AI Engineering

    AI Engineers for Your Team — Building Production Features, Not Demos

    LLM integration, retrieval systems, agent workflows. We embed senior AI engineers into your team or build the AI layer of your product directly. EU timezone, no hype, real evals.

    Senior
    AI engineers, pre-vetted
    CET
    EU timezone, real-time collab
    Evals first
    We measure, then ship
    Discuss your AI projectSee team extension model

    Three ways we plug AI into your business

    Start where the ROI is clearest. We've seen plenty of fancy demos die in production — we don't ship those.

    LLM features in your app

    Best when: you have a SaaS or internal tool, want to add chat / summarize / classify / generate

    Add LLM-powered features to existing products. Streaming UIs, prompt versioning, A/B tests against deterministic baselines, fallback paths when the model is wrong.

    • Anthropic Claude + OpenAI SDK with provider fallback
    • Streaming with cancellation, retries, rate-limit handling
    • Prompt versioning + evals against golden datasets

    AI agents & workflows

    Best when: you have repetitive multi-step work that humans do today

    Build agents that complete real tasks — research, classification, drafting, multi-step automations. Bounded scopes, observability, human-in-the-loop where needed.

    • Tool-use loops with strong evals + tracing
    • Human approval gates for high-stakes actions
    • Cost + latency budgets enforced at runtime

    Retrieval (RAG) over your data

    Best when: you have a body of docs / tickets / wikis / contracts your team searches daily

    Searchable knowledge over your data. Hybrid retrieval (semantic + keyword), citations, freshness controls, access controls aligned to your existing permissions.

    • pgvector or Qdrant for vector store — your call
    • Citation tracking — every answer links back to source
    • Reranking + filters by metadata (date, owner, ACL)

    What we actually do well

    The parts that separate working AI from impressive demos.

    Prompt engineering

    Versioned prompts, structured outputs (JSON Schema, tool calls), guardrails for prompt injection. We treat prompts as code — reviewed, tested, deployed.

    Evals & monitoring

    Golden datasets, LLM-as-judge with calibrated rubrics, regression detection on every model/prompt change. Production logs sampled into eval sets.

    Vector DBs & embeddings

    pgvector, Qdrant, Weaviate — each has trade-offs. We pick based on scale + your existing infra, not vendor preference. Hybrid search (BM25 + cosine).

    Agent orchestration

    LangGraph, custom state machines, or simple tool-use loops — whichever fits the problem. Distributed tracing across agent steps. Resumable on failure.

    Cost optimization

    Model routing (cheap for easy queries, smart for hard ones), prompt caching, batch APIs, response caching. Typical 40–70% cost reduction without quality loss.

    Privacy & on-prem

    On-prem deployments for sensitive data (open-source LLMs via vLLM / Ollama), zero-retention policies on commercial APIs, EU data residency, GDPR-aligned.

    AI tech we ship with

    Tools we've put through production, not demo decks.

    Claude (Anthropic)

    LLM

    GPT (OpenAI)

    LLM

    LangGraph

    Orchestration

    pgvector / Qdrant

    Vector DB

    Python

    Language

    TypeScript

    Language

    Why not just hire one of the AI hype-shops?

    Most AI work fails in production — not because the model is bad, but because the engineering around it is brittle. We come from a software engineering background first (ERP, POS, mobile), so we treat AI features like any other production system: evals, monitoring, rollback paths, cost controls. Need dedicated AI engineers on your team instead of a one-shot project? Our Team Extension model covers that too.

    See Team Extension model

    How we build AI features

    Discovery first. No demos shipped to production.

    Discovery & eval plan

    What's the task, what's the user impact, what counts as 'good'? Define the eval before the model. If we can't measure it, we won't build it.

    Rapid prototype

    Smallest end-to-end slice that hits real data + real users. Throwaway code if needed — speed to learning matters more than reusable scaffolding.

    Productionize

    Once evals pass, harden it. Rate limiting, observability, fallbacks, cost budgets, security review, deployment pipeline.

    Operate & improve

    Monitor evals in production. Detect drift. Iterate on prompts/models. We stay on the team after launch — AI features get better with feedback, not less.

    Have an AI feature in mind?

    Tell us the problem you're solving — not the model you want to use. We come back with an eval plan, an honest take on whether AI is the right fit, and a rough scope. Usually within 48 hours.

    Start an AI project

    No hype. No demo videos. Evals or it didn't happen.