Common questions

Full-stack web applications, SaaS products, internal tools, AI automation, semantic search, and backend/cloud systems. The common thread is production-ready software built around a real business problem.

Cognivox Labs focuses on marketplaces and digital platforms, finance/analytics/ESG, sports and fitness products, agriculture and operational businesses, and SaaS or startup products.

Both. That includes building a focused first version for a startup and improving or extending systems that are already in production for established teams.

It depends on scope, integrations, and data. Most engagements start with a short discovery step that produces a realistic timeline before any build commitment. See how we work for the engagement models.

Understand the business problem, agree a practical scope, then deliver in reviewable slices with regular check-ins. Each release replaces a real piece of manual work rather than shipping features in isolation.

A short regular check-in (typically weekly) is enough for most projects, plus a few decision points. You work directly with the engineer building the system, so communication stays direct.

Yes. Cognivox Labs can integrate with an existing technical team or take over and continue an existing codebase where that is the practical choice.

Typically Next.js, React, and TypeScript on the frontend; Laravel, FastAPI, and Node.js on the backend; PostgreSQL/Redis for data; Docker and CI/CD for deployment; and RAG/LLM workflows for AI. The stack is chosen to fit the problem, not the other way around.

Yes. Ongoing maintenance, improvements, and support are available as a continuous engagement for systems that need it after launch.

Yes. You own the source code and intellectual property created for your project, with documentation to support handoff.

Cognivox Labs builds with a GDPR-aware and EU AI Act-aware approach and privacy-by-design thinking. This is an engineering approach, not a certification — see the compliance page for detail.

A security-minded approach: data minimisation, sensible access control, secure service boundaries, and documented data handling. Defaults are chosen to be safe and auditable rather than clever.

Yes. Confidentiality agreements are fine and expected for most discovery conversations.

There is no fixed public price list. Scope depends on complexity, integrations, data, and timeline, and is defined together after a short discovery conversation. The engagement page explains the models.

No. Most engagements start with a discovery sprint or pilot so both sides can decide based on real work before any larger commitment.

Scope changes are discussed, estimated, and agreed before implementation, with transparent communication about the effect on timeline.