Voight Labs is a boutique technology consultancy building production machine learning, web platforms, and hardware-software systems. We work with companies that need real engineering — not slide decks.
Predictive models, forecasting systems, and custom ML pipelines — designed to ship and stay shipped. Training, evaluation, deployment, and the MLOps to keep them reliable in production.
Operational dashboards, internal tools, and client-facing platforms. Type-safe, well-tested, and built for teams that will maintain them long after we hand them over.
Embedded systems and the software stack around them — firmware, edge inference, device-to-cloud telemetry, and the dashboards that make the data useful.
Problems that don't fit a category. We assemble the right team — from our core and our network — and build what's needed. Often the work no one else wants to take on.
Tooling we reach for
We run lean on purpose. The result is faster decisions, less overhead, and a team genuinely accountable for what it ships.
A senior team with experience at leading Latin American technology companies — fintech, ML platforms, marketplaces, and hardware. For specialized work, we bring in vetted experts from our network, so capacity scales without diluting quality.
No junior offshore tier, no project managers writing tickets they don't understand. The people on the call are the people writing the code.
We measure ourselves on systems running in production, not deliverables produced. We work in short cycles, push to production early, and iterate against real signal.
If a problem is the wrong shape for us — or for outside engineering at all — we'll say so. We'd rather pass on work than take on a project we can't do well.
A short, paid discovery. We interrogate the problem, look at your data and systems, and define what success measurably means. You get an architecture proposal and a plan — useful even if we never write a line of code together.
A small senior team builds in short cycles. You see working software every week, not a status report — and you talk directly to the engineers writing it.
We push to production as early as the problem allows and iterate against real signal — real users, real data, real hardware. Monitoring and alerting ship with the system, not after it.
Documentation, runbooks, and pairing sessions with your team. The goal is that you own the system outright — we're happy to stay on for support, but you shouldn't need us to.
Active projects, anonymized. Specifics available under NDA.
Built predictive models and real-time analytics dashboards for an international trading firm. End-to-end ownership: feature engineering, model training and evaluation, production inference, and the operator-facing tooling on top.
Currently developing computer vision systems and the surrounding software for the automotive and residential security sectors — from edge inference on constrained hardware to the cloud services and operator interfaces around them.
Anything else, ask on the intro call — you'll get a straight answer.
Production machine learning, data-heavy web platforms, and systems where hardware meets software — plus custom engineering that doesn't fit a category. We're at our best when the problem is technically hard and the deliverable is a running system, not a report.
With a short, paid discovery sprint — usually one to two weeks. We dig into the problem, your data, and your constraints, then come back with an architecture proposal, a scoped plan, and an honest read on whether we're the right team. If we're not, we'll say so.
The senior engineers you meet on the first call. We don't run a junior offshore tier, and there's no handoff to a delivery team — the people who scope the work are the people who build and ship it. For specialized needs we bring in vetted experts from our network.
Yes. Many engagements are embedded: we join your repositories, your standups, and your on-call reality. We adapt to your tooling and conventions rather than imposing ours, and we leave the codebase easier to work in than we found it.
Discovery is fixed-price. After that, most engagements run as milestone-based fixed scopes or a monthly team rate, depending on how well-defined the problem is. Either way you'll know the cost before we start — no surprise invoices.
The core team is distributed across Latin America and works remotely with clients worldwide, in English and Spanish. Our time zones overlap the full US working day, so collaboration is synchronous, not ticket-driven.
We take on a small number of engagements at a time. Tell us about the problem — we'll be straightforward about whether we're the right team to solve it.