About Chelsea AI Ventures
Most AI projects fail between prototype and production. We deliver working AI systems in weeks rather than months while building your team's capabilities to maintain and extend solutions independently.
Innovation Hub & Expertise
AI Consultancy
Expert guidance for SMEs implementing and scaling LLM technologies with measurable ROI.
AI Education
Comprehensive training programs and mentorship to build AI capabilities within organizations.
Startup Hub
Launching innovative data and AI products to solve specific business challenges.
Our Team
Ben Auffarth
Founder & Chief Data Officer
Enterprise AI Leadership & Implementation
With 15+ years of individual experience in AI, Ben has architected and deployed mission-critical systems including fraud detection platforms with £60M impact, real-time decision engines processing 100k+ transactions daily, and ML-driven marketing optimization delivering 15% ROI improvements. His expertise is showcased in three bestselling books with about $200,000 in sales. His research in computational neuroscience and machine learning, and his technical writing earned him 500+ citations, while his industry experience includes building up data systems, infrastructure, and teams from scratch.
Ben holds a PhD in Computer Science from the Royal Technical University (KTH) in Stockholm, where his thesis "Machine Learning Techniques with Specific Application to the Early Olfactory System" established foundational principles for understanding how biological neural networks process complex information. Nowadays, he's running a consultancy specialising in bespoke AI development and expert team augmentation.
Minghan Bao
Head of Sales
Technical Sales & Business Development
With a Ph.D. in Chemical and Process Engineering and expertise in artificial intelligence, Minghan brings a unique technical perspective to our client partnerships. His background in industrial AI applications enables him to quickly understand client challenges and connect them with the right solutions.
Prior to joining Chelsea AI Ventures, Minghan gained valuable experience at Industrial Tomography Systems and led an EPSRC-funded research project focused on commercializing advanced measurement technology. His blend of technical knowledge and business acumen helps our clients identify high-value AI opportunities that deliver measurable returns.
Connect:
Justin Ju
Junior AI Associate
Project Research, Data Visualisation
Justin Ju is a Mathematics student at Imperial College London who combines strong analytical capabilities with practical business acumen at Chelsea AI Ventures. With expertise in mathematical modeling, supervised machine learning, and data visualization using Python and R, Justin helps translate complex technical concepts into business value for our SME clients. His leadership experience from managing successful sales events in the Young Enterprise program, coupled with fluency in English, Cantonese, and Mandarin, enables him to effectively communicate solutions across diverse markets—particularly valuable for clients looking to implement AI solutions with clear ROI in international contexts.
Connect:
Saman Shah Hosseini
AI Engineering Intern
MLOps, Time Series, Teaching
Saman Shah Hosseini is an AI Engineering Intern at Chelsea AI Ventures, where he develops agentic and generative AI solutions that transition smoothly from prototype to production. As a Machine Learning Engineer at ATLAS R&D Group, he delivered an automated ophthalmic test classifier that digitises thousands of clinic results each month eliminating paper waste and accelerating patient care. Holding a BEng in Computer Engineering and pursuing an MSc in Artificial Intelligence, Saman explores LLM tooling and AI for finance, driven by a belief that well-crafted AI agents can unlock smarter, greener workflows at scale.
Connect:
Our Product Ecosystem
VeritaMetrics
- Privacy-focused tracking
- Behavior analysis
- Custom dashboards
- Platform integrations (in development)
KirokuForms
- Drag-and-drop builder
- Human-in-the-loop options
- Full API capability (REST, MCP)
- Secure data collection
- Custom branding options
You x you i
- Market fit testing phase
- Early adopter program
- User feedback integration
- Rapid iteration cycles
Akilima
- Predictive Pricing Optimizer
- Shopify Data & SEO Plugin
- AI-Powered UX Research
- Aegis AI Compliance Monitor
- Optimus Logistics Engine
- Veritas Logic Auditor
Published Books
Retrieval Augmented Generation, The Seminal Papers
Published: March 2026 | New Release
Principles for architecting reliable and verifiable AI
Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data. RAG has grown from a simple prompt engineering workflow into a sophisticated set of data analysis, storage, and retrieval techniques. Retrieval Augmented Generation, The Seminal Papers explores foundational research papers that explain why RAG works, how it’s built, and what makes it different from other approaches.
Focus areas include:
Generative AI with LangChain (2nd edition)
Published: May 2025 | Amazon Bestseller in Programming
Build production ready LLM applications and advanced agents using Python and LangGraph
A practical guide to leveraging LangChain and LangGraph for GenAI implementation, with real-world examples ranging from customer support to data analysis. The 2025 edition features updated code examples and improved GitHub repository.
Focus areas include:
Machine Learning for Time Series
Published: October 2021 | Industry Standard Reference
Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods. This comprehensive guide covers everything from data preprocessing to advanced models for time-dependent data. The included tutorials range from simple forecasting to complex deep learning architectures for time series analysis.
Focus areas include:
Artificial Intelligence with Python Cookbook
Published: October 2020 | BookAuthority Best-Seller
Proven recipes for applying AI algorithms and deep learning techniques
Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch. The practical cookbook approach provides ready-to-use solutions for common AI challenges, from computer vision to natural language processing, with complete code examples and detailed explanations of implementation considerations.
Focus areas include:
Speaking & Conferences
Moving Beyond Statistical Parrots: LLMs and Their Tooling
ODSC 2024, Data Science Week Amsterdam
Focus: Open-source LLMs and enterprise implementation
Time-Series in Python -- Preprocessing and ML
ODSC 2022
Focus: Machine learning techniques for time-series data
Strategic AI Implementation
Data & Analytics Summit EU 2022
Focus: Enterprise AI strategy and implementation roadmaps
Future of Data Science and LLMs
PyData London 2023
Focus: Panel discussion on open-source AI technologies
Our Values
Technical Excellence
Business Impact
Ethical AI
Knowledge Transfer
Ready to explore our ecosystem?
Let's discuss how our team and products can help your organization succeed.