# DataCards · Decision Intelligence Platform

> DataCards is the decision intelligence platform for engineering and management. Expert knowledge becomes documented, executable, versioned decision services in the Algorithm Warehouse; AI agents compose them into decision models and reactive workflows; Executive Decks show scenarios, simulations and live data with every number traceable to its source; the decision lifecycle keeps decisions governed (versions, monitoring, audit) and feeds the next iteration. Built by Cavorit Consulting GmbH (Berlin); runs in your cloud, as SaaS hosted in the EU (Hetzner, Germany) or on-premise.

Key facts:
- Product: DataCards (https://datacards.app), web application – bring your own cloud (BYOC), SaaS or on-premise.
- Positioning: decision intelligence for engineering and management – five layers, each turning one thing into the next. 1. Data foundation: knowledge defragmentation (not only data, also the knowledge scattered in tools, code and heads becomes one trusted context). 2. Analytics layer: the Algorithm Warehouse (rules, physical models and analyses as documented, executable, versioned decision services). 3. Orchestration layer: reactive AI orchestration (when an input changes, every dependent decision recomputes). 4. Decision layer: the Executive Deck (scenarios, simulations, live data, every number traceable to its source). 5. Governance layer: the decision lifecycle (versions, monitoring, audit; design iterations as in aerospace – each decision makes the next one better and faster).
- Ad hoc vs. governed: ad hoc AI decisions (files pulled together for one question, a throwaway model gone after the chat, start from zero next time, nobody can say why six months later, workflow glue around a chat, ungoverned) versus governed decisions (one shared decision model, versioned decision services, reused and refined iteration by iteration, traceable to the source and audit-ready, humans decide while agents assist inside guardrails).
- The decision lifecycle: 1. Model – experts capture their knowledge as decision services. 2. Composition – AI agents compose services into decision models and reactive workflows that update whenever an input changes, with engineers in the loop. 3. Decision – Executive Decks show scenarios, simulations and live data, every number traceable to the logic and the expert behind it. 4. Learning – every decision feeds the next: models refined, services replaced, heuristics deepened.
- Decision execution: decision services publish and consume data products and form a reactive dependency graph; when any input changes, every dependent decision recomputes automatically – no stale numbers, no manual re-runs.
- T-shaped knowledge: specialists model decision services in the Algorithm Warehouse, generalists combine them in Executive Decks (cards, scenarios, what-if analysis, simulation, live data, digital twins) and decide, with the deep dive always one step away – a shared line of sight.
- Humans and agents: AI agents act only on validated logic and inside clear guardrails, show their reasoning, humans check, correct and approve, and every correction becomes knowledge the platform keeps; approval before action, model-agnostic, multi-level agents, reactive knowledge graph, structured outputs, hallucination guard.
- Traceability: every output has a paper trail and every decision is explainable – when an auditor asks how a number was calculated, the answer is a click-through to the exact notebook, reasoning and code side by side.
- Architecture: bring your own cloud or use ours in the EU; every project runs in its own isolated microVM inside your VPC and your data stays where it is; Postgres, S3-compatible storage, Git, SSO, roles & permissions, monitoring; EU hosting and data residency, audit-ready, API-first.
- What management gets: speed (from months to minutes – decision services ready to run, scenarios on demand), consistency (same question, same answer – versioned logic, one shared model across teams), confidence (show why – every result explainable and traceable to its source), continuity (every decision feeds the next – corrections become knowledge, models improve); decisions that are coherent, contextual, connected, compliant, cost-optimised, collaborative and continuous.
- Industries: proven in aviation – Rolls-Royce models design decisions with DataCards, from R&T development twins for new engine generations to maintenance twins mirroring the active fleet (Virtual Jet Engine Twin); pilots in space (space weather, satellites and mission planning across component partners and consortia), energy (grid and generation assets, from asset health to scenario planning) and maritime (ship and fleet: design, service cycles and material loads in one shared decision model for yards, operators and research). Cavorit serves aerospace, automotive, energy, mechanical engineering, medtech, pharma, maritime, public infrastructure and space.
- Customers / references: Rolls-Royce, Deutsche Bahn, Vattenfall, DLR, Land Brandenburg, Timpla, Federal Ministry of Research, Technology and Space, HU Berlin, TUM.
- Company: Cavorit Consulting GmbH, Elsenstrasse 41, 12435 Berlin, Germany – the engineering and consulting team behind DataCards (AI maturity workshops, decision-model builds, scoped pilots to end-to-end enablement). Contact: info@cavorit.de. Demo: https://cal.com/team/cavorit/datacards-demo. Docs: https://docs.datacards.app/. Login: https://datacards.app/login.

## Pages

- [Home](https://datacards.app/): What is decision intelligence (the five layers), ad hoc vs. governed decisions, the decision lifecycle, decision execution, T-shaped knowledge, humans and agents, traceability, architecture, what management gets, industries, data sovereignty, about Cavorit.
- [Features](https://datacards.app/features/): Algorithm warehouse, legacy system orchestration, security & architecture FAQ (on-premise or SaaS, SSO, data sovereignty, AI security, integrations), digital twins, frontend components (the Deck), process canvas, data sovereignty.
- [Algorithm Warehouse](https://datacards.app/algorithm-warehouse/): The operating layer for algorithmic knowledge – why data is the fuel and DataCards the engine, how knowledge becomes dockable for business and AI, why engineering teams care, digital twins, platform delivery.
- [Legacy System Integration](https://datacards.app/legacy-integration/): Make fragmented tool landscapes operational – integration that serves orchestrated processes, bridging old systems without importing their complexity, from legacy inputs to reusable data products.
- [Case studies](https://datacards.app/casestudies/): Deutsche Bahn (galley management and maintenance in ICE4 trains), Rolls-Royce (Virtual Jet Engine digital twin simulating 600 parameters in real time), Timpla (financial planning by orchestrating legacy tools), Probabilistic Wind Avatars (experimental research on circular wind statistics).
- [Imprint / Legal](https://datacards.app/imprint/): Legal notice, privacy policy (Google Analytics), accessibility statement (German).

## Optional

- [Documentation](https://docs.datacards.app/)
- [Cavorit Consulting](https://cavorit.de/)
- [Cavorit on LinkedIn](https://www.linkedin.com/company/cavorit/)