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Corvx works across AI development, payments reconciliation, and DevOps and cloud.
We work with financial services, healthcare, logistics and supply chain, e-commerce, education, and enterprise software teams. The first conversation is about the workflow, constraints, and rules in your setting.
Send us the outline through the contact page. We will use the first conversation to understand the work, the current system, and the next decision. There is no commitment for that call.
We keep the scope and owner visible, bring architecture and delivery into the same conversation, and show the evidence behind the decisions. Our two hubs also give teams useful overlap without hiding the work in a handoff.
We build automation around manual document intake, cross-system handoffs, and repetitive data entry. Automated extraction is paired with deterministic validation and human review checkpoints so your team stays in control. Read about AI & automation.
We work with AWS, Azure, Google Cloud, and hybrid environments. The toolkit can include Jenkins, GitLab CI, GitHub Actions, Docker, Kubernetes, Terraform, and observability tools, chosen around your current setup. Read about DevOps services.
Yes. We can work above the models you already use. The surrounding control, memory, data, and logging layers can change without forcing a model replacement.
We start with the work and the current system, agree on the architecture and measures, build in short releases, test the important paths, and then hand over or continue to operate the result with you.
Timing depends on the scope, data, and number of systems involved. A focused MVP may take 4 to 8 weeks. Larger applications or governance work take longer. We give a more useful estimate after the first technical review.
We schedule live decisions and reviews around your working hours, then keep the rest of the work documented in the tools your team already uses. The engagement has a named delivery owner.
The agreement covers confidentiality, IP, and data handling. In the build, we review identity and access, logging, encryption, secure coding, and the controls needed for the systems in scope. AI workflows get the same access review as other services.
Yes. We start with the documents and systems that matter to the first use case, then add extraction, validation, and ownership as we go. The data does not need to be perfect before the first review.
Support can include monitoring, bug fixes, security updates, feature work, documentation, and knowledge transfer. We agree on the response model and the ownership boundary before launch.