Distribution
Algorithms that run where data lives. Federated learning, sovereign on-prem AI, integration between central servers and edge devices — research meets decentralized architectures here.
HOW WE WORK — FIVE CAPABILITIES, ONE METHOD
How we work.
The laboratory publishes research — whitepapers, open source code, the Impact Report. That research has an applied counterpart in production: five capabilities that enter companies when a real problem needs to be taken on.
We're not an agency selling a catalog of services. We're a laboratory applying our own research where it's needed. The five entries that follow describe how the work enters companies — not brochure slogans, but the practices we move with.
The capabilities below cluster into three engineering frames. Each describes a way of intervening on real systems — not a sector, not a market. Specific offerings are linked from each theme.
Orchestration
Multiple models and multiple sources of knowledge coordinated with explicit policy and traceable audit. Agentic systems and reliable RAG live here — the work is designing who does what, and keeping track.
Stabilization
Senior AI engineering applied to systems that already exist. Audits, targeted architectural redesign, technical bandwidth for AI-native founders who can't yet hire full-time.
Senior AI engineering on legacy systems
What it is
Audits, architectural redesign, stabilization of fragile AI codebases. For those who already have AI in production and see the cracks — prototypes that don't scale, runaway LLM costs, agents that run in demos and fall in production. We typically engage as senior bandwidth alongside the internal team: we read the code, identify what needs to be redone and what should be preserved, write a brief the team can carry forward after we step out. We don't rebuild everything; we redo only where the redesign pays off.
Who it's for
CTOs, Heads of Engineering, R&D directors at companies with decades of systems that work. Not looking for a showcase POC; looking for systems that enter production and stay.
Agentic systems and multi-model orchestration
What it is
Architectures where multiple models work together, each with a verifiable task, traceable audit, and explicit policy. No "single agent that does everything": orchestration of specialized agents, each with precise permissions, coordinated by readable rules. Our work spans the architectural design — which model handles what, where context lives, how handoffs flow — through to production with monitoring, rollback, and versioning of prompts and policies. The public proofs are Reasonance (visual multi-AI orchestration in an IDE) and VIBE Framework (engineering discipline for AI-generated code).
Who it's for
Organizations moving from single-LLM calls to distributed systems of coordinated intelligence, with compliance requirements or ambitions of scale.
Federated learning and sovereign on-premise AI
What it is
Models that train on the data where the data lives, without centralizing it. Sovereign on-prem AI when needed, hybrid when it makes sense. The stack covers the federated architecture design, integration with existing storage systems, fine-tuning models on proprietary datasets, and — where governance requires it — multi-tenant node orchestration with centralized audit and distributed training. The public proof is Jouelry: over 3,500 jewelers profiled without centralizing transaction data from any retailer.
Who it's for
Companies in regulated sectors — finance, manufacturing, healthcare, public administration — where data cannot leave the corporate or national perimeter, and where centralization on cloud LLMs isn't an option.
Reliable RAG on corporate knowledge
What it is
Integration of LLMs with internal knowledge — technical documentation, procedures, decision history, contracts, manuals — so the system answers precisely on specific domains. Not a chat panel pasted onto a database: a system that understands, verifies, cites sources, and declares honestly when it doesn't know. Typical work: ingestion and chunking, vector database selection, hybrid retrieval (semantic + keyword + metadata), end-to-end evaluation with test datasets, hardening against hallucinations.
Who it's for
Companies with rich but under-used knowledge bases that want them queryable from the inside (operations, support, R&D) without AI inventing plausible but wrong answers.
Senior AI bandwidth for AI-native founders
What it is
Senior technical bandwidth — on multi-model orchestration, agentic engineering, federated learning, reliable RAG — for those building an AI-native product who need expertise the labor market struggles to supply. Not generalist consulting: direct engagement on specific problems, with architecture reviews, pair-engineering on critical paths, security audits, and the establishment of practices the team can inherit.
Who it's for
Founders and CTOs of early-stage AI-native startups who can't yet hire a full-time senior engineer with deep distributed-AI experience, and are looking for an Italian-European partner aligned with their technical and identity posture.
How to begin
A conversation, not a pitch. Messages from the contact form we read directly. We respond within 3-5 days, assess whether the problem is ours, and — if there's a match — we propose a project setup. The methodological detail is in the Applied Lab.