AI-native enterprise engineering

Foundation. Capability.
Intelligence.

Build trusted foundations. Make enterprise capabilities available to AI. Turn them into intelligence.

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The Cardim model

From enterprise foundations to intelligence

AI value does not start with a model. It starts with trusted foundations, reusable capabilities, and systems designed for intelligence.

01

Foundation

Data + Governance

Trusted enterprise data, governance, security, architecture, and context create the base for everything above.

  • Data platforms
  • Governance
  • APIs
  • Enterprise architecture
  • Security
DatabricksMicrosoft Fabric
02

Capability

Make the enterprise available to AI

Turn systems, data, knowledge, APIs, and tools into reusable capabilities that AI can safely discover and use.

  • MCP
  • Tool catalogs
  • Skills
  • Enterprise integrations
03

Intelligence

Turn capabilities into outcomes

Build AI systems that observe, reason, act, evaluate and improve.

  • Agents
  • AI loops
  • Orchestration
  • Intelligent workflows

Platform stack

A structured path to enterprise AI

Every layer builds on the one below. Intelligence is only as reliable as the foundation it stands on.

Cardim Intelligence Stack

Foundation → Capability → Intelligence

  1. Intelligence · Autonomy

    6Loops

    Outcome-driven autonomous workflows

    • Observe
    • Reason
    • Act
    • Evaluate
    • LearnObserve

  2. Capability · Capabilities

    5Skills

    Reusable AI capabilities: tools + instructions + retrieval

    • Tools
    • Instructions
    • Retrieval

  3. Capability · Capabilities

    4Tools Catalog

    Governed registry of callable capabilities

    • Registry
    • Owners & versions
    • Access policies
    • Usage & evals

  4. Capability · Standardized Access

    3MCP

    Standard protocol between AI apps and enterprise systems

    • MCP servers
    • Tools
    • Resources
    • Prompts

  5. Foundation · Data Access

    2API

    Data & service access

    • API endpoints
    • API gateway
    • Events

  6. Foundation · Data Foundation

    1Unified Data Platform

    Trusted data foundation

    • Lakehouse / Warehouse
    • Streaming
    • Data pipelines
    • Catalog & lineage

Identity & access · Security · Observability · Evaluation · Governance

Controls run through every layer — not only the data platform.

Cardim Intelligence Stack — Foundation. Capability. Intelligence.

Explore the Intelligence Stack

Capabilities

What we engineer

One path, four capabilities — from trusted data to intelligent, production-grade systems.

01 · Foundation

Unified data platforms & governance

Unify what matters, govern everything. We design and build unified data platforms on Databricks and Microsoft Fabric with ownership, quality, lineage and access control built in — one governed catalog over your data — consolidated where it pays off, shared zero-copy where it should stay put.

How we build unified data platforms

02 · Capability

AI-ready access: MCP, tool catalogs and skills

We turn your documents, APIs and business systems into governed services AI can safely use: enterprise RAG, MCP servers on top of your APIs, and reusable tool catalogs and skills — with your data under your control.

AI-ready services from your data

03 · Intelligence

Agents, loops and orchestration

We design agents and workflows that observe, reason, act, evaluate and learn — with least-privilege identities, human approval where it matters, and evaluation that keeps behavior measurable.

How we build intelligent systems

04 · Industrialization

From AI prototype to enterprise system

AI builders prove an idea in hours. Scaling it into an enterprise system takes architecture, security and integration — we engineer that path, step by step.

Read the prototype-to-production guide
Explore our capabilities

How we work

Build what lasts. Automate what scales.

Cardim combines senior engineering expertise with AI-native ways of working — so every engagement leaves behind capability, not dependency.

01

Architect first

Build the right foundations before adding complexity.

02

Make capabilities reusable

Avoid isolated AI experiments. Create building blocks that can be reused across products and teams.

03

Design for AI from the start

Expose data, tools, and business capabilities in ways AI can safely consume.

04

Automate toward outcomes

Move from individual tools to skills, workflows, and intelligent loops.

Why Cardim

Senior-led. Durable capability.

We focus on systems and capabilities that compound over time — senior engineering, not headcount.

  • Senior engineering depth

  • AI-native delivery

  • Reusable enterprise capability

  • Architecture designed to scale

Work

Prototype fast. Industrialize properly.

AI builders can prove an idea in hours. Cardim turns that prototype into a secure, integrated, scalable enterprise system.

Illustrative scenarios based on typical engagements.

Prototypes built with

  • Lovable
  • Bolt
  • Replit
  • Base44
  • v0
  • Cursor
  • Claude Code
  • OpenAI Codex
  1. 01AI Prototype
  2. 02Architecture
  3. 03Integration
  4. 04Governance & Security
  5. 05Industrialization
  6. 06Enterprise System

Identity, security and governance run from step 01 — the architecture step includes a threat model.

Example 01 · Typical prototype

Internal app built in Lovable

A business team creates a working internal application in Lovable with forms, workflows, authentication, and sample data.

Industrialization

Enterprise operations platform

  • Enterprise SSO / Entra ID
  • Governed APIs
  • Production data integration
  • RBAC and audit logging
  • CI/CD and separated environments
  • Observability and alerting
  • Security scanning
  • Infrastructure as code
  • Scalable backend services

Typical outcome

A prototype becomes a supported internal product that can handle real users, real data, and enterprise controls.

Example 02 · Typical prototype

AI assistant built with Cursor or Claude Code

A team builds an AI assistant that answers questions from documents and calls a few APIs.

Industrialization

Governed enterprise AI application

  • Enterprise RAG architecture
  • Governed document ingestion
  • Identity-aware retrieval
  • API / MCP capability layer
  • Tool catalog
  • Model routing
  • Prompt and version management
  • Evaluation framework
  • Tracing and observability
  • Guardrails and access control
  • Scalable cloud deployment

Typical outcome

The demo becomes a production AI capability integrated with enterprise systems rather than an isolated chatbot.

Example 03 · Typical prototype

Workflow prototype built in Replit or Bolt

A team prototypes a workflow where AI reads a case, recommends an action, and updates a simple application.

Industrialization

Autonomous enterprise workflow

  • Event-driven integrations
  • Enterprise APIs
  • MCP / tool interfaces
  • Reusable AI skills
  • State and workflow management
  • Human approval where required
  • Retry and failure handling
  • Auditability
  • Evaluation and monitoring
  • Autonomous AI loops

Typical outcome

The prototype evolves from “AI gives an answer” into “AI safely participates in a real business process.”

The prototype proves the idea. We engineer what happens next.

From a fast AI-generated prototype to a system that can operate every day at enterprise scale.

Read the guide: from AI prototype to production Lovable to production: the enterprise checklist

About

An AI-native engineering company from Gothenburg

Cardim IT is not a traditional consultancy selling engineering hours. We build the path from trusted enterprise foundations to AI-ready capabilities and intelligent systems.

AI engineering in Gothenburg

Fernando Mattos

Founder & AI Platform Architect

I turn ambitious technology strategy into working products — from the first executive conversation to secure, production-ready delivery. Twelve years building complex systems across Azure, AWS, Databricks and Kubernetes, including a global predictive maintenance platform for connected trucks. Senior judgement and AI leverage belong together: senior-led delivery moves faster when automation removes the mechanical work.

LinkedIn profile

We engineer the path from data to intelligence.

Start with the foundation. Build the capability. Create the intelligence.

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