Gil Mazuz

🚀 #AI Funding News! Eikona, a Tel Aviv–based generative AI company focused on lifecycle marketing, has raised $5M in Seed funding. The round was led by StageOne Ventures, with participation from Crescendo Ventures Partners, Wix Ventures, Clarim Ventures, and several others — signaling growing investor confidence in AI-driven marketing innovation. With this new capital, Eikona plans to expand its operations and accelerate product development. Founded by ‪Nir Weingarten‬‏ (CEO) and Omer Hacohen (CTO), Eikona is redefining how enterprises execute lifecycle marketing. Its platform uses adaptive, RLHF-powered A/B testing to continuously learn from customer engagement, enabling brands to optimize content performance across: 📩 Email 📲 SMS 💬 Instant messaging 📱 In-app channels Serving industries such as retail, telecom, finance, insurance, and healthcare, Eikona helps businesses drive measurable improvements in sales, retention, and conversions. A strong step forward for AI-powered customer engagement. #AI #GenerativeAI #MarTech #LifecycleMarketing #FundingNews #SeedFunding #CustomerEngagement #Automation #Startups #Eikona

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Novee Security raises $51.5M in Seed and Series A. Led by YL Ventures, with participation from Canaan Partners and investor oren zeev through Zeev Ventures., marking one of the largest early-stage rounds in Israeli cybersecurity. Novee is building an AI-powered penetration testing platform that continuously simulates real attackers, helping security teams identify and prioritize real exploit paths in fast-changing environments. Founded by Ido Geffen, Gon Chalamish, and Omer Ninburg, the team combines deep offensive security, R&D leadership, and large-scale cybersecurity experience to rethink how organizations test and defend their systems. Follow Who Got Funded Israel for more startup funding updates.

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By far the highlight of this week’s PyData Tel Aviv was a highly practical session on 𝐀𝐠𝐞𝐧𝐭 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 by Linoy Cohen and Shirli Di-Castro Shashua, Ph.D. from Intuit. Here are the key takeaways I gathered: 𝐓𝐡𝐫𝐞𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐫𝐲 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐭𝐨 𝐜𝐨𝐦𝐛𝐢𝐧𝐞: 1️⃣ 𝐖𝐡𝐨𝐥𝐞-𝐚𝐠𝐞𝐧𝐭 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Judge the final output of the full flow of the agent, similar to system tests in software. 2️⃣ 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭-𝐥𝐞𝐯𝐞𝐥 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: For complex agents, similar to software unit tests, evaluate subcomponents directly to spot issues faster and easier. 3️⃣ 𝐓𝐫𝐚𝐣𝐞𝐜𝐭𝐨𝐫𝐲 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: A “white-box” look at internal steps of the agent: whether the correct tools were called, the number of calls, and the order of operations, etc.. A helpful library for all three approaches: 𝐚𝐠𝐞𝐧𝐭𝐞𝐯𝐚𝐥𝐬. 𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐮𝐬𝐢𝐧𝐠 𝐋𝐋𝐌𝐬 𝐚𝐬 𝐣𝐮𝐝𝐠𝐞𝐬: 🔵 𝐀𝐜𝐜𝐨𝐮𝐧𝐭 𝐟𝐨𝐫 𝐦𝐨𝐝𝐞𝐥 𝐛𝐢𝐚𝐬𝐞𝐬 (preference for first answers in comparison between two options, preference for longer text, preference for model's own output, authoritative tone, etc.). Mitigate or account for them. 🔵 𝐈𝐧𝐜𝐥𝐮𝐝𝐞 𝐟𝐞𝐰-𝐬𝐡𝐨𝐭 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 in judge prompts to reduce bias. 🔵 𝐀𝐬𝐤 𝐟𝐨𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧 𝐟𝐢𝐫𝐬𝐭, 𝐧𝐮𝐦𝐞𝐫𝐢𝐜 𝐬𝐜𝐨𝐫𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 to avoid justification of the number instead of real explanation. 🔵 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐬𝐜𝐨𝐫𝐞𝐬 𝐟𝐫𝐨𝐦 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐣𝐮𝐝𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬 for stronger reliability. 🔵 𝐎𝐧𝐞 𝐣𝐮𝐝𝐠𝐞, 𝐨𝐧𝐞 𝐭𝐚𝐬𝐤. Avoid combining multiple evaluation questions in one prompt. 🔵 𝐏𝐫𝐞𝐟𝐞𝐫 𝐛𝐢𝐧𝐚𝐫𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 (good/bad). If numeric scoring is required, define exactly what each number means. And the most important tip shared: 𝐃𝐫𝐢𝐧𝐤 𝐰𝐢𝐧𝐞 𝐚𝐧𝐝 𝐠𝐨 𝐬𝐤𝐢.

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