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DataCards – Decision intelligence for engineering and management

Trusted by engineering teams in sectors like aviation, automotive, energy and research, including:

Rolls-Royce
Land Brandenburg
DLR – German Aerospace Center
Deutsche Bahn
Vattenfall
Timpla
Federal Ministry of Research, Technology and Space
Humboldt-Universität zu Berlin and TUM

What is decision intelligence?

Decision Intelligence Platform

Decision intelligence for engineering and management.

Making decisions with more data and knowledge, better connected and in context. Every framework has the same layers, and each layer turns one thing into the next. DataCards is one opinion on how to build them, for engineering.

1. Data foundation — internal and external, structured and unstructured sources become one trusted context.
At DataCards: knowledge defragmentation. Not only data, also the knowledge scattered in tools, code and heads.

2. Analytics layer — data becomes decision services.
At DataCards: the Algorithm Warehouse. Rules, physical models and analyses as documented, executable, versioned services.

3. Orchestration layer — decision services become decision models.
At DataCards: reactive AI orchestration. When an input changes, every dependent decision recomputes.

4. Decision layer — models become insight.
At DataCards: the Executive Deck. Scenarios, simulations, live data, every number traceable to its source.

5. Governance layer — insight becomes governed impact, and feeds the next iteration.
At DataCards: the decision lifecycle. Versions, monitoring, audit. Design iterations, as in aerospace: each decision makes the next one better and faster.

Ad hoc vs. governed

What breaks when decisions
stay ad hoc.

Ad hoc works. Pull the files together, ask the AI, decide. It is fast and it feels smart. But the model behind that decision is thrown away. Six months later, the auditor asks why, or a similar question comes up, and the archaeology starts again. A chat plus a folder plus a workflow tool gives you answers. It does not give you a system that learns.

Ad-hoc AI decisions
Governed decisions
Files pulled together for one question
One shared decision model
Throwaway model, gone after the chat
Versioned decision services
Start from zero and hope the AI remembers something
Reused and refined, iteration by iteration
Nobody can say why, six months later
Traceable to the source, audit-ready
Workflow glue around a chat
Humans decide, agents assist inside guardrails
Ungoverned
Governed

The Decision Lifecycle.

  1. Model

    Experts capture their knowledge as decision services: rules, physical models and analyses, documented, executable and versioned in the Algorithm Warehouse.

  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 is traceable to the logic and the expert behind it.

  4. Learning

    Every decision feeds the next. Models are refined, services replaced, heuristics deepened, iteration by iteration.

Decision execution

One graph. Every dependency visible. Every update automatic.

Decision services publish and consume data products, forming a reactive dependency graph. When any input changes, every dependent decision recomputes automatically. No stale numbers, no manual re-runs.

T-shaped knowledge

Depth and breadth.
One shared workspace.

Good engineering decisions need both: the depth of specialists who hold the knowledge, and the breadth of generalists who carry the responsibility. In DataCards, specialists model decision services in the Algorithm Warehouse: proud to share. Generalists combine them in Executive Decks and decide, with the deep dive always one step away.

  • specialists
  • generalists
  • algorithm warehouse
  • executive decks
  • cards
  • scenarios
  • what-if analysis
  • simulation
  • live data
  • digital twins
  • shared line of sight
T-shaped knowledge: the breadth of responsibility of generalists in the Executive Deck meets the depth of knowledge of specialists in the Algorithm Warehouse. BREADTH OF RESPONSIBILITY DEPTH OF KNOWLEDGE ALGORITHM WAREHOUSE EXECUTIVE DECK Generalist Specialist Ask Implement

Humans and agents

Faster because it’s governed,
not despite it.

AI agents in DataCards act only on validated logic and inside clear guardrails. They show their reasoning, humans check, correct and approve, and every correction becomes knowledge the platform keeps. That is what makes decisions fast: nobody has to rebuild trust from scratch.

  • humans and agents
  • guardrails
  • explainable reasoning
  • approval before action
  • model-agnostic
  • corrections become knowledge
  • reactive knowledge graph
  • multi-level agents
  • structured outputs
  • hallucination guard

Traceability

Every output has a paper trail.
Every decision is explainable.

When your auditor asks how a number was calculated, the answer is a click-through to the exact notebook — reasoning and code, side by side.

Architecture

Runs in your cloud.
Enterprise-ready by design.

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, monitoring: building blocks your IT already trusts, with the same governance and audit trail everywhere. Not a science project. A platform your IT can sign off on.

  • BYOC
  • SaaS
  • on-premise
  • EU hosting
  • data residency
  • SSO
  • roles & permissions
  • audit-ready
  • isolated microVMs
  • git
  • API-first
Get a demo ↗
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What management gets

Speed. Consistency.
Confidence.

Three things management asks of every decision, and one that keeps them true over time. Behind them: decisions that are coherent, contextual, connected, compliant, cost-optimised, collaborative and continuous.

Speed

From months to minutes. Decision services are ready to run, scenarios on demand.

Consistency

Same question, same answer. Versioned logic and one shared model across teams.

Confidence

Show why. Every result is explainable and traceable to its source.

Continuity

Every decision feeds the next. Corrections become knowledge, models improve.

Industries

Proven in aviation.
Piloting in space, maritime and energy.

Aviation · Case study

Virtual Jet Engine Twin. How Rolls-Royce models design decisions with DataCards: from R&T development twins for new engine generations to maintenance twins mirroring the active fleet.

Space · Pilot

Space weather, satellites and mission planning. Decision services that connect component partners and consortia across the mission lifecycle.

Energy · Pilot

Grid and generation assets. Operational decisions across engineering and management, from asset health to scenario planning. Built environment and critical infrastructure: talk to us.

Maritime · Pilot

Ship and fleet. Design, service cycles and material loads in one shared decision model for yards, operators and research.

data sovereignty

Built and hosted in the EU 🇪🇺

DataCards is developed and owned in the EU and deployed exclusively on Hetzner servers in Germany or on-prem.

made by cavorit

The team behind the platform.

Cavorit is the engineering and consulting team behind DataCards. From AI maturity workshops to full decision-model builds, we offer everything from a scoped pilot to end-to-end enablement, so your organisation doesn’t just buy a platform, it builds a capability.

industries:

  • aerospace
  • Automotive
  • Energy
  • Mechanical engineering
  • Medtech
  • Pharma
  • Maritime
  • Public infrastructure
  • Space
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Learn more about Cavorit ↗

Let’s engineer your next decision.

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