Insurance Intelligence,
Reimagined
Automate repetitive tasks, surface hidden signals, and streamline complex workflows — with insurance-native AI designed for carriers, syndicates and MGAs.
Automate repetitive tasks, surface hidden signals, and streamline complex workflows — with insurance-native AI designed for carriers, syndicates and MGAs.
Three live products today. Up and running inside your business in under six weeks. Fixed commercial terms — no science projects, no open-ended pilots.
Slip-literate. SOV-literate. MRC v3-aware. Trained on the documents specialty insurance actually runs on — not the public internet.
Every output explained, sourced and traceable. Every regulated decision reviewed by an underwriter. Built for the audit standards specialty insurance answers to — across the UK and US.
Clause-level comparison for renewal workflows. Every change identified, severity-rated, and traced to source. Minutes, not days.
Asset classification from schedules, slips, SOVs and loss narratives. The memory loop learns your firm's mappings over time.
Directors & Officers underwriting research in five minutes. Eight risk vectors. Every finding sourced, explained, and ready for review.
Reads every incoming submission, scores it against your appetite, and routes it before the first human touches it.
See how we turn complex challenges into real-world impact.
Featured delivery: Databricks-aligned lakehouse and AI data foundation implementation.
AI Agent · Specialty Insurance
Document Intelligence · PolicyCompareIQ
Data Platform · Microsoft Fabric
Clinical AI · NHS
D&O Underwriting · D&O AnalyzerIQThe slip, the SOV, the loss narrative — the documents specialty insurance actually runs on — needed AI engineered for them. Not generic tools retrofitted for our market. That's what we ship.
Every person at Marzal Labs has operated inside regulated industries — not just consulted to them. That means we understand the real constraints, the real stakeholders, and the real difference between a demo and a deployed system.
Founder and Principal Architect of Marzal Labs. Over 20 years working inside the UK's most data-complex regulated environments — Lloyd's of London, specialty insurance, NHS, regulated financial services, and government.
Led data and AI programmes across Nationwide Building Society, Talbot Underwriting, Gen Re, STARR, and the UK Covid-19 Test & Trace programme.
Architecture-first conviction: AI on unprepared data is a liability, not an advantage. MSc, Information Technology.
Solution Architect and consultant with 16+ years of experience delivering enterprise data platforms, including 5+ years architecting production-grade cloud lakehouse solutions across major cloud providers.
I specialise in designing scalable, governed data architectures that turn fragmented data estates into reliable, business-ready assets. Beyond platforms, I build AI products end to end - from data foundations through to working, production-grade solutions - combining deep architectural craft with hands-on delivery.
Blending technical depth with product and consulting skill, I shape platform and AI strategy, guide delivery teams, and align data and AI initiatives with real, measurable business outcomes.
An MSc Data Science graduate (Distinction) focused on reliable, explainable AI for regulated sectors. As a Data and AI Consultant she has built and evaluated NLP and sentiment-analysis systems for a sensitive, high-stakes dementia-care chatbot, applying responsible-AI principles throughout.
Her work spans deep learning and NLP, from BERT and BiLSTM pipelines (reaching 99.25% accuracy on a fake-news detection project) to LSTM demand forecasting over multi-million-row datasets and cloud-scale processing with PySpark. She brings that rigour to the Lloyd's of London specialty insurance market.
A Machine Learning engineer with applied experience in healthcare-focused AI. At Bluesense she designed, trained and validated deep-learning computer-vision models, including YOLO object detection optimised for real-time inference and deployable decision support.
She works across the full ML lifecycle, from large-scale data preprocessing and feature engineering through to model validation, and has built NLP and image-classification systems in Python with TensorFlow, PyTorch and Keras, always with an eye on responsible, explainable AI.
Why now is the right time to act. What leading specialty insurers are doing with AI. How to become an AI-Centric enterprise. UK / London market and US E&S.
No deck. No sales sequence. Just the product, your documents, and an honest read on what it does and doesn't do.