Singapore, Singapore
If you're here, read this 👇 My name is Damien Jacquemart. I split my time between Paris and Bangkok, and I'm dad of two kids. And yes, I also build AI systems for a living. Ex-Dataiku lead Data Science, I've delivered AI projects for 100+ companies — from SMEs to CAC40 giants (L'Oréal, Sephora, Crédit Agricole...). Today, I co-founded FoodBear 🐻 with my partner Rata. We help Southeast Asian restaurant owners stop burning cash on Grab — and actually start profiting. How it works: our systems ingest menus, campaigns, ads and promotions, then deliver profit-first recommendations. Per item. Per branch. Per day. No analysts. No guesswork. My background: → Dataiku — L'Oréal, Sephora, Crédit Agricole → Co-founder Differs — +28% average profit growth for clients → Top 10 AI Innovation France — Le Point 🏆 I don't sell dreams. I build systems that run. Running a restaurant in Southeast Asia? We might have something to talk about. 🐻
Grab serves hundreds of thousands of restaurants in Thailand and millions across Southeast Asia. Most of them are bleeding money on campaigns, ads and promotions without knowing what actually drives profit. We fix that. FoodBear is an AI-Native Agency. Every single process is automated from day one. No analysts filling spreadsheets. No guesswork. Our systems ingest menus, campaigns, ads and promotions, then deliver the best fit for each item, each branch and each day. Profit-first recommendations — we tell restaurant owners exactly what's working, what's burning cash, and what to scale. 🍕 Ronin Pizza: +70% sales growth & 45x ROAS 🥟 Kinza Gyoza: 16x ROAS ☕ Bottomless: +31% sales growth 🍗 Dong Bang Ching Chicken: +285% earnings 🍲 Nico Nico: menu restructured, +17% sales I build the backbone of every automated process.
Alkimy is a closing engine for high-ticket infopreneurs — pairing an elite distributed team of sales closers with proprietary AI to convert leads into revenue. Architecting and shipping Alkimy's full stack — from frontier-model integration to cloud-native revenue infrastructure. My core expertise: turning state-of-the-art LLMs into production-grade business applications through rigorous prompt engineering, multi-agent orchestration, and applied AI systems design. I own three pillars end to end: 🎯 Sales-call simulator — real-time, voice-optimized training where closers rep against LLM-generated prospects. Multi-agent LLM pipeline: a generation agent producing psychologically realistic prospect behavior Turn-by-turn state machine governing confidence dynamics, objection resistance, and content-gating Latency-tuned for live voice via asynchronous agent scheduling Batch simulation engine running persona × skill-level matrices server-side to calibrate and validate model behavior at scale 🧠 Conversational intelligence layer — turns raw sales-call transcripts into actionable revenue signal. Verbatim clustering, personality type profiling, pre-call briefings, funnel & confidence analytics, per-source acquisition KPIs Stratified sampling + lightweight model assignment scaling semantic analysis to thousands transcripts without blowing context windows ⚙️ RevOps infrastructure — the full GTM stack for a distributed closing team, stood up from zero. Event-driven calendar-sync engine on Google Cloud Run VoIP/telephony provisioning and CRM 🛠️ Applied-LLM & systems expertise: State-of-the-art prompt engineering: system-prompt design, structured/JSON outputs, behavior calibration grounded in real human transcripts Multi-agent orchestration with cost/latency-aware model routing (heavy models for generation, fast models for classification) AI infra cost optimization via context-window management Active contributor to product roadmap
I document the process of finding product-market-fit when you start a business and real AI & automation projects I did for SMEs. What I cover: → Field-tested AI use cases (accounting, restaurants, publishing, e-commerce) → How to land your first AI clients without quitting your job → Building AI-powered services with no-code & low-code tools → The business side most AI creators skip: sales, pricing, client management Every video is based on real work, real numbers, real mistakes. No theoretical fluff.
Built a predictive AI SaaS platform for demand forecasting and pricing optimization. Led product from 0→1, serving retailers across Europe. Business Impact: - Clients achieved +15-26% revenue/profit improvements - Scaled across diverse retail models: luxury assortments (9 SKUs, $120M+) to mass-market (100,000+ SKUs) - Executive consulting: advised C-level on pricing, demand planning, promotional optimization Recognition: - 🇬🇧 Top 10 Innovations Retail Tech Show 2025 UK - 🇫🇷 Top 10 French AI Innovations 2024 by Le Point - 🚀 Future40 - STATION F (2023) Solutions Delivered: - ⭐️ Markdown optimization: optimal discounts maximizing profitability. +28% gross profit in avg - ⭐️ Demand forecasting: short term and long-term until end of season, and by promotion mechanisms (BOGO, bundles, …). up to 60% less over-estimation, 50% more accurate vs. existing methods - ⭐️ Price elasticity modeling at SKU x channel level - Cannibalization analysis: cross-product impacts - Initial pricing: new products. +12% gross profit in avg - Competitor scraping: real-time market intelligence - Robust A/B testing framework creation - LLM merchandiser assistant: RAG with LangChain/LlamaIndex (GPT-4o, cross-encoder) AI R&D: - Demand forecasting, price elasticity, promotion optimization, markdown strategies, cannibalization, competitive intelligence Team & Leadership: - Built cross-functional team: data scientists, ML engineers, developers, designers - R&D culture: experimentation, rigorous validation - Client engagements: discovery to deployment - Mentored data scientists and engineers Technical: - ML pipeline: ingestion → training → real-time recommendations → A/B testing → deployment - AWS: containerized, auto-scaling, Terraform - MLOps: versioning, tracking, automated retraining, monitoring - Data engineering: web scraping, ETL, quality monitoring
I’m leading a cross functional team composed of Data Scientists, Implementation Manager and training specialists. I am directly responsible for the success of customers in my region (France and BeLux) and my industries (Finance, Telco, Transport, HealthCare), with a focus on service delivery. My responsibilities go throughout the sales cycle of a client: from pre-sales to implementation to data science service delivery. Implementation consists in quickly deliver value and set the basis for a long term value-driven usage of Dataiku. It includes installation, trainings and delivery of the firsts high impact projects up to production.
I lead a team of 6 data scientists in charge of enabling client, delivering project up to production and conducting POC. The team manages all our clients in banking, insurance, telco, transportation and public service industries. France and South Europe. Total portfolio under the team responsibility $19M.