Arvantti for Manufacturing Businesses

Arvantti is Sequoia Space's intelligence solutions brand. Today I am going to show you, in a simple way, what pieces the ecosystem has and how to put together a solution tailored to each company.

Everything we are going to see is part of Sequoia Space. Sequoia is the company; Arvantti is its applied intelligence brand, with two faces: Arvantti Labs, which is the product that is installed in the company, and Arvantti Training Center, the business learning platform that trains teams to take advantage of it.

Most companies are at stage one: using AI as a personal assistant. The real value appears in stage three, when the AI ​​understands the business and operates within its processes, with rules and permissions.

This is the complete tour of the presentation. Each topic has a link that jumps to its slide.

Arvantti is not a single application or an AI: it is a set of pieces that come together. The base is the Harness, which governs everything. Added to this are the business brain for AI (visual search, data lake, knowledge graphs and RAG), the harness and agentic rules with governance and traceability, and the digital twins: Arvantti-DigitalTwin® co-simulates the plant and Arvantti-Fractal® analyzes predictive events. Arvantti Harness, Arvantti-DigitalTwin and Arvantti-Fractal are registered trademarks. The Training Center accompanies the teams in learning.

Arvantti Harness is a registered trademark. It is a harness of harnesses: it wraps around the different AIs (Claude, Codex, Gemini, Microsoft Copilot or local models) and gives them the same structure. Conversation mode: the friendly front door. Business brain: a graph with knowledge, pending decisions and tasks. Agent flows: sequences of agents that are configured within the harness, each with its own context, without contamination. Guided projects: SDD methodology to plan and execute. Connectors: integration with company systems without giving passwords to AI. Guardians: security rules and permissions. Isolated environment: terminals and sandbox for programmers. And a record of everything that happens.

This capability was born in a real project: finding in which video, and at what time, a scene appears within thousands of videos saved in Google Drive, and showing where else it could appear. What we bring to Arvantti is that visual and agentic capability of enterprise RAG: it works for media files, but also for security cameras, plant cameras, inspection photos, plans and scanned documents. Agents only read; They never modify the original files.

This is how an Arvantti digital twin operates. The real plant has its equipment and sensors; the PLC controls them and reports their signals. These signals are read securely, with OPC UA, without intervening in the plant, and feed Arvantti-DigitalTwin®, our digital twin and trademark: a 3D replica that moves live and co-simulates the plant in parallel. Arvantti-Fractal®, also a registered trademark, works on the twin, which analyzes predictive events, compares alternatives and recommends adjustments. The operator decides whether to accept them, and only then is the change applied in the PLC. Today the PLC is virtualized in the demonstration plants; the same schematic connects to a real PLC.

These are real views of the platform: four reference floors built with the Arvantti equipment library, and the aqueduct control room, which shows live what the virtual PLC reports. Reference plant data are demonstration estimates, not from an actual installation.

These are reference figures of use cases for this type of solutions, not promises. The real impact of each company is estimated in the initial diagnosis.

Five services that are combined according to need: knowledge, governance with Arvantti Harness, integration with company systems and operation with digital twins: Arvantti-DigitalTwin® and Arvantti-Fractal®; and training with Arvantti Training Center, with routes for managers, content and engineers, plus the Private AI Clinic.

The implementation goes in stages: diagnosis in the first month, data preparation in months one and two, development in months two and three, validation in months three and four, deployment in month four, and from there continuous scaling.

We close with an open question for the client: what part of their operation do they want to make smarter. From that answer we propose the modules, AI and personalization.