



- Software engineering experience (preferred architecture) - Strong knowledge of automation architecture - Fair knowledge of finance
Ordelis
41% of fintech startups struggle to measure the value of AI initiatives. Deloitte's 2025 Banking Industry Outlook mentions that “Modular AI architecture and composable tools will be critical to scaling personalized financial services.”. Complexity and cost barriers of fintech are growing faster than ever, alongside the growing need for automation tools without internal team experience. Given the fast adaptation of AI agents of big financial companies and institutions, smaller firms are left behind in this race. Currently, only big corporations can afford to build performant AI agents that precisely suit their needs. Available tools are too generic or too proprietary, with rigid APIs and fragmented tooling. Most available tools require a high level of developer dependency, otherwise, the company must hire an external outsourcer. However, AI consultancy is still untrustworthy, and there is a lack of a systematic way of choosing and evaluating AI workflows.
Ordelis is a B2B SaaS provides AI dev tools which allow companies to create modularised and highly personalised automated financial processes, without developer involvement. The components are module-based, already trained, and can be shared. AI tools can now be deployed fast without an in-house AI team, yet they allow for extensions via APIs. This allows fintech startups to catch up with big players in the financial AI race, used to perform any task from financial modelling to business valuation. In this way, we offer an affordable, transparent, and versatile solution to a current worldwide problem, highlighted in various reports. Ordelis focuses on providing modular, extensible, and no-code AI components with affordability, transparency, and ease of deployment in mind. What sets us apart? Pre-trained models, API extensibility, and the ability to share models.
Help fin-tech startups create modularised financial AI workflows via component-based dev tools.