CoreAI — AI systems built for real work.

We design AI automation, augmented workflows and intelligent agents that work inside the systems your business already runs on — with people kept in control.

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About

AI should augment people — not add complexity.

CoreAI is a Copenhagen-based AI automation consultancy. We help organisations move from experimenting with AI to relying on it in daily operations — built on the systems, data and controls they already have.

The most valuable AI doesn’t replace people. It removes the friction around them.

What we believe
  1. 01

    Augment, don’t replace

    Systems designed around the people who use them — not around the model.

  2. 02

    Measured, not assumed

    Every system starts from a baseline, so its value can be proven — or questioned.

  3. 03

    Humans in the loop

    Clear points where people review, approve or override. Autonomy is earned, not assumed.

  4. 04

    Secure by design

    Data boundaries, access control and audit trails from the first prototype.

  5. 05

    Built on what you have

    We integrate with your ERP, data platform, identity and APIs instead of adding another silo.

  6. 06

    AI for good

    Responsible adoption that is transparent to employees and customers, designed with GDPR and the EU AI Act in mind.

Services

Four ways we put AI to work.

From one well-scoped assistant to agents working across your stack. Every engagement is built for production — not for the demo.

01 — Augmented LLM

An expert colleague for every team, grounded in your own knowledge.

LLM-powered systems that extend human capability through enterprise knowledge, contextual assistance, research, analysis and decision support. Every answer traces back to its source.

  • Enterprise knowledge assistants
  • Retrieval-augmented generation
  • Document intelligence
  • AI search
  • Decision support
  • Internal copilots
SOURCES DOCS POLICIES ERP EMAIL ACL RETRIEVErank·filter LLM ANSWER [1] SOP 17 [2] POL 4.2 [3] ERP·INV PERSON
Fig. 01 — Retrieval with cited sources. Access rights are respected before the model ever sees a document.

02 — AI Workflows

Redesign the work itself, not just the tools around it.

We rebuild repetitive processes around AI and automation — so documents, reports and approvals move on their own, and people handle the exceptions that need judgement.

  • Document processing
  • Reporting
  • Research
  • Operational workflows
  • Approval flows
  • System integrations
exception → person 01 02 03 04 05 INPUT UNDERSTAND DECIDE ACT VERIFY email·pdf extract rules+llm post·route check·log exception → person 01 02 03 04 05 INPUTemail · pdf UNDERSTANDextract DECIDErules + llm ACTpost · route VERIFYcheck · log
Fig. 02 — Input → Understand → Decide → Act → Verify. Every workflow ends in verification; exceptions go to a person, not into a void.

03 — AI Agents

Controlled autonomy across your enterprise systems.

Agents that plan and carry out multi-step work across ERP, data platforms and APIs — within explicit permissions, with every action logged and a person at the decisions that matter.

  • Autonomous research
  • Operational agents
  • System monitoring
  • Multi-agent workflows
  • Tool & API integrations
  • MCP-based systems
  • Human-in-the-loop agents
GuardrailsScoped permissions · Approval for irreversible actions · Full action log · Instant stop
PERMISSION SCOPE ERP DATA MCP API CRM AGENT plan·act·log HUMAN APPROVAL ACT
14:02:11 read erp.ledger(period=08) 14:02:13 flag 3 anomalies · evidence attached 14:02:14 wait approval required → controller
Fig. 03 — The agent can read widely but act narrowly. Anything irreversible crosses a human gate.

04 — AI Strategy & Architecture

Know where AI pays off — before anything is built.

We help leadership teams see where AI creates real value, and design the architecture, governance and operating model to implement it responsibly.

  • AI opportunity discovery
  • Reference architecture
  • Governance
  • Prototypes
  • Production roadmap
  • Security
  • Evaluation
  • AI operating models
VALUE ↑ FEASIBILITY → START HERE
Fig. 04 — Opportunity mapping. We start where value and feasibility overlap, and sequence the rest into a roadmap.

How we work

From first question to production system.

Each phase ends with a decision — continue, adjust or stop. You never pay for momentum.

01

Discover

Understand the business problem and identify where AI can create measurable value.

OutputOpportunity map, baseline and business case

02

Prototype

Build quickly using real company data and workflows.

OutputA working prototype on your own data and processes

03

Validate

Measure quality, reliability, cost and business impact.

OutputEvaluation report against agreed success criteria

04

Integrate

Connect AI with existing systems, APIs and data.

OutputProduction integration with your ERP, data platform and identity

05

Scale

Add monitoring, governance, security and operational controls.

OutputMonitoring, runbooks and an operating model your team owns

Projects

Selected work.

Systems we design and build across knowledge, finance, data and management reporting. Clients are anonymised; each case is described as the system it is.

P—01

Enterprise Knowledge Agent

An assistant connecting internal documents, policies and operational systems.

RAG / LLM /
Enterprise Search

Challenge
Policies, procedures and operating knowledge spread across document stores, shared drives and the ERP. Finding an answer meant knowing whom to ask.
Approach
Permission-aware retrieval over all sources, with cited answers and connectors into operational systems for live context.
Human control
Answers always show their sources; low-confidence questions are routed to the owning team.
Stack
Hybrid search · vector index · LLM · SSO / access control · evaluation set

P—02

Finance Operations Agent

An agent that analyses operational data, detects anomalies and prepares evidence for human review.

Agents / MCP /
Enterprise Systems

Challenge
Controllers spent the period close reconciling data by hand to find the few entries that actually needed attention.
Approach
An agent reads ledger and operational data through MCP tools, flags anomalies and assembles an evidence pack for each finding.
Human control
Read-only by default. Nothing is posted or changed without a controller’s approval; every step is logged.
Stack
MCP servers · ERP API · anomaly rules + LLM reasoning · approval queue · audit log

P—03

Intelligent Data Pipeline

AI-assisted ingestion, validation, monitoring and data-quality analysis.

Data / Automation /
AI

Challenge
Data from many sources, where quality issues only surfaced once they reached downstream reports.
Approach
A validation layer that classifies anomalies at ingestion, explains the likely cause and routes each issue to its data owner.
Human control
Data owners confirm or reject every proposed fix; rules learnt from their decisions are versioned.
Stack
Medallion lakehouse · orchestration · schema & drift checks · LLM triage · alerting

P—04

Management Intelligence

Automated synthesis of operational information into decision-ready management reporting.

LLM / Analytics /
Automation

Challenge
Leadership reports assembled by hand each month from several systems, leaving little time for analysis.
Approach
Automated KPI assembly with drafted commentary, highlighted exceptions and drill-through to source data.
Human control
Analysts review and edit every narrative before distribution; figures are never generated, only sourced.
Stack
Semantic model · scheduled pipelines · LLM summarisation · review workflow

Case studies are illustrative placeholders pending client approval.

Contact

Let’s build something useful.

If you’re exploring how AI could improve your operations, workflows or products, let’s talk. We start with the problem, not the technology.

Start a conversation
Email
hello@coreai.dk
LinkedIn
linkedin.com/company/coreai
Location
Copenhagen, Denmark