Frequently asked
questions, answered.

Red Olive is a UK-based data and AI consultancy. We help organisations turn their data into a genuine business asset: whether that means building data platforms, deploying AI, improving decision-making or modernising legacy systems. We work across insurance, housing, financial services and commercial sectors, and we focus on practical results, not hype.

Our clients include specialty insurers and reinsurers in the London market, housing associations and property managers, banks and investment firms, private equity-backed businesses, and organisations in the consumer, retail and technology sectors. We tend to work with organisations where data is central to operations and where getting it right has a direct impact on performance.

We provide AI strategy, design and implementation. That includes predictive analytics and modelling, generative AI and agentic AI solutions, and AI-powered automation of operational workflows. We build bespoke solutions and also integrate commercial LLM technologies where they add genuine value – always with a focus on transparency, explainability and measurable business outcomes.

We design and build enterprise data platforms that accurately reflect how a business actually operates. Our data modelling approach uses the BEAM method, which means we work closely with your teams to model data in business language, not just technical terms. We build on Microsoft Fabric, Databricks, Azure and other leading platforms, and we deliver decision-support tools and visualisations in Power BI that put insight where it’s needed.

Our work delivers results in three areas: growth – helping clients expand operations and improve revenue through better use of data; efficiency – saving costs and reducing manual effort through automation and process re-engineering; and compliance – strengthening risk, regulatory and governance positions through trusted data and responsible AI. We measure success by business outcomes, not technical outputs.

We are technology-agnostic and recommend technology which fits your context. Our deepest experience is with Microsoft Fabric, Power BI, Wherescape, Databricks and Azure, but we also work with AWS and other platforms. We choose the right tool for the job, not the one we have a partnership with.

We start most engagements with a short discovery phase to identify the use cases that will deliver the most value, assess data readiness, and map out what an enterprise-grade solution looks like. From there we move into iterative delivery – building a working pilot, testing it with real users, and then scaling it into production. This approach means you see results early and can steer direction as we go.

For data platform and engineering projects we typically use Agile methods, managed through Azure DevOps. For data science work we follow an iterative explore-build-test-refine cycle until we have a model that works in practice, which we then productionise. For consulting engagements we break work into sprints so you see progressive outcomes. We regularly embed within client teams and are happy to follow your preferred ways of working.

Our consultants have deep experience in business change and transformation. We help clients define strategies, develop business cases, design target operating models and manage the organisational changes that come with new technology and ways of working. We can lead or support depending on what you need – the goal is always to leave your organisation in a stronger position.

We work with specialty insurers and reinsurers in the London market including Lloyd’s syndicates. Our insurance work covers underwriting analytics, submission triage and processing automation, real-time aggregated risk detection, pricing model development, and claims data analytics. We also help insurers build the data platforms and governance frameworks needed to support AI adoption at scale.

We help banks and investment firms with data platform modernisation, regulatory reporting, predictive analytics and AI-driven decision support. Our work typically focuses on improving data quality and governance, building trusted reporting foundations, and deploying analytical models that improve risk assessment and operational efficiency.

We work with PE-backed businesses to accelerate value creation through data. That includes building data platforms that give management teams a clear, trusted view of performance, deploying analytics to identify operational improvements, and using AI to automate processes that are slowing growth. We understand the pace and focus that PE ownership demands.

We work with housing associations and property management organisations on data platforms, tenant analytics, predictive maintenance, compliance reporting and geospatial insight tools. Our solutions help housing providers understand demand, allocate resources, meet regulatory requirements and improve outcomes for residents.

We help consumer and retail businesses use data to understand customer behaviour, optimise operations and improve commercial decision-making. Our work includes building data platforms, deploying predictive models for demand and supply, and creating real-time insight tools that help teams act on what the data is telling them.

We work with TMT organisations on data engineering, advanced analytics and AI deployment. Typical projects include building scalable data platforms, applying machine learning to customer and network data, and designing analytical tools that support product, commercial and operational decisions.

We help you build a practical, results-driven data and AI strategy that links directly to your business goals. We cut through the hype to identify where data and AI will genuinely make a difference, and we deliver a strategy designed to be implemented – not a document that sits on a shelf. That includes assessing feasibility, building the business case, and identifying the people, processes and platforms needed to execute.

Most organisations have more data than insight. This service turns that data into clear, actionable intelligence by establishing a trusted single source of truth and then designing decision-support tools around the way your teams actually make decisions. We go beyond dashboards: we embed real-time analytics into operational processes so the right people get the right information at the moment it matters.

We design and build robust, scalable data platforms that bring scattered data together into a trusted enterprise asset. This covers cloud migration, data warehouse and lakehouse design, Master Data Management and data governance. We build on Microsoft Fabric, Databricks, Azure and other leading platforms, and we use business-led data modelling so the platform reflects how your organisation actually operates.

We help you identify where generative AI and agentic AI will genuinely add value, then we design, build and deploy solutions that are practical, trustworthy and measurable. We focus on transparency and explainability – if we can’t explain how a model reaches its conclusions, we won’t deploy it. Our work spans use-case identification, data readiness assessment, pilot development and scaling to production.

We build predictive models and analytical tools that are embedded in your operations, so insight leads to action. Our data scientists combine domain expertise with advanced techniques to surface patterns manual analysis misses, predict what’s coming – whether that’s customer behaviour, operational risk or market shifts – and deliver models that demonstrably improve performance.

We provide ongoing support for the platforms, models and systems we build. That includes monitoring and retraining production models as conditions change, proactive maintenance of data pipelines, and delivering ongoing enhancements through a flexible managed service. We built it, we understand it, and we make sure it keeps delivering.

We’ve packaged our experience into pre-built, sector-specific tools and frameworks that compress delivery timelines and reduce risk. Each accelerator has been tested in live client environments and is tailored to your organisation’s data, processes and requirements. Examples include automated bordereaux processing for insurers and compliance classification tools for housing providers.