Can we understand it?
Assess code structure, dependencies, architecture, data flow, documentation, and whether the implementation can be maintained.
AI-assisted software
Keep the insight and momentum from your experiment while adding the engineering required for real users, real data, and dependable operations.
From useful demo to business system
An AI coding tool can quickly help a team test an idea. Before the application carries customer information, supports operations, or becomes part of revenue delivery, it needs a different level of examination.
Assess code structure, dependencies, architecture, data flow, documentation, and whether the implementation can be maintained.
Add deterministic validation, automated testing, error handling, audit trails, and human review for important decisions.
Review authentication, authorization, secrets, sensitive information, dependency risk, and the boundaries between customers and roles.
Design APIs, database contracts, file ingestion, events, and adapters for the business systems already in use.
Prepare repeatable deployment, environment configuration, logs, health checks, backup, recovery, and support procedures.
Preserve working ideas, replace fragile sections selectively, and avoid a costly rewrite unless the evidence supports one.
A measured pathway
Review the application, data, dependencies, security, and intended business role.
Separate launch blockers from improvements that can follow after value is proven.
Implement the architecture, testing, controls, integration, and operations needed now.
Deploy, monitor, document, and continue improving the application with its users.
A constructive handoff
The prototype contains valuable learning about the problem, users, terminology, and desired workflow. CoreData treats that work as evidence and uses engineering judgment to decide what should be retained, improved, isolated, or replaced.

Bring the prototype
Share what the application does today, who will use it, and what success would look like.