Launch 4x Faster with
AI-Driven Software
Development

You are the sole owner of the code and all artifacts produced during the project. Your IP rights are protected with a written contract and NDA.

Technical complexity, the number of features, and custom design are among factors that can affect the costs of AI-assisted coding. Complex business logic, database connections, and integrations reduce potential cost savings. Another vital factor is the approach to maintainability you choose:

1) “Disposable” proof of concept can save up to 75% of your budget. Such a bare-bones app is meant to validate your business or technical ideas. The app won’t see future upgrades, allowing the AI to handle more tasks without human intervention.

2) Fully-featured MVP provides 2-3x lesser savings than the previous approach. However, you get a high-quality product designed for future upgrades and maintenance.   

It can have unexpected costs in the form of API fees, data preparation, UI customization, compliance, maintenance, and updates.

We follow cybersecurity best practices with AI-powered development. Additional measures are possible to comply with industry-specific regulations, such as HIPAA. 

Encryption for data in transit and at rest to protect sensitive information.

Access control measures to limit who can access sensitive data.

Anonymization or pseudonymization of data to prevent the identification of individuals.

Compliance with relevant data protection regulations like GDPR and CCPA.

Regular security audits and vulnerability assessments to identify and mitigate potential risks.

Secure development practices and frameworks to minimize vulnerabilities.

There are two approaches to AI-driven development that determine the project’s future.

Disposable Proof of Concept (PoC) isn’t meant to become a fully functional product. The goal is to validate your ideas with minimal investments. Therefore, we use as much AI-generated code as possible. Solution Architect puts time to market above code quality. If your PoC succeeds, the team incorporates its lessons into a new, much better product. If it fails, you lose almost nothing.

Fully functional MVP is the opposite of this approach. From the very start, the team pays great attention to scalability and upgradability. AI might need more iterations to generate production-ready features. Moreover, the Solution Architect is very demanding of code quality. That’s why this approach is more expensive. However, it results in a high-quality product that’s easier to upgrade with new features.