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Marionete

London, United Kingdom

10-49€45-85/hrFounded 2016
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About

Marionete is a London data and AI engineering consultancy building enterprise data platforms, self-service data science environments and machine-learning systems for banks, brokers and industrial groups, with a nearshore delivery office in Lisbon.

Who They Work With

Banks and insurers that need the data and MLOps platform under their AI built to regulated-industry standards.

Engagements:Embedded teams

Based on 6 client engagements with a source URL · researched 23 Sept 2026

Our Take

The clearest AI delivery on the site is infrastructure, not models: for AXA, Marionete built an MLOps factory on Databricks and Azure DevOps with a model registry, champion-challenger releases, human approval gates and drift monitoring, adopted by 4 teams across 2 business units and credited with 6x faster time to value.

The rest of the published work is data and integration engineering at bank scale—a payments orchestration platform tagged to HSBC, reported live in 54 countries and processing over $101.5bn a month, and a streaming DataHub built over 3 years for Saxo Bank.

The GenAI and agentic practice on the capabilities page has no case study behind it, and at Beazley the AI element is one bullet on extracting broker-submission data, with no model, accuracy or volume published.

The weaker fit is buyers who want an LLM or agent product built end to end and need a published production result for that kind of work before signing.

Key strengths

  • The AXA MLOps study names its operational controls—model registry, champion and challenger aliases, human approval gates, drift and cost monitoring—instead of stopping at a deployed model
  • Payments platform numbers are specific enough to test in a reference call: 54 countries in production, $101.5bn+ processed a month, a 99.998% payment message success rate
  • A named leadership team including a CTO and a Head of Data & Intelligence, street addresses in London and Lisbon, and an in-house Academy whose syllabus covers MLOps and GenAI

Good to know

  • No published case study shows a GenAI or agentic system in production—ask for a live reference before buying the AI & Agentic Solutions line
  • Case-study bodies name no client; identity rests on page tags and URLs—ask to speak to the platform owner at AXA or Beazley directly
  • Marionete publishes no rate itself; the rate shown here comes from a third-party directory listing—ask how London and Lisbon staffing shapes the day rate
Gabor Kiss

Reviewed by Gabor Kiss

Founder & Curator, AIAgencies.eu · 23 Sept 2026

Based on the agency's publicly available portfolio. Own this agency? Claim your profile to provide additional context or request a review update.


How we evaluate agencies

Notable Clients

Every engagement documented in a published case study

Documented engagements: AXA (UK and Ireland), Beazley, HSBC, Saxo Bank, Siemens, and Bank of England. All 6 carry a source URL on file.

AXA (UK and Ireland)Enterprise MLOps factory on Databricks and Azure DevOps: shared pipelines from development to production, model registry with champion and challenger aliases, human approval controls, drift and cost monitoring; MLOps maturity 1.3 to 3.8+, 6x faster time to value, adopted by 4 teams in 2 unitsSource
BeazleyCloud data platform on a federated data-product model, industrialized across cyber underwriting, policy administration and finance; Guidewire integration and extraction of unstructured broker-submission data; study credits it with supporting £5B+ in cyber premium quote growthSource
HSBCDistributed, event-driven payments orchestration platform with an ISO 20022 layer, reported live in 54 countries, $101.5bn+ processed a month at a 99.998% message success rate; data and integration engineering rather than AISource
Saxo BankStreaming DataHub built over a 3-year partnership: self-service schemas, topics and connectors, Scala stream processing, infrastructure as code across on-prem and Azure; data platform work rather than AISource
SiemensTestimonial from the Head of AI in Cybersecurity crediting AI capabilities built within the platform with near real-time malware detection
Bank of EnglandTestimonial from the Data Analytics and Modelling Director; no engagement detail published

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