π Welcome to Learning VC! Iβm Luis Llorens and I write monthly about venture capital, fundraising and my personal experiences as an investor.
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57 startup applications.
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Stages: 72% Pre-seed | 25% Seed | 4% Series A & B
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Geographies: 26% US | 21% UK | 9% Spain | 44% Rest of the world.
π€― 1,222 submissions in 2025.
If you're a business angel or VC and would like to see the list of startups, leave a comment below or scroll to the end of this post.
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π HQ: Madrid, Spain
π Industry: Generative Engine Optimization (GEO) / Martech SaaS
β Stage: Seed
π¨ Problem: AI search is reshaping digital discovery, yet 85%+ of businesses are almost invisible in AI-generated answers and lack tools to manage it. Many sites are already losing 20β50% of organic traffic, reducing leads and conversions, but brands have no unified metrics or influence layer for this new channel.
π Solution: AI-native SaaS platform that helps brands measure, benchmark, and improve their visibility in AI-generated answers across ChatGPT, Gemini, Perplexity and other emerging assistants.
π±Product:
π«° Business model: Tiered SaaS model with free trial
π― Ideal Customer Profile: Marketing Agencies and enterprise brands with large digital presence seeking GEO (Generative Engine Optimization)
π Traction: +10 Clients signed including an Ibex35 company. β¬+4M ARR Pipeline.
π Market: β¬3Bβ6B SAM, initially focused on European markets.
π₯ Luis Anton (CEO): 10+ years in strategy, finance and innovation, strong big-data & tech background, former Amadeus, HBG and BBVA, with multiple product and startup launches.
π₯ Maher el Ouahabi (CTO): Senior Data Scientist with ~10 years building AI products across industries, top performer on Kaggle competitions, ex-IBM, Santander and Akkodis.
π° Round: β¬700K Seed Round (nearly closed), accepting up to β¬50K from high-value, smart-money investors. Planning β¬+5M Series A within the next 6 months, driven by accelerating ARR and strong partner traction.
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π HQ: New York, US
π Industry: AI Infra
β Stage: Pre-seed
π¨ Problem: Agents fail frequently in production, they select wrong tools, follow inconsistent reasoning, and hallucinate, but failures are fragmented across logs, prompts, and tool calls, making root-cause analysis slow and manual.
π Solution: Infrastructure that enables your agentic system to learn from production. We observe agent behaviour, score performance, convert failures into lessons, and automatically deploy improvements back into your agent, enabling self-healing agents.
π±Product:
π«° Business model: Platform fee + per task fee of $0.001
π― Ideal Customer Profile: Companies building or deploying AI agents in production, either as their core product or for critical internal operations.
π Traction: Pilots with Palo Alto Networks and Auth0.
π Market: Deploying AI agents is easy; improving them in production is hard. Evals are static and fail to capture real-world edge cases, observability tools tell you what broke but can't prevent it happening again, manual prompt and context engineering doesn't scale.
π₯ Gaby Haffner (CEO): Led platform commercialisation at Farfetch, ex-IB and Strategy Consultant at EY, Cambridge econ grad.
π₯ Aman Jaglan (CTO): ML/RL engineer, ex-Protivi, led AI implementations for F500, published continual learning research.
π° Round: $2m
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π Full list for January: Here
π Real-time list for February: Here
Thatβs all folks β thank you for taking a look! If you liked this post, donβt forget to follow me on LinkedIn & Subscribe belowπ
