors such as predictive maintenance or performing tasks autonomously. We have created an intelligent assistant that is able to answer questions such as describing relation of signal strength, internet speed, bandwidth and reliability with having high downtime and latency. The Intelligent assistant also provides recommendations such as how to improve performance of basic telecommunication infrastructure. We are integrating open source platforms by using AI to generate new responses then taking the code used to generate the responses then placing it in open source platforms such as python or MySQL. Currently, the language being is used for the code is python, the code is obtained from Watsonx.ai which is from IBM. For asset utilization analytics i am using Cognos analytics so as to identify dormant or underused infrastructure with AI-driven tools. The AI driven tools provide narrative insights based on data uploaded in the form of excel sheets. Performance of the telecommunication infrastructure needs to be summarized into an excel document The project for last mile enhancement is designed by an application from Huawei which is called Appcube, Appcube has various features such an IoT (Internet of Things) platform that is used to simulate performance of telecommunication devices. The demo link is a prompt notebook from Watsonx.ai from IBM.

LexiSense-AI Powered Dyslexia Support

"🧠 LexiSense – AI-Powered Dyslexia Support LexiSense is an advanced AI-driven tool designed to assist individuals with dyslexia by enhancing reading, writing, and comprehension skills. Using fine-tuned AI models, it simplifies complex words, improves pronunciation, and offers real-time text assistance through text-to-speech and speech-to-text features. Key Features: ✅ Fine-tuned AI for word simplification & pronunciation support ✅ Real-time text-to-speech & speech-to-text conversion ✅ Dyslexia-friendly fonts & adaptive text modifications ✅ Phonetic breakdowns & audio hints for better understanding ✅ Interactive tools like quizzes & games for engagement ✅ Personalized learning & progress tracking ✅ Emotional support & confidence-building encouragement With an intuitive and accessible design, LexiSense empowers individuals with dyslexia to overcome learning challenges, making education more inclusive and effective. "

Generating Knowledge Graphs with IBM Granite

Utilized IBM granite instruct and code models to extract themes and relationships from documents. Intuitive visual representation of document data Easily identify key themes and connections for research and analysis Downstream tasks: Recommendation Systems Data Integration Semantic Search Industries: Education Finance Policy More… Successfully extracted themes and relationships using Granite model and generated Cypher queries for knowledge graph generation in Neo4j Challenges: Given the non-deterministic nature of LLMs Granite code instruct does not always generate a valid query. Future Work: Refining prompts for better/ more predictable and structured outputs from model