AI Launches: Enterprise software, ecommerce, cybersecurity & search

The Tech Panda takes a look at recent launches in the superfast field of Artificial Intelligence (AI).

Enterprise software: A new state-of-the-art Tabular Foundation Model transforming how Enterprises harness AI for Structured Data

Lexsi Labs (formerly AryaXAI Alignment Labs), by Aurionpro, the research lab dedicated to frontier research in AI Alignment and Interpretability, launched Orion-MSP, launched new state-of-the-art (SOTA) foundation models to deliver enterprise-grade accuracy and scalable deployment for any tabular predictive task. It also released Tab Tune Open-Source Library, a first-of-its-kind open-source tool that standardizes fine-tuning and inferencing for leading tabular foundation models, including Orion-MSP.

“All current AI research is primarily aiming to predict the world in ‘zero-shot’. We are seeing this achieved by modality-specific SOTA models like LLMs for text, LRMs for reasoning tasks, and LVMs for vision tasks. We want to make this possible as well for tabular predictive tasks with Orion-MSP class of models. Orion-MSP is the state-of-the-art model, with top mean-rank accuracy across various benchmarks compared to other TFMs, including classic Machine Learning models,” says Vinay Kumar, Founder & CEO of Arya.ai and Founder of Lexsi Labs. “We are also launching TabTune, which is the perfect tool for any practitioner or enterprise to infer or fine-tune TFMs. With advanced components like fairness and conformity index, this is purpose-built for enterprises. We are releasing these classes of models under the MIT license, making both the models and the tool fully usable without constraints.”

Features

Enterprises such as banks, insurers, and healthcare organizations generate large volumes of structured, table-based data from transactions, customer records, insurance claims, health records, sensor telemetry, and ledgers.

Lexsi Labs’ latest models allow organizations to predict any task in zero-shot, on such enterprise data, using a single model through an API or improve it further through fine-tuning to achieve the best performance in just a few lines of code.

This transforms the current data science from multi-pipeline processes into a zero-shot or few- step deployment.

Ecommerce: Real-Time Pricing Engine Built for Grocery

Quick commerce grocery platform Satvacart launched Shiv AI, a patent-pending innovation that brings real-time, session-level price optimization to grocery e-commerce. The system continuously learns from user behavior, adjusting prices, offers, and on-screen nudges a global first in the category.

Rahul Hari, Founder & CEO, said, “With Shiv AI, our platform behaves like a smart kirana. It computes contribution margin in real time as a customer adds items to their cart. Based on loyalty, frequency, and session behaviour, it shares part of that margin back with the customer tailoring the offer to each user.”

Features

• The AI platform SHIV optimises prices in real time in the session by maintaining knowledge of customer behavior and relationship.
• The system runs continuous experiments, adapting layouts, prices, and nudges autonomously. Over time, it reduces our dependence on category managers and marketing teams  and drives every decision with profit and personalization in mind.

Cybersecurity: Open-Source Security Benchmark for LLM backends in AI Agents

Check Point Software Technologies Ltd. (NASDAQ: CHKP), a company of cyber security solutions, and Lakera, an AI-native security platform for Agentic AI applications, with researchers from The UK AI Security Institute (AISI), announced the release of the backbone breaker benchmark (b3), an open-source security evaluation designed specifically for the security of the LLM within AI agents.

“We built the b3 benchmark because today’s AI agents are only as secure as the LLMs that power them,” said Mateo Rojas-Carulla, Co-Founder and Chief Scientist at Lakera, a Check Point company. “Threat Snapshots allow us to systematically surface vulnerabilities that have until now remained hidden in complex agent workflows. By making this benchmark open to the world, we hope to equip developers and model providers with a realistic way to measure, and improve, their security posture.”

Features

• The b3 is built around a new idea called threat snapshots. Instead of simulating an entire AI agent from start to finish, threat snapshots zoom in on the critical points where vulnerabilities in large language models are most likely to appear.
• By testing models at these exact moments, developers and model providers can see how well their systems stand up to more realistic adversarial challenges without the complexity and overhead of modelling a full agent workflow.

Search: New Vector Storage Format DiskBBQ for More Efficient Vector Search

Elastic (NYSE: ESTC), the Search AI Company, announced DiskBBQ, a new disk-friendly vector search algorithm in Elasticsearch that delivers more efficient vector search at scale than traditional industry-standard search techniques used in many vector databases. DiskBBQ eliminates the need to keep entire vector indexes in memory, delivers predictable performance, and costs less.

“As AI applications scale, traditional vector storage formats force them to choose between slow indexing or significant infrastructure costs required to overcome memory limitations,” said Ajay Nair, general manager, Platform at Elastic. “DiskBBQ is a smarter, more scalable approach to high-performance vector search on very large datasets that accelerates both indexing and retrieval.”

Features

• In benchmark testing, DiskBBQ demonstrated a balance of speed, stability and efficiency that is ideal for large-scale vector search on lower-cost memory infrastructure and object storage.
• As a disk-friendly ANN algorithm, it requires far less memory than HNSW, which keeps the entire graph in RAM by offloading data to disk and reading only relevant vector clusters at query time. This design removes memory as a limiting factor, enabling Elasticsearch to scale to massive datasets limited only by CPU and disk.
• DiskBBQ sustained query latencies of roughly 15 milliseconds while operating in as little as 100 MB of total memory, where traditional HNSW indexing could not run. As available memory increased, DiskBBQ’s performance scaled smoothly without the sharp latency cliffs typical of in-memory graph approaches.

Cybersecurity: India’s Fastest AI-Powered MDR Service with MTTR of just 6 minutes

ESET, a cybersecurity solutions company, launched two Managed Detection and Response (MDR) subscription tiers: ESET PROTECT MDR for small and medium businesses (SMBs) and ESET PROTECT MDR Ultimate for enterprises in India.

“Cyber threats are evolving faster than ever—and so must defense. With over 2 million cyber incidents reported in India last year, the urgency is undeniable. Our new AI-powered MDR services mark a major leap forward: delivering faster detection, intelligent automation, and human-guided response—all within minutes. Backed by ESET’s global threat intelligence, we’re empowering Indian businesses to move from reactive defense to proactive, AI-driven resilience,” said Parvinder Walia, President of the Asia Pacific Region, ESET.

Features

• ESET PROTECT MDR delivers a comprehensive cybersecurity package, offering 24/7/365 superior protection. This includes modern protection for endpoints, email, and cloud applications, vulnerability detection and patching, and managed threat monitoring, hunting, and response.
• For enterprises, ESET PROTECT MDR Ultimate offers continuous proactive protection and enhanced visibility, coupled with customised threat hunting and remote digital forensic incident response assistance.