datarockets

As a product engineering agency, datarockets describes its method as agentic engineering. In its account, AI agents carry out most of the design, architecture, coding and testing work, while people set the product strategy and check quality. The company puts the split at roughly 20 percent human thinking and 80 percent automation, and it describes itself as AI-native. Its clients are mainly startups and product companies, including some that do not have an in-house development team.

Product discovery before any code

Discovery helps a client decide what to build, who it is for and the fastest route to launch. The firm's FAQ says it translates business requirements into technical specifications and user stories, then prepares an estimate of time and cost from them. It also advises on what belongs in an MVP and can help validate an idea with a prototype or clickable wireframes before the idea is presented to investors.

MVPs and dedicated teams for apps

The company says it can ship a working product in weeks, covering web, mobile and cross-platform apps. Its model is dedicated teams of developers who work through the client's requirements iteratively, with no long-term commitment required from the client. When a deadline is tight, it says it allocates two or more developers who already have the technical knowledge the project needs.

Agentic AI engineering for products

The AI engineering service builds the layer a product runs on: agents, pipelines, a harness and the infrastructure that keeps them reliable. Purpose-built agents handle multi-step tasks on their own, AI features can be added to existing products and workflows, and production systems are given evaluation, monitoring and context engineering.

AI audits before a project starts

Its AI strategy and audit work looks at how a team works today and identifies where AI would save real time and money, and where it would not help. The company says the assessment is candid about those limits, and then it helps the team reach the changes it recommends.

Modernizing legacy systems

Modernization work reaches older systems that need updating, repetitive workflows that can be automated and internal tools that teams lack. The company also supplies extra engineers for a team that is missing in-house expertise, and says it works alongside the client to meet deadlines and expectations.

How the work is run

The FAQ describes real-time task boards, and says the firm does not use private chats hidden from the client. All project discussion happens in public channels the client can see. It also says it sets up continuous integration and delivery, writes tests, peer-reviews code and leaves technical and project documentation behind, and that it uses tools to cut routine work so engineers can focus on decisions.

Confidentiality and recent projects

The company says it signs a non-disclosure agreement before project talks and follows internal security rules to protect client information. Its recent work includes Quartz, a native email client for Mac that runs its AI on the device itself, a Product Hunt launch for the Yapz.app agent, and the core renewal experience built for SpendHound.