AI Orchestrator

Agentwolf orchestrates models, data and actions. And puts them into production.

The conversational, agentic engine of Statwolf: it orchestrates LLMs, RAG, models and functions across your systems, with human-in-the-loop where it matters. Transversal to the three suites.

Simplify. Understand. Act.
AI Orchestrator
Inputtesto · voce · img
Orchestration engineLLM · RAG · fn
Azioni sui sistemiCRM · ERP · MES
Non risponde. Esegue.

Why it’s different

Other chatbots
Agentwolf
Standalone, isolated
Integrated into real processes
Q&A only
Action-oriented — it executes
Locked to model and cloud
Model- and deployment-agnostic
Data leaves your perimeter
Data can stay in-house (local/on-prem)
Agentwolf in five points
It doesn’t reply. It acts.
It opens tickets, updates the CRM, queries systems, starts processes. Replying is just the final step.
Your data stays yours.
It also runs on local LLMs, on-premise or air-gapped: in regulated environments the data never leaves your perimeter.
No surprises on the bill.
Configurable spending caps on model costs: you set the ceiling in advance.
One engine, many use cases.
The same engine behind customer service, ticketing, internal assistants and digital channels: the second use case costs a fraction of the first.
Determinism where needed, AI where it counts.
A price, a calculation, a policy is not left to a generative model: a function handles it and always gives the same answer. AI does the rest.
Who it speaks to
Customer ServiceOperations / plantsField ServiceIT / Digital TransformationMarketing & digital channels
How it works

At the core is an orchestration engine. It takes a request and decides how to handle it, piece by piece: a deterministic function where the answer must be exact, an LLM where it needs to understand and write, retrieval over your documentation (RAG) when the answer lives there, a predictive model when it needs an estimate. Where you decide, it asks a person before acting. It plugs into your systems — ticketing, ERP, IoT, legacy applications — and it isn’t tied to one model or one cloud: change LLM tomorrow and the workflows stay. And the knowledge it answers on is governed: content gets in once it’s been validated.

Cloud, hybrid, on-premise or air-gapped deployment.

Integration

One engine, inside the platform and the three suites

Agentwolf does not live on its own: it accesses Statwolf Platform data (via MCP) and uses the suites’ models. The same engine changes job depending on the context.

With Customer Intelligence

It reads the Single Customer View and uses the CDP models (propensity, next best action, churn): conversations know who the customer is and what to offer.

With Service & After-Sales Intelligence

It is the Field Assistant on manuals and documentation and works on ticketing: sentiment, prioritisation and assignment to the right technician.

With Manufacturing Intelligence

An assistant on factory alarms and documentation, with natural-language querying of production data.

Use case — Consumer service

CDP + Agentwolf: B2C after-sales that retains and sells

On the website or in the app, Agentwolf on top of the CDP turns support from a cost centre into a retention and revenue lever: it resolves, it knows the customer, and it catches the right moment for the next move.

Returns & exchanges

Handles the return request, checks eligibility against the order and offers an exchange or store credit instead of a refund, then triggers logistics.

Where is my order

Answers shipping and delivery questions on real order data: the number-one support volume in e-commerce, deflected automatically.

Warranty & repair

Product registration, warranty status, troubleshooting on the manuals and booking of repair or replacement.

Proactive service

The CDP catches the signal (repurchase window, expiring subscription, end-of-life product) and Agentwolf reaches out to the customer first.

Retention & upsell

The CDP next best action enters the support conversation: accessory, loyalty, trade-in or a targeted offer to stop a cancellation.

Triage & escalation

Resolves the simple cases and escalates the critical one or the high-value customer to a person, already with full context from the CDP.

Typical impact
Customer Service
−35%handling time
+25%first contact resolution
Operations
−20%downtime
−30%MTTR
+15%OEE
Web / digital
×2engagement
+18%conversion
Real cases, anonymised

Services

A digital secretary that doesn’t just answer: it performs actions on the systems.

Industrial machinery

Smart ticketing and image-based alarm analysis to speed up diagnosis.

Regulated environment

Local LLMs only: data never leaves the perimeter.

Agentwolf is the GenAI/LLM engine of the Statwolf ecosystem and integrates natively with the CDP (data access via MCP).
Agentwolf · AI Orchestrator

Got an AI demo that never reaches production?

Bring us the use case that stalled. We’ll show you what it takes to orchestrate it: tools, guardrails, and determinism where it matters.

  • Local LLMs too: your data never leaves your building
  • Spend caps, so there are no surprises on the bill
  • One engine, many use cases
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