Questions about working together

Cognitrace fits when a proprietary AI capability, ML system or platform needs to be built or improved — and model quality, software architecture, data, integrations and cloud operations belong together. Typically not: standalone tool rollouts, no-code automation, training programmes or staff augmentation.

Both, with a clear bias towards implementation. Architecture and technical leadership are also available as a standalone assignment when an existing team owns delivery.

Yes. We start with a technical review of architecture, quality, operability and the path forward. We then take on the implementation or work with the existing team in the areas that need support.

We integrate directly with existing teams rather than building a parallel stack. Responsibility is divided clearly across architecture, technical decisions and the areas where we implement. Code, documentation and knowledge transfer to the internal team.

Through evals, production telemetry and cost attribution built into the system: test sets, metrics and monitoring for output quality, failure behaviour, latency, cost and tool use. The exact measures depend on the product and its risk profile.

The client does. Code, infrastructure, models and documentation transfer to the internal team and can be operated without Cognitrace.

As part of the architecture: through data flows, roles and permissions, deletion concepts, audit trails and suitable operating models. GDPR and the EU AI Act are assessed per use case, with the necessary evidence produced throughout delivery.