AI Staff Augmentation & Dedicated Teams

AI and machine learning engineers who have shipped LLM features to production and know where the failure modes are — retrieval quality, evaluation harnesses, streaming that hangs, cost that scales the wrong way. On the TypeScript side that means Mastra, the Vercel AI SDK, and Postgres with pgvector; on the .NET side, Semantic Kernel and Kernel Memory. Full-stack and backend engineers across .NET, Node, Python, Java, and PHP. Mobile engineers for iOS and Android. Data engineers for pipelines, warehousing, and the integration work that makes analytics trustworthy. DevOps and cloud engineers on AWS, Azure, and GCP.

A common pattern: a capable in-house team with no one who has taken a language model to production. One or two experienced engineers alongside them for a couple of quarters changes that permanently — the knowledge stays with your team rather than leaving with a contractor.

Augmentation means individuals joining your existing team and reporting into your management. You direct the work; we handle contracts, payroll, equipment, and cover. This suits teams that know exactly what they are building and simply lack hands or a specific skill.

A dedicated team means we assemble a self-contained unit — engineers, a technical lead, QA where it is needed — that owns a product area end to end. This suits companies without the internal management bandwidth to direct individuals daily, or those spinning up a new product line alongside existing commitments.

Both run on monthly rolling terms. Roadmaps change, and a contract that punishes you for adapting to that is a contract designed for the vendor.

Riga sits in Eastern European Time — a full overlapping working day with every EU market, a couple of hours from London, and a real morning overlap with the US East Coast. Meetings happen at reasonable hours for everyone, which sounds minor and is the single biggest predictor of whether a distributed team stays coherent.

Latvia is in the EU, so GDPR, contract law, and IP protection are the same framework your legal team already works in — no data-transfer mechanism to negotiate. English is the working language across our network, and Latvia's engineering culture is closer to Northern European directness than to the report-what-they-want-to-hear dynamic that makes some outsourcing relationships fail slowly and quietly.

We will not send you a CV for someone we have not worked with. We will not staff a role with a junior priced as a senior. And we will not take an engagement when the honest answer is that our network does not currently have the right person free — being told no in week one is cheaper for you than finding out in month three.

That constraint is why the model works. In twenty years and 100+ projects we have never abandoned a client, and our clients' own descriptions of the engineers — fast, precise, and easy to work alongside other teams with — are the reason the network keeps being reused.