Hire a pod, not a queue of CVs.

The buyer problem

Hiring individual developers does not automatically create delivery.

Recruitment adds capacity slowly. Marketplaces add coordination work. Contractors can help, but often need more product and technical direction than the team has available. A pod solves for delivery, with ownership built in.

• You need a product shipped while recruitment continues.
• You need AI-native development habits inside the team.
• You need one accountable unit, not several disconnected freelancers.
• You need engineering judgment as well as implementation.

More than a developer

The developers you hire do more than write the code.

The name for this is a forward-deployed engineer, and it describes a job rather than a seniority band. They sit inside your team, join the conversations where product and commercial tradeoffs get decided, and then write the code that implements them. Nothing has to survive being written down and handed across, because the same person holds both ends. We match on domain as well as stack, so the developer arrives already familiar with the kind of business, customers, and constraints you work under.

• Present for the decision, rather than handed the outcome of it.
• Matched to your industry, so week one is not spent learning what your customers want.
• Already fluent in the stack you run, so there is no ramp-up tax to pay.
• Willing to tell you when the thing you asked for is not the thing you need.

Stacks

The stack is a decision, not the point.

We work across modern full-stack and AI-heavy stacks and we pick per project. But we try not to let the small questions lead. Choosing a model is a lot like choosing a programming language: you would not make a mad choice, and on some projects it counts for more than others, but nobody describes a good piece of software as the project where they picked Python over Java. What decides the outcome is the implementation around that choice.

• TypeScript, React, Node, Python, APIs, databases, and cloud deployment.
• LLM and agent systems built on frontier models, with retrieval and evaluation underneath.
• Frontend, backend, product UX, infrastructure, and maintenance.
• Fast ramp-up in an existing repository, including one nobody has documented.

Engagement shape

A small team that works like part of your team.

The pod embeds into your operating rhythm: calls, Slack, repo, roadmap, and product decisions. That is the difference between hiring abstract capacity and getting a committed engineering partner.

• One or two engineers as the default starting point.
• A defined first week of work, agreed on the discovery call.
• Direct communication and visible work in your systems.
• Option to stay through maintenance, iteration, and hiring transition.