Product manager at Chatfuel (YC W16), where I build and measure AI agents on WhatsApp and Instagram for service businesses. Economist by training, with two peer-reviewed papers. Tbilisi now, Vienna next.
About
Daniil Shaposhnikov is a product manager at Chatfuel, a Y Combinator (W16) company that builds AI agents for service businesses on WhatsApp and Instagram through a long-standing Meta partnership. Since 2023 he has owned activation and monetization — onboarding, pricing tiers and trial logic, freemium growth loops — and built the company's customer success function from zero, taking the product to clients in the US, Latin America and the EU. Before Chatfuel he was Head of Research at Netology, one of the largest edtech companies in Eastern Europe, and a product manager at Ultima.School in Brazil. He trained as an economist at HSE University in Moscow and co-authored two peer-reviewed papers on behaviour in professional sports labour markets, published in the Journal of Behavioral and Experimental Economics (Elsevier) and Applied Economics (Taylor & Francis) in 2022.
In my own words: I turn missed messages into booked calendars and paid orders — AI agents on WhatsApp and Instagram for service SMBs, built hands-on and measured like a research project. The through-line from the research years to the product years is the same habit: define the metric before building, instrument it, and let cohort data decide what ships.
I am based in Tbilisi and moving to Vienna. I work in English, Spanish and Portuguese, and I am learning German.
Now: Chatfuel, 2023–present
Chatfuel is an AI-native operating system for service SMBs: plug-and-play agents on the Meta Business API that capture, qualify and book leads from Instagram, Facebook and WhatsApp. The company is a long-standing Meta partner, went through Y Combinator in the Winter 2016 batch and raised a $1.5M seed round in October 2025 to build out the OS. What I do there:
Launch new products and business lines from zero to one — discovery and segment research, positioning and pricing, go-to-market and sales process — working in a tight pair with engineering.
Own activation and monetization end to end — the full activation funnel from acquisition creative to onboarding to paywall; pricing tiers, trial logic and billing on Stripe; a roadmap prioritised from cohort and retention data. I run the experiments and decide what ships.
Built customer success from zero — processes, playbooks and webinar funnels — and took it to new markets in Latin America, primarily Brazil, Mexico and Colombia.
Build AI workflows for key accounts hands-on — client brief, agent and workflow design, launch, reading results and tuning — for clients from single-location service businesses to a global mobility platform.
Rebuilt first-run and education flows around the AI agent and the booking calendar, launched the freemium calendar as a lead engine, and ran pricing, packaging and upsell experiments.
Write for the Chatfuel blog about what comes up on calls with agencies and businesses setting up AI automation on Instagram and WhatsApp — see Writing below.
Stack I work with: OpenAI Responses API (structured outputs, function calling), prompt architecture and fallback logic; Claude Code; Hono BFF, Zod/OpenAPI, REST and GraphQL, Stripe, Meta APIs (Instagram, Facebook, WhatsApp); React 19 and TypeScript; BigQuery, GA4, SQL, Python; Figma, Notion.
Side project: Savera
Savera is a chat-first AI kitchen assistant that I founded and build on my own — product, engineering and the articles on the site. You tell it what you bought by text, voice or photo; it estimates shelf life, warns you before food spoils and suggests recipes from what is about to expire, with one shared fridge for up to five people in a household. It runs as a web app (PWA) in English and French and is free during early access. Built with Next.js, FastAPI, Supabase and Paddle, with multi-model LLM orchestration.
The articles carry the research habit over to food waste: every figure comes from a primary source such as ADEME, Eurostat, INSEE or SDES, carries its year and a link, and is checked by me before a page goes live. Author page.
Ultima.School is a Brazilian online school for IT and digital professions, with cohort-based courses and career support. I owned monetization and end-to-end sales conversion, reporting to the CEO and CPO. In 2022 I led the replacement of the human presales stage with a ChatGPT-powered WhatsApp bot built on Chatfuel — before I joined Chatfuel. The bot answers website inquiries, books a free consultation with a career expert, sends reminders and re-engages old leads; acquisition costs fell by half at the same conversion to purchase as with human presales managers. Chatfuel later published the project as a customer story. Alongside that: client validation rate up 1.7×, the share of the best-performing funnel raised to 20%, and growth and unit-economics work with development, marketing and sales that improved commercial efficiency by 25% and halved the distance to break-even.
Head of Research — Netology, Moscow
Netology is an upskilling platform for digital professions and part of the Talent Tech group — 1,000+ employees, valued at $100M+ by Forbes. I built the market research and UX research function from scratch to a team of seven, with a user-insight database, an internal competitor-monitoring app and research training for product discovery. More than 30 studies informed expansion into five markets across four product tracks; the company grew 96% year on year in that period. Co-author and project lead of the Netology study of the Russian and CIS online adult education market (2021 and 2022 editions), the most-cited edtech study in the region with 20,000+ downloads. I wrote about speeding up product discovery for RBC Pro.
Researcher — Laboratory of Sports Studies, HSE University
Sports economics as a clean laboratory for labour-market questions: motivation, market inefficiency, performance data from the NBA, the NHL and European football. Two peer-reviewed papers (below), a presentation at the International Conference on the Economics of Football 2019 in Baku, and the lab's day-to-day operations.
Strategy Analyst, head of strategic planning — AV Consult
A niche consultancy developing strategies for urban areas. Second employee: helped win the first projects, wrote the methodology and hired the core team. Three strategies for areas in Krasnodar Krai, North Ossetia–Alania and Sakha (Yakutia), home to 90,000+ residents in total; the methodology was reused for 10+ municipalities.
Management board member — Economics and Mathematics School, Lomonosov MSU
An evening school for high-school students founded in 1968 at the Faculty of Economics of Moscow State University. Ran the school's operations and created and taught “Not in My Back Yard”, a course on urban economics for high-school students.
BSc in Economics — HSE University, Moscow
Specialisation in microeconomic theory: behavioural, institutional, labour and education economics; minor in urban studies. First place in the national “Higher League” olympiad for students and graduates, “Evidence-based Development of Education” track (2020); nominee for HSE's “Silver Nestling” graduate award (2019).
Writing
Practical pieces for agencies and small businesses on the Chatfuel blog, drawn from weekly calls with people setting up AI automation on Instagram and WhatsApp.
Both papers come from HSE's Laboratory of Sports Studies and use professional sport as unusually clean data on how people and labour markets behave. Together with the 2020 working paper they have been cited 27 times (Google Scholar, November 2026).
Prove them wrong: Do professional athletes perform better when facing their former clubs?
With Artur Assanskiy, Igor Tylkin and Gleb Vasiliev. Journal of Behavioral and Experimental Economics, vol. 98, 2022 (Elsevier; Q1 in Social Sciences). DOI 10.1016/j.socec.2022.101879
Using player-level data from multiple seasons of the NBA, the NHL and the major European football leagues, we found that athletes take more shots and score more against their former clubs. The effect is stronger in away games and when the player had little playing time at the former club, took a pay cut on transfer, or was waived — evidence that people are motivated to outperform a former employer.
Two German Bundesliga seasons (2017/18 and 2018/19), 1,224 team-game and 772 player-season observations. Defensive actions contribute to winning more than the transfer market pays for them: defenders are underpriced relative to forwards, a Moneyball-type inefficiency in the football labour market.
I live with my wife Sofiia and Mallo, a Cavapoo born in December 2025 who has been working with a trainer since he was a puppy. We are moving to Vienna together.
Off the clock I cook (carbonara, pulled beef, cured salmon, chili), drink specialty coffee, follow football — the Russian and English leagues most closely — and read about the history of multilingual cities, which is part of why Vienna.
One for the record: in February 2022 the Laboratory of Sports Studies fielded a team on the Russian TV quiz show 100 to 1 (“Сто к одному”) against Match TV, and I was one of the five players — the lab's note and the episode.
Questions people ask
Who is Daniil Shaposhnikov?
Daniil Shaposhnikov is a product manager at Chatfuel, a Y Combinator (W16) company that builds AI agents for service businesses on WhatsApp and Instagram. He trained as an economist at HSE University in Moscow and co-authored two peer-reviewed papers in sports economics published in 2022. He is based in Tbilisi, Georgia, and is relocating to Vienna, Austria.
What does Daniil Shaposhnikov do at Chatfuel?
Since July 2023 Daniil Shaposhnikov has owned activation and monetization at Chatfuel — onboarding, pricing tiers, trial logic, billing — launched new products and business lines from zero to one, and built the customer success function from scratch, taking the product to clients in the US, Latin America and the EU. He also writes for the Chatfuel blog about AI automation on Instagram and WhatsApp for agencies and small businesses.
What is Savera, and how is Daniil Shaposhnikov involved?
Savera (savera.kitchen) is a chat-first AI kitchen assistant that helps households waste less food. Daniil Shaposhnikov founded it and builds it on his own as a side project, alongside his role at Chatfuel, and writes its data articles on household food waste, drawn from primary sources such as Eurostat, ADEME, INSEE and SDES.
Is this the same Daniil Shaposhnikov as the game developer?
No. Several people share the name Daniil Shaposhnikov, including a game developer. This page is about the product manager at Chatfuel who studied economics at HSE University and worked at Netology and Ultima.School.
What has Daniil Shaposhnikov published?
Two peer-reviewed papers: “Prove them wrong: Do professional athletes perform better when facing their former clubs?” in the Journal of Behavioral and Experimental Economics (Elsevier, 2022) and “Moneyball in offensive versus defensive actions in football” in Applied Economics (Taylor & Francis, 2022). He also co-authored and led Netology's study of the Russian and CIS online adult education market, downloaded more than 20,000 times.
Where is Daniil Shaposhnikov based, and which languages does he work in?
He is based in Tbilisi, Georgia, and is relocating to Vienna, Austria. He works in English, Spanish and Portuguese, is a native Russian speaker, and is learning German.
