Business Challenges
Organizations across insurance, finance and the public sector face recurring challenges: manual fraud detection processes that don’t scale, decision-making that relies on incomplete or delayed data, and AI pilots that never make it past proof-of-concept because they weren’t built with production and compliance in mind from the start.
Our Approach
We start from the business problem, not the technology. Each engagement follows the same discipline: scope the use case against a measurable business outcome, validate feasibility on real data before committing to a full build, then move to production with monitoring and governance built in from day one — not bolted on afterward.
What We Deliver
- Fraud detection and anomaly detection
- Decision-support and predictive analytics
- Data engineering and dashboards
- Machine learning model development and integration
- Generative AI and LLM-based assistants
Methodology
- Use-case scoping and feasibility assessment
- Data audit and preparation
- Model development and validation
- Production integration and monitoring
- Ongoing model performance review
Why Hydatis
- Business-focused AI, not experiments for their own sake
- Proven experience in insurance and public sector
- End-to-end: data, models, integration, monitoring
- Compliance and data protection standards
- Local and cost-effective delivery
Technologies
Our AI and data engineering work draws on modern data science and machine learning tooling, cloud-based ML infrastructure, and current generative AI / LLM platforms — selected per project rather than fixed to a single vendor stack.
Example Use Case
Illustrative example, not a named client. An insurance provider needed to reduce manual review time on claims flagged as high-risk. Hydatis built a fraud-detection model combining historical claims data with behavioral signals, integrated into the existing claims workflow, with a monitoring dashboard for the risk team — reducing manual review volume while keeping a human decision-maker in the loop.
FAQ
Does Hydatis build Generative AI applications?
Yes, including LLM-based assistants and applied Generative AI use cases.
What AI use cases does Hydatis deliver?
Fraud detection, decision support, predictive analytics, and process automation.
How does Hydatis ensure AI models are reliable in production?
Models are built to be explainable, monitored after deployment, and reviewed against business and compliance requirements — not left unmonitored after go-live.
Do I need clean data before starting an AI project?
No — a data audit is the first step of our methodology, and initial data quality gaps are addressed before model development begins.