We show where AI truly adds value in insurance — from more effective sales and sharper risk assessment to shorter processes and higher conversion rates. Whenever a challenge arises, we help test solutions in practice.
What automations are worth implementing? We’ll show you which processes to automate to optimize sales, service, and operations — not just for today, but for the years ahead.
Which AI tools make business sense? We’ll match the technology to your goals and budget. No costly experiments, with a focus on fast returns.
How do you manage growing process complexity? We’ll design solutions that bring order to your environment, make scaling easier, and give you greater control over change.
How do you build AI capabilities within your organization? We’ll deliver the technology, ways of working, and know-how to help your teams develop further automations.
BENEFITS
Why does AI
make sense in insurance?
Smarter decisions
We design solutions that streamline analysis and organize data, so teams find what they need faster and decide with confidence.
Less manual work
We help reduce the time spent on repetitive tasks, freeing up experts and improving the day-to-day efficiency of your teams.
Better market insight
Our AI tools produce fast, precise policy wording comparisons, supporting the development of more competitive offerings.
Faster document analysis
Our AI solutions speed up the analysis of medical, financial, and expert documentation, making it easier to close a policy, assess insurance needs, or process a claim.
SERVICES
See where AI can support your processes
We deliver AI projects for insurers and intermediaries — from document analysis and decision support to fraud detection and operational automation. Each of these areas can be validated in practice through a PoC.
Automated document reading and processing using AI, reducing manual work in operational workflows.
Correspondence management. Brings order to information flow — AI handles process orchestration, case routing, data extraction, and next-step recommendations.
Claims document analysis. AI reads PDFs and document images, extracts key data, and identifies information and policy documentation references that form the basis for the claims adjuster’s decision.
RFQ analysis. AI compares client or partner requirements against offer content, prepares a summary of key information, and flags deviations from the request for quote.
Solutions where AI analyzes data, identifies patterns, and suggests the most appropriate next step in the process — supporting people with faster, more consistent analysis rather than replacing them.
Medical and financial underwriting decisions. AI-supported risk assessment based on medical, financial, and behavioral data.
Claims decision recommendation. AI automatically prepares a decision recommendation with a rationale for the adjuster, extracts data from policy documentation, and identifies the clauses underpinning the decision. The system also checks the claim against policy terms, reducing the risk of oversights.
Solutions where AI supports process compliance with regulations — reducing risk, strengthening controls, and speeding up decision-making through automated data analysis and well-configured rules./span>
Real-time fraud detection. AI models analyze events in real time, so teams can respond to threats faster and more precisely. Depending on your needs, we build solutions based on business rules, models trained on historical cases, or detection of anomalies and unusual behavior patterns.
AML transaction monitoring. AI-driven analysis of transactions and customer data, so teams can focus on real threats rather than processing high volumes of alerts.
AI assistant grounded in your organization’s knowledge, providing quick access to procedures, products, and operational information.
MCP layer enabling AI assistants to use data and functions from existing insurance systems in a controlled and auditable way. It allows AI to support operational processes without replacing core systems or reducing control over data access.
Controlled access to systems.AI is connected with policy, claims, CRM, document management, and finance systems through a single managed access point.
Roles and boundaries for AI.Access to data, system functions, and process stages is clearly defined, with a separation between automated actions and those that require human involvement.
Support for operational processes.AI can be used in claims handling, collaboration with the sales network, reporting, contact center operations, and document management.
Audit and compliance.Activity logging, source traceability, and access rule enforcement help maintain control and regulatory oversight.
AI assistant that supports agents during calls, suggests responses, and automates call summaries.
Validate your project through a PoC
Start with hands-on validation, then scale AI where it creates real business value.
Order a PoC and take the first step toward rollout
Fill out the form and let us know
what kind of project you’re planning
SUPPORT
Choose data and AI projects with long-term value
Helping you select AI initiatives that make business sense and can move safely from idea to deployment.
AI where it changes outcomes
Focused on applications that improve efficiency and strengthen risk management — not experiments without business impact./span>
Security in practice
Fraud, AML, intelligent document processing, underwriting, and AI that supports employees are currently the safest and most well-justified areas for implementation.
Foundation for scaling AI
Removing barriers related to data, integration, and model transparency, so AI can be scaled safely across the organization.
GenAI that supports people and decisions
Helping you introduce generative AI in a way that supports teams in their daily work and decision-making, while maintaining control and regulatory compliance.
Developed a PoC system for flexible patient claim registration. Used generative AI to automate email…
Process and data warehouse audit
Developed over many years, the data warehouse no longer kept pace with the growing complexity…
Trusted data for better insurance decisions
Integrating data from key systems – ERP, HR, customer portal and core operational platforms –…
Unified data platform for smarter investment decisions
A Qatar-based global investment fund struggled with fragmented data sources that slowed decision-making. A modern…
Bank’s Data Culture Transformed by Data Governance Audit
Inconsistent data governance was hindering a financial institution’s efficiency and security. A thorough audit and…
PROCESS
Looking for the right place to start?
We start by identifying the insurance processes where AI can deliver the greatest impact — shortening handling times, reducing manual work, improving decision quality, or lowering the risk of errors and fraud. We analyze areas such as claims, underwriting, document handling, sales, and irregularity detection. We assess data availability and quality, regulatory conditions, and architectural constraints. The output is a prioritized list of deployment areas.
We translate objectives into specific AI applications within insurance processes. Each use case is evaluated against business value, data availability, implementation complexity, and regulatory risk. We define what data is needed, where in the process the solution will operate, and how its impact will be measured. The result is a portfolio of AI projects that can realistically be launched
Before committing to full deployment, we test the solution against the client’s historical data. We build a prototype or pilot that allows us to assess model quality, its impact on the process, and the potential business effect. At this stage, AI supports the team but does not make production decisions. This makes it possible to compare the new approach with current ways of working and evaluate whether it genuinely shortens analysis time, improves decision accuracy, or reduces manual work.
We bring structure to how AI solutions are managed within the organization. We define roles across business, data, risk, and IT, as well as standards for model documentation, explainability, and the scope of automation. We monitor data and model quality so the organization retains control over how the solution operates — keeping AI a structured and safe enabler of business processes.
We design architecture ready for integration, data ingestion, and model monitoring. We embed solutions within the client’s insurance systems and operational processes, and set up data pipelines, model training and serving environments, and quality monitoring and versioning mechanisms. This means subsequent deployments don’t require building everything from scratch.
Once a solution is live, we track its impact on business metrics and observe how it performs in day-to-day use. We translate results into further development decisions and identify the next processes suited to a similar approach. By reusing existing data pipelines, integrations, and governance frameworks, new deployments can be delivered faster and with lower risk.
WHY IT MATTERS
What you gain with our AI solutions
Fewer operational errors and greater data consistency through automated document reading, PDF analysis, and data extraction
Faster claims settlement through automatic document analysis and decision recommendations
More effective medical, financial, and behavioral risk assessment
Stronger fraud detection
Shorter time-to-market for new products and processes
Faster back-office processes without expanding headcount
Greater decision standardization and full process auditability
Quick access to product knowledge, policy wordings, and procedures — no more time-consuming information searches
An AI assistant that suggests responses during client conversations
Faster quote preparation through policy terms and wording analysis
Less administrative work and more time for clients
Higher service quality with a lower operational workload
Automatic comparison of insurer offers and policy wordings
Faster analysis of documents such as policies, clauses, and endorsements
The ability to serve more clients without growing your team
Lower risk of misinterpreting policy language through flagging of differences and key points
More time for advisory work and client relationships, less time on manual analysis
Support for client risk analysis based on financial, medical, and operational documents
BLOG
Read more about the role of Data & AI in finance
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10 min reading
From broker submission to a decision-ready case: how underwriting automation works
Between the broker’s email and the underwriting decision, even the most experienced underwriters can lose valuable time. Not because of expert assessment, but because they repeatedly have to determine whether the case is complete, current, and ready to move forward. In this article, we show how the Underwriter Portal turns the first message into a structured case prepared for analysis and recommendation — so that real underwriting can begin where it should: with risk assessment.
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9 min reading
Why does underwriting get delayed? 6 places where underwriters lose time before making a decision
If people in your organization are increasingly saying that quotes are not going out on time, deadlines are starting to slip, and the underwriting team is at capacity, it is easy to assume that the team is simply too small. But it is worth checking whether the delays are happening earlier — before the decision itself is even made. In this article, we highlight six points where the underwriting process most often loses momentum and explain why improvements should start with better workflow.
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9 min reading
From AI experiments to real processes: what MCP can offer insurers
A new phase in thinking about AI is beginning in insurance. Not as a standalone tool for experiments, but as an element meant to work alongside the processes, data, and systems used every day. This shifts the focus away from the models themselves and toward how they are embedded in the organization, and MCP is one approach to addressing the challenges of the next stage of digital transformation.
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