AI agents & automation

They don't just answer. They act.

Operational co-pilots that read e-mails, orders and documents, query company systems and complete repetitive tasks — with controlled tools, role-based permissions and a human in the loop where it matters. Agents designed to cut time and errors, not to impress in a demo.

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In practice

Tools, not magic

The agent uses explicit tools — document search, ERP queries, draft sending, ticket creation — each with permissions, limits and logs. No generic access.

Orchestration

Multi-step flows with states, retries, timeouts and fallback to a person: built with LangChain and LangGraph, observable step by step.

Human-in-the-loop

Confidence thresholds and approvals: the agent proposes, the person decides where it matters. Every action is traced and reversible.

Measurement

Time saved, errors avoided, escalation rate: KPIs are defined before starting and read on dashboards, not told as stories.

Stack and technologies

LangChain · LangGraph
Open-weight models
On-premise LLMs (Triton)
Python
ERP · CRM · ticketing APIs
Docker

Project

Our approach

From retrieval to action: the step after RAG

Co-pilots start from what we already run in production — the local RAG with LangChain on invoices and technical documentation — and add controlled tools: field extraction, cross-checks with the ERP, draft replies and requests ready for approval. Every action is logged; every critical decision goes through a person.

Base: local RAG · LangChain
Tools: documents · ERP · tickets
Control: role permissions · approvals
Deployment: on-premise or cloud

How we work

Four steps, always the same

01
Map of repetitive tasks and systems involved
02
Definition of tools, permissions and approval points
03
Prototype on a real flow, with time and error metrics
04
Gradual extension, monitoring, rule review

Frequently asked questions

Can an agent do damage?

Only if you let it. We design tools with minimal permissions, reversible actions, explicit limits and human approvals for everything critical; every step stays in the log.

How is it different from a chatbot?

A chatbot answers; an agent completes a task using tools: it reads an order, checks stock in the ERP, prepares the confirmation and submits it for sending.

Does it work on-premise?

Yes: the same agents can use local LLMs served with NVIDIA Triton, so data and actions stay inside your network.

Related capabilities

All AI capabilities →

RAG & LLM

RAG and LLMs on company data

Answers with sources, from your documents: invoices, manuals, contracts — locally too.

On-premise generative AI

LLMs and vision served on your GPUs with NVIDIA Triton: data never leaves.

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