Services

Applied AI & Agentic Workflows

We do not promise generic AI. We design multi-agent systems with memory, orchestration and governance.

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Capabilities

What we build

Multi-Agent Systems

Layered short/long-term memory, automatic summarisation after 10 interactions to optimise token usage, effectively infinite context window.

Voice AI Real-time

STT/TTS with intelligent interruption that distinguishes human voice from ambient noise. Target latency under 200ms.

RAG Pipeline

Semantic chunking, vector deduplication, Weaviate cursor-based pagination on datasets exceeding 10,000 documents.

Privacy-First NLU

Real-time classifier for sensitive data, selective memory deletion on user request, GDPR compliance by design.

AI for Developers

Roslyn + LLM for automatic unit test generation, agents for code documentation, enterprise AI-in-Coding workshops.

AI Document Analysis

Data room analysis, structured extraction from unstructured documents, automated due diligence.

Use cases

Who this is for

Companies with unstructured data

The most common starting point in Italian mid-market: data lives in shared folders, email threads, scanned PDFs, legacy ERP exports, and someone's Excel file. No data governance. No API. No structure.

The wrong answer is buying an AI tool. The right answer is understanding what questions the business needs to answer from that data — then building the retrieval layer around those questions.

Our approach: a 2-week discovery sprint to map what exists, where it lives, and what quality it is. Then a 4-week RAG MVP on a defined, bounded corpus. Validated with real users before any further investment. This is the entry point for organisations that have never shipped an AI system.

PDFEmail archivesERP exportsRAG MVPDiscovery sprint

Engineering & technical documentation

Civil, structural, aerospace, industrial engineering firms sit on enormous corpora: ISO and UNI norms, EN standards, project specifications, capitolati, contractor documentation, revision histories, technical manuals. A senior engineer knows this corpus by instinct. But the corpus has thousands of pages, changes constantly, and varies by client and jurisdiction.

We build RAG systems that reason over technical documentation. Engineers query in natural language and receive cited answers — norm, article number, revision. The system is version-aware: it knows which revision of a standard applies to a given project date, and will not answer with an obsolete clause.

Every response is logged: who asked, when, which document answered. This is not optional in contexts with professional liability.

ISO/UNI/EN normsTechnical specsRevision controlTraceable RAGDocument versioning

Finance & regulated data rooms

An AI agent acting on financial data is not a chatbot. It is a system making operational decisions. Internal audit, risk management, and compliance will ask: who authorised this action? Which model decided? On what data? Is the log immutable? Was the four-eyes principle enforced?

We design agent architectures with human-in-the-loop approval gates before any write action, immutable audit trails with configurable retention, RBAC with segregation between front office / risk / compliance / IT, and full LLM call logging: input, output, model version, timestamp — for every inference.

Compliance alignment: DORA (operational resilience, incident logging), MiFID II (decision traceability), AI Act (high-risk system requirements), GDPR (data minimisation, right to explanation).

Audit trailHuman-in-the-loopDORAMiFID IIRBACAI Act

FAQ

Frequently asked questions

How do you design a multi-agent system to GDPR requirements?
A multi-agent system designed to GDPR requirements needs a real-time NLU classifier to identify personal data, selective memory deletion mechanisms, and a self-hosted architecture to keep data within the enterprise perimeter. Nexus MDS Core implements these patterns in production.
What is the difference between RAG and fine-tuning for enterprise data?
RAG lets you query enterprise documents without modifying the base model, with real-time updates and source traceability. For enterprise data that evolves continuously, RAG is almost always the right choice.
Does Nexus MDS Core support open-source LLMs?
Yes. Nexus MDS Core integrates vLLM and OpenWebUI for inference of open-source models (LLaMA, Mistral, DeepSeek and others) on dedicated GPU. In a fully local deployment nothing about an answer leaves the enterprise perimeter; in a hybrid deployment only the final generation step calls an external endpoint, and that component is replaceable.
We have data everywhere and no structure — where do we actually start?
Start with a 2-week discovery sprint: inventory what data exists, where it lives, what format it is in, and what quality it is. Then define the business questions you need to answer. Only then build the retrieval layer — a bounded RAG MVP on a defined corpus, validated with real users before further investment.
Can the system reason over ISO norms and flag conflicts with our project specs?
Yes. We build RAG systems that are version-aware: they know which revision of a standard applies to a given project date and will not answer with an obsolete clause. Engineers query in natural language and receive cited answers — norm, article number, revision. Every response is logged for professional liability traceability.
How do you handle agent governance for internal audit and four-eyes requirements?
We design agent architectures with human-in-the-loop approval gates before any write action, immutable audit trails with configurable retention, RBAC with segregation between front office, risk, compliance, and IT, and full LLM call logging — input, output, model version, timestamp — for every inference. Aligned with DORA, MiFID II, and AI Act requirements.

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AI infrastructure to build, a legacy system to modernise, or an ERP to connect to the future? Get in touch.

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