AI infrastructure
From raw LLM to governed service
Understanding the paradigm shift from AI products to AI-based service infrastructure. Software is no longer just a tool: it delivers outcomes.
AI infrastructure
Understanding the paradigm shift from AI products to AI-based service infrastructure. Software is no longer just a tool: it delivers outcomes.
The substrate — the context engine
Capture, index across multiple memories, then retrieve exactly what's needed — from each one — to build an agent's context. Live.
Retrieval — multi-RAG & consensus
Vector, graph, full-text, SQL: every memory is queried in parallel, then the results are crossed. What several methods confirm rises; the noise drops. Every dial is in your hands — or automated for performance.
Consensus across methods is what makes precision. The threshold tunes vector recall — raise it to cut noise, lower it to widen.
The control surface
You stay in control of every AI decision — which model, which knowledge, which database, how it's retrieved, monitored, versioned. Pick a node's model and watch sovereignty react.
Knowledge stays in the sovereign substrate; only the processing location changes.
Point sensitive steps at a local model — data never leaves your perimeter — and reserve hosted frontier models for the steps that deserve them, in the same flow.
Describe the flow in plain language; Syrtis turns it into a buildable Scenario blueprint, constrained to the real vocabulary and the 31 real nodes. An AI service in hours, not months.
Every slot and the API are versioned; the Trace node consolidates a run's full history. Quality, drift, cost, CO₂ — observable, audit-ready.
How it's built
Three applications, a message bus, a multi-store data layer, and a deterministic DAG executor. One vocabulary, from the UI down to the tables.
The graph-first scenario editor, the test chat/console, the request waterfalls and the supervision dashboards.
The single source of truth. Persists every entity, enforces access rights, records requests and bridges to Core over the bus.
The brain. Consumes jobs, runs the node DAG, drives the LLM interactions and rendering — and emits the Messages.
The library — 31 atomic nodes
Syrtis captures heterogeneous sources, orchestrates specialized agents, and delivers results into the channels where your teams already work.
Interface
Data sources
Distribution
Interface
Data sources
Distribution
Large language models (LLMs) are not products in themselves — they are closer to a commodity, like electricity.
Just as electricity only becomes useful once it is combined with machines that turn it into tangible products (light, washing, computation), LLMs must be embedded in structured applications to deliver real services. This is the dawn of a new era: service-as-software.
| System | Approach | Limitations | Syrtis differentiation |
|---|---|---|---|
| ChatGPT | Universal black-box application | Limited customization, no traceability | Composable models + full audit trail |
| Claude | General-purpose AI assistant | Black box, compliance risks, opaque tuning | Domain-specific governance and compliance |
| Open-source LLMs | Community frameworks | Heavy engineering effort, fragmented tooling | Open models inside a governed control plane |
| Syrtis | Orchestration & control plane | None of the black-box constraints | Modularity, prebuilt modules, enterprise oversight |
Syrtis was born from a simple but demanding question: can we design an autonomous system able to teach, guide and assess learners in real time?
Education is one of the hardest human processes to model. It demands:
The lessons drawn from education were transformative:
Our goal was not to build yet another chatbot. We wanted a virtual teacher able to guide thousands of learners at once, while upholding the same principles as a human teacher — fairness, explainability and adaptability. What started as a way to solve one of AI's hardest problems (autonomous teaching) evolved into an enterprise AI control plane.
Enterprise AI has reached a tipping point. Budgets are shifting from experimentation to deployment, yet most organizations remain stuck in proof-of-concept cycles. Meanwhile, new regulations such as the EU AI Act are raising the stakes around compliance, transparency and sovereignty.
Syrtis reduces the complexity of enterprise AI deployment to a production line for reliable, auditable AI services. The platform combines ingestion, design, testing, deployment and monitoring into one coherent control plane.
Outcome: a living enterprise knowledge layer that evolves with the organization.
Outcome: a custom AI assistant built in hours, not months.
By unifying these five stages — Ingestion → Design → Testing → Deployment → Monitoring — Syrtis delivers a CI/CD pipeline for generative AI, a control plane that governs both AI and non-AI, and a reliable path from prototype to production, grounded in European values of sovereignty, transparency and compliance.
At Syrtis, we believe enterprises should take control of AI, not the other way around. By breaking services down into agents and nodes, teams build workflows like assembling building blocks — dramatically cutting development time while keeping every workflow transparent and auditable.
Syrtis is the control plane that turns modular building blocks into production-grade, fully traceable AI services — faster than black-box apps, with enterprise governance built in.
Drag-and-drop workflows made of agents and nodes (LLM or classic code) let teams model any synchronous or asynchronous service in minutes.
Syrtis is model-agnostic: OpenAI, Anthropic, Mistral, Gemini or open-source LLMs, combined with search, RAG, HTTP calls and business rules in a single graph.
Every change comes with built-in versioning, live experimentation and automated evaluations — a test harness and a citation trail for every iteration.
AI applications in Syrtis are built step by step through scenarios (a full service), composed of agents (specific goals), themselves composed of nodes (LLMs or other functions).
Syrtis turns LLMs from a raw material into governed, modular, enterprise-ready AI services. By combining visual design, modularity, prebuilt modules, safe experimentation and governance, Syrtis lets organizations: