Four directions
01 — AI agents
Autonomous agents that execute specific workflows end-to-end. Not generic chatbots. We work on agents when the workflow is repeatable, has measurable outputs, and the judgment complexity justifies AI orchestration over a deterministic pipeline.
Typical examples: document triage and classification, compliance support, lead qualification, structured-reporting automation, ongoing market intelligence.
02 — Automation and integrations
Most of the value of applied AI in a company doesn’t come from a new product, but from removing repetitive human work on the systems you already run. We connect ERPs, CRMs, document systems, accounting, email, with an AI reasoning layer that decides, classifies, drafts, and routes.
Outcome: less human time on repetitive tasks, more time on judgments that require people.
03 — Applied infrastructure
The invisible layer that makes an AI system reliable in production: retrieval-augmented generation (RAG) designed around your content, knowledge graphs, data pipelines, observability of AI decisions, ongoing evaluation, prompt and model version management.
We often build this layer as a foundation for one or more agents that follow. We sometimes build it on its own — when a company has already adopted AI tools and is finding that without solid infrastructure those tools become a bottleneck.
04 — Applied research
Feasibility studies, model evaluation, prototype-to-production paths. When the question is “is it possible?” before “what does it cost?”. This is the natural entry point for projects where technical risk is still high.
How we start
First conversation is free. We understand the problem, tell you what we think can and cannot be done, and figure out together whether it makes sense to work together. If it does, we write a short proposal with scope, output, and price. No paid discovery dressed up as consulting, no packaged quotes.
Most engagements run from a 2–4 week feasibility study or prototype to a 2–4 month production build, with optional light retainers for ongoing evolution. Specifics get fixed in writing in the proposal — no hourly rolling.
AI agents
Autonomous agents that execute specific workflows end-to-end — not generic chatbots, but systems orchestrated around a measurable business task.
2–4 mo Production build
Automation and integrations
Connectors between the systems you already run (ERP, CRM, document workflows, accounting) with an AI reasoning layer on top. Outcome: less human time on repetitive work, more on judgment.
2–6 wk Per integration
Applied infrastructure
RAG, knowledge graphs, retrieval, data pipelines, observability. The invisible layer that makes an AI system reliable in production.
1–3 mo Foundation build
Applied research
Feasibility studies, model evaluation, prototype-to-production paths. When the question is 'is it possible?' before 'what does it cost?'.
2–4 wk Feasibility study
Frequently asked
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What does Reeb Labs mean by 'production agent'?
A production agent is an AI system that performs a specific workflow reliably, repeatably, and observably — not a demo that only works on the happy path. That means structured logging, explicit fallbacks, error handling, continuous evaluation, and end-to-end technical accountability. We only build agents after we've understood the problem, prototyped the flow, and verified the system produces measurable value. -
Do you only work with large companies, or with SMBs as well?
We mostly work with European SMBs — manufacturers and professional services firms (law firms, accountancies, consultancies, agencies). Our working hypothesis is that most of the value of applied AI in Europe over the next few years will be unlocked in these companies, not in 10,000-person enterprises. -
Do you build models from scratch or use existing ones?
Almost always existing models — frontier (Anthropic, OpenAI), open-weight (Llama, Qwen, Mistral) — orchestrated around the problem. Building a foundation model from scratch is rarely the right answer. What we design from scratch is the system around the model: retrieval, evaluation, disciplined prompt engineering, integration, observability. -
What languages do you operate in?
English, Italian, Spanish. Technical documentation and code in English; client communication in their own language. -
How long does a typical engagement last?
It varies. A feasibility study or prototype: 2-4 weeks. A production system: 2-4 months. A continuous retainer for evolution and operations: open-ended. Initial scoping is free and defines where we land.