Observed Signal · Aug 24, 2026 · Funding · Source: EU-Startups · Impact: 2/5 · Sentiment: Positive
Kazimi Raises €2.2M to Secure App First-Party Data
Berlin-based mobile cybersecurity startup Kazimi has secured €2.2 million in a pre-seed funding round led by Market One Capital, with participation from IBB Ventures and prominent mobile industry executives. Founded in 2024, Kazimi provides a privacy-preserving security solution that helps mobile app developers protect and verify their first-party data. By utilizing cryptography and zero-knowledge proofs, the platform detects bot activity and fake users, keeping data accurate and compliant. The startup plans to use the new capital to scale its solution and target the global mobile ad spend market.
Securing first-party data and mitigating bot traffic/fraud is increasingly critical for mobile publishers and advertisers in a privacy-first ecosystem.
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Key Takeaways & Evidence Grounding
- Kazimi secured a €2.2 million pre-seed funding round led by Market One Capital.
- IBB Ventures and several mobile industry executives (including ex-Rovio, ex-Supercell, and ex-Sportradar leaders) participated in the round.
- Founded in 2024, Kazimi uses cryptography and zero-knowledge proofs to distinguish real users from bots.
- The capital will be used to scale Kazimi's solution for the global mobile ad spend market.
Connected Companies & Entities
1 Entity mapped“...according to Cloudflare, yet mobile businesses are unable to distinguish signals from bots......”
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Meta, Walmart, Sierra launch personal agent protocol
Meta and Sierra, led by chairman Bret Taylor, have announced the Personal Agent Protocol (PAP) for AI-agent interactions with businesses, with initial partners including Walmart, Shopify, Stripe, Genesys, Rocket, and Instinct. PAP defines three access levels (guest, read-only, and write access) built on OAuth and places businesses in charge of vouching for consumer AI agents, differentiating it from rival designs by Visa, Mastercard, and AI platforms. The protocol aims to control customer data and liability in agentic commerce, with future implications for agentic payments. However, PAP is still in early stages: no specification, license, or governing body exists, and payments are not yet supported. Version 0.1 is expected this month, with payments and fine-grained permissions planned later. Notably, Amazon, Google, and OpenAI are absent, reflecting Meta's competitive positioning, especially after Amazon blocked Meta's Muse agent. The protocol competes with OpenAI's Agentic Commerce Protocol and Google's Universal Commerce Protocol.
TypeSafe's Jev Decision Model Explained for Product Managers
The article introduces Jev, a decision model from TypeSafe AI, released in September 2026. Unlike LLMs, Jev returns a decision with a probability based on input text and a question, supporting operations like Noul (yes/no), Choice (one of many options), and Score (rating). It processes up to 32K tokens, with free output tokens and input at $0.042 per million tokens. The author tested it against other models, finding it cost-effective and accurate. Jev is integrated into AskOne for moderation, with rules encoded in prompts to handle abuse and prompt injection. The article notes Cloudflare released similar models (Clef) on October 1, and provides guidance for PMs on quick wins, evaluation, and setup templates.
Google DeepMind Launches Gemini 4 Argon with 1M Output
Google DeepMind introduced Gemini 4 Argon, a new frontier model aimed at coding, enterprise knowledge work, and cyber defense. It is currently rolling out to a limited set of trusted testers through the Fairwind Program, including government users and cyber defenders. Argon demonstrates SOTA results in 13 of 19 credible benchmarks, and offers an industry-first 1M-token output via Long Decode Continuation. Pricing is set at $4/$20 per million input/output tokens, with a 50% introductory discount and 95% discount for cached input. Google also claims internal agentic deployments have freed over 300 TiB of memory and are migrating 800K lines of C/C++ code to Rust. Early evaluations show Argon matching GPT-6 Astra on the Artificial Analysis Intelligence Index (53), but also show higher token usage per task and trade-offs in accuracy vs. hallucination rates.
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