AI agents for complex operations.

Complex operations generate large volumes of documents, forms, reports, and records. Most of this is still processed manually: operators transcribe purchase orders into ERP systems, logistics teams copy container numbers from PDFs, site managers extract figures from inspection reports by hand.

Mirage builds the systems that eliminate this work. Our AI agents read operational documents, extract structured data, trigger downstream workflows, and integrate directly with the systems companies already use: ERP platforms, logistics tools, planning software, field databases, and communication layers.

In practice, this includes ERP order entry automation, customs and freight document processing, construction site reporting workflows, maintenance and inspection data extraction, operational email routing, and field document intelligence across multi-language, multi-format document environments.

We work on document processing, operational coordination, ERP workflow automation, data extraction, field intelligence, and decision routing. Our agents handle the repetitive operational work that consumes teams, freeing them to focus on decisions that require judgment.

We are not a software vendor. We are an engineering team that deploys, configures, and operates these systems inside our clients' environments. We stay until the results are measurable.

Mirage Metrics was co-founded by two Centrale Paris alumni and operates as a forward-deployed engineering company focused on operational AI systems.

A large part of the engineering team comes from Centrale Paris and EPFL. The company operates between Paris, Barcelona and Casablanca, and deploys systems internationally depending on operational requirements.

Mirage combines software engineering, AI systems, operational workflow design and field deployment. Our engineers work directly inside client environments to understand workflows, operational bottlenecks, field constraints and existing systems before deploying production-grade AI agents.

Mirage deploys in environments where document volumes are high, coordination work is largely manual, and the operational cost of errors is measurable.

The common thread across our deployments is not a specific industry. It is an operational profile: large volumes of documents and forms, complex workflows spread across multiple systems, and teams that spend significant time on tasks that should not require human attention.

We work with companies in logistics and freight forwarding, construction, mining, manufacturing, infrastructure services, and industrial maintenance. We also build internal automation systems for companies running high-volume operational coordination, regardless of the sector label. If the work involves documents, multi-system workflows, and repetitive coordination at scale, it is within scope.

Mirage operates on a deployment-first model. Before writing a line of production code, our engineers spend time on-site inside the client's operational environment. We observe how documents flow through the organisation, where coordination breaks down, which systems are already in place, and what field constraints determine what is actually buildable.

This operational immersion is not optional. It is how we understand the real problem before building the system. Most failures in industrial AI deployment come from systems designed without direct exposure to the operational environment they are meant to run in. We do not design from the outside.

Most of our deployments reach production in under four weeks. They run inside existing workflows with no migration, no parallel software stack, and no disruption to the teams already using the tools they know. Our agents integrate with SAP, Oracle, Microsoft Dynamics, and any ERP, TMS, or WMS through standard APIs and file-based connections. When a client's systems expose no API, we build the connector. The integration layer is always part of the deployment.

We do not hand over a system and leave. We operate it, monitor it, and iterate on it with real production data. The deployment is considered complete when the operational results are measurable and the system runs without supervision.

Beyond individual deployments, the longer-term objective is to make operational data usable at scale. Industrial companies generate this data continuously, but most of it sits in documents rather than databases: PDFs, spreadsheets, scans, printed forms, and field reports, often in multiple languages, from dozens of source systems, in formats that were never designed to be machine-readable.

Mirage builds the processing and integration layers that convert this data into structured inputs for downstream systems. The work involves AI agents, document pipelines, and system connectors that handle documents from any source and in any format, and route the output into the right place in the right system.

This requires familiarity with the operational realities of each sector, and systems reliable enough to run continuously, without supervision, in live environments. The deployment model is not just an operational choice. It is how you learn what those environments actually look like.