Apica announced the general availability of its Generative AI Assistant for the Apica Ascent Platform.
The first functionality introduced since Apica acquired Logiq.ai in August, Apica delivers advanced artificial intelligence capabilities to streamline and enhance data management worldwide.
The Apica Ascent platform gives users limitless storage, unified data pipeline control, and comprehensive insights at the lowest cost on the market. With the introduction of the Generative AI Assistant, Apica takes a significant step forward in simplifying and automating data management, enabling faster and more efficient delivery of high-quality contextualized data.
Ranjan Parthasarathy, Chief Strategy Officer (CSO) at Apica, said: “This marks a significant milestone in our mission to simplify data operations and give our customers much-needed relief over data growth, sprawl, complexity, and various challenges. By harnessing the power of artificial intelligence, we empower organizations to take full advantage of their data and get the most out of the applications they most care about.”
Strategically designed to reduce the friction common in the last mile of data analysis, the Generative AI Assistant provides context to analyzed data.
Apica’s new Generative AI capabilities help customers utilize time more efficiently by enriching data context and filling knowledge gaps. Additionally, customers now have the ability to bring their own modeling data to tune derivative output to meet their business objectives. The Generative AI Assistant will increase productivity and reduce toil by providing faster insights with more contextually relevant data. Thanks to its flexibility, the tool can be used for a broad set of data types and formats, giving context to data such as security and system events or OpenTelemetry data. This unique level of flexibility can be applied to both SaaS and on-premises deployments, ensuring Apica customers have access to the tools required to take full advantage of AI in their telemetry data streams. Apica’s data fabric architecture integrates many data sources and provides the necessary context. Now, with AI’s power, Apica adds context to all ingested data.
Apica is committed to providing users with amplified, rapid insights into their mission-critical data. The Generative AI Assistant integrates with the two leading vendors in the AI space, OpenAI ChatGpt and Azure OpenAI. Users can use popular models such as GPT-4 and GPT-3.5 Turbo or custom models on proprietary datasets.
The solution is available now globally.
The Latest
Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...
77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...
In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...
We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...
In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI's impact on the Wide Area Network (WAN) ...
Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...
The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...
A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...
The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...