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oril

Real Rental Data & AI-Ready Infrastructure - With Jonas Bordo, Dwellsy | The Innovation Blueprint Podcast

Jul 27, 2026 By Roman Havrylyuk In ORIL
For as long as there’s been a rental market, there’s been a version of this question: is the number on the listing actually the number? Ask anyone who’s built a pricing model, a forecasting tool, or a CPI estimate on top of rental data, and you’ll get the same answer — probably not, and there was never a good way to check.
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oril

AI Is Only as Good as Your Property Data: Preparing Data Foundations for AI Initiatives

Jul 27, 2026 By Ihor Kosandyak In ORIL
PropTech teams sometimes plan an AI initiative by starting with the wrong question. They ask which model to use before they can answer a more basic one: is our property data ready for any of it? AI pilots may stall not because the models underperform, but because the property data feeding them is fragmented across MLS feeds, PMS records, and CRM exports, duplicated across sources, and missing the ownership, tax, and location context a model needs to reason.
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cloudera

The Missing Piece of Your AI Strategy: Data at the Edge

Jul 27, 2026 By Katie Gdula In Cloudera
Companies everywhere are rushing to deploy AI models to outpace the competition, but they are running headfirst into a brutal reality check: an AI model is only as brilliant as the data feeding it, and most data simply isn't AI-ready. The high-value, real-time data required to power these models doesn’t live in a pristine, pre-formatted cloud data warehouse. It is generated in the physical world on factory floors, inside hospital rooms, and at point-of-sale terminals.
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The Best Tier 1 SOC Automation Tools in 2026

The Best Tier 1 SOC Automation Tools in 2026

Jul 27, 2026 By SystemsDigest In SystemsDigest
Tier 1 SOC is alert triage, enrichment, initial investigation, and escalation. Most of this work is repetitive and hard to scale, and legacy options for automating it (e.g., SOAR) can't keep pace with modern workloads because they're engineering-led, not analyst-led or browser-based (where the actual work happens). The tools below automate tier 1 work, and all of them use AI in some way. They range from AI SOC analysts that investigate alerts the way a human would to automation platforms with AI layered on top, plus AI built into platforms you may already run.
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databox

How to Use the Shopify MCP with Claude, What It Does Well, and How Databox MCP Completes the Picture

Jul 24, 2026 By Nevena Rudan In Databox
Shopify’s MCP servers give Claude real command of your store, from storefront conversations to bulk product updates. Analytics is the one job they were never built for, and pairing them with Databox MCP closes that gap.
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databox

AI Marketing Forecasting: The Plan Is Only as Good as the Data It Can See

Jul 24, 2026 By Nevena Rudan In Databox
Ask a Marketing Lead how the quarterly plan actually gets built. Not the strategy, the mechanics. The answer, in most teams, is a spreadsheet: spend pulled from five ad platforms with five different backends, pipeline exported from the CRM, last quarter’s numbers copied from a deck, targets negotiated in a separate thread. One customer described their version of it to us in a sentence that needs no editing.
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katalon

Building a Playwright AI Agent: How Claude Code Drives Katalon True Platform End to End

Jul 24, 2026 By Huy Tieu In Katalon
Claude Code shipped workflows: reusable, versioned instructions an agent can follow instead of improvising a process from scratch each time. We used that feature to hand Claude Code our entire QA job. Not "write a test case." The whole loop: read the requirement, design the coverage, run it on the platform, file the defect, report back with evidence. The kind of work a QE does in a day, compressed into one skill triggered with a sentence.
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smartbear

API definition-native AI testing: Support faster, confident shipping with your existing Swagger and OpenAPI specification

Jul 24, 2026 By Vyshnavi Dabbir In SmartBear
APIs are the backbone of modern software. They connect microservices, power mobile experiences, and make integrations possible across industries. For all their importance, API testing remains one of the most fragmented, manual, and maintenance-heavy parts of the software development lifecycle (SDLC). So as development accelerates in an AI-disrupted SDLC, application integrity – continuous, measurable assurance that your software just works as intended – becomes harder to maintain, not easier.
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smartbear

What Is the SmartBear Zephyr Agent for Rovo? AI testing in Jira, explained

Jul 24, 2026 By Alisha Siddhartha In SmartBear
AI can now handle the slowest parts of test management, inside Jira. – The Zephyr Agent for Rovo creates test cases and links them to your work items, in the projects where your team already plans and builds. This guide covers how testing is changing in the AI age, what the agent is, where to find it, and how to run your first task.
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ClearML

Managing Across Schedulers: HPC Meets Kubernetes

Jul 24, 2026 By Adam Wolf & Damian Erangey In ClearML
Almost every infrastructure team running modern AI is wrestling with the same question. The orchestrator their AI workloads want, Kubernetes, and the orchestrator their HPC environment was built on, Slurm, pull in different directions. This piece, drawn from the HPCKP 2026 session of the same name, looks at why that tension exists, what the workloads actually look like, how teams are bridging the two today, and where the pattern is heading.
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