
Graph Databases and AI: The Catalyst for Contextual Intelligence in the Enterprise
Simplified, cost-effective graph databases are driving AI innovation, enabling advanced applications like RAG and agentic AI.
Simplified, cost-effective graph databases are driving AI innovation, enabling advanced applications like RAG and agentic AI.
Graph AI is finally here, and it promises to fix many of the problems in current retrieval, augmenting generation AI services and improving accuracy, context, and reasoning capabilities.
The era of deep collaboration and democratised data analytics forces two very different disciplines – knowledge management and data management – together. A surplus of idealised hype and a lack of practical technologies add pressure.
Enterprise platforms now embed industrial AI agents, shifting from bespoke solutions. ServiceNow focuses on governance; Salesforce on proactive automation, simplifying deployment and impacting various enterprise roles.
Salesforce’s Agentforce 2dx introduces autonomous AI agents for background tasks, boosting workforce productivity by proactively preparing and processing information, streamlining workflows.
Atturra’s Kitepipe acquisition expands its North American footprint, bolstering Boomi integration, crucial for TechnologyOne and Microsoft ecosystems, with Philippine support enhancing scalability.
In this opinion piece by Dr. Joseph Sweeney, he discusses how GenAI’s promise transcends simple prompts. Real automation, targeting repetitive, complex, and costly tasks, will truly revolutionise work and productivity.
dLLMs’ parallel processing offers tenfold speed gains, reducing costs and enabling sovereign AI on standard GPUs, a key strategic shift for ICT.
SaaS implementation benchmarking reveals tangible improvements across twelve key areas, from cost savings to enhanced decision-making, validating cloud investment success.