FREQUENTLY ASKED QUESTIONS

Yes — and it’s one of the most common ways we start working with a new client. Your team knows the product and the domain. You don’t need a full outsourced build — you need a senior specialist who can come in, fill a specific gap, and ship alongside your people. That might be a firmware engineer for a connected hardware push, an ML engineer for a model your team doesn’t have experience training, or an architect to help you through a platform migration. We scope it as a focused engagement with a clear deliverable, and it either wraps when the gap is filled or expands naturally into a broader relationship if the fit is right.

We can design custom hardware from the ground up, but it’s usually more cost-effective to use off-the-shelf components where possible. Most clients work with Very on custom hardware development when one or more of the following is true: minimizing per-unit cost is critical at scale, the hardware is unique enough that no existing components can replicate it, or there’s no OEM/ODM that can deliver a semi-custom design — meaning it has to be designed from scratch.

It depends on the application — and specifically whether you’re talking about a large language model or a purpose-built model. For purpose-built models (computer vision, anomaly detection, predictive maintenance), custom training is often viable and can deliver performance tuned precisely to your data and use case. For LLMs, training a custom model from scratch is almost never feasible — the cost and data requirements are prohibitive for all but the largest organizations. In practice, we find that when a high-quality commercial model exists for the task, it typically performs at or near the best-case scenario, saves significant development time, and comes with ongoing updates and performance improvements. That said, we architect every AI integration with abstraction layers so models can be swapped as the landscape evolves — you’re never locked into a single vendor or approach.

Three ways. First, we use enterprise-grade AI tooling with security controls that keep customer data protected — no client code or data is exposed to public models. Second, we’ve built internal tooling that gives every project a strong architectural foundation from day one, so our engineers focus on the highest-value parts of your problem from the start, not on scaffolding. Third, we keep an expert human in the loop on every decision that matters — but we use AI to check for security vulnerabilities, enforce code quality standards, and enhance our CI/CD pipeline. The result is faster delivery with more engineering attention on the problems that actually determine whether your product works.