For CTOs, CIOs and engineering leaders

Build the foundation for an AI-native organization.

We design agent harnesses, transform software delivery, and help businesses put AI to work — reliably, securely, and within the systems they already use.

Discuss your AI transformation Explore our services

Agents teams can trustDelivery that keeps paceAI inside your systemsCopenhagen · DK

Why it matters

Experiments are easy. Operations are not.

Most organizations have already tried AI. Few have changed how work gets done. The gap is rarely the model — it is the system around it: shared context, controls, testing and clear ownership.

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

What we believe

Where AI stalls

  1. 01

    Agents that don’t connect

    Every team builds its own prompts, tools and workarounds. Context is fragmented, instructions drift, and each success is hard to repeat.

  2. 02

    Engineering that hasn’t caught up

    Coding tools speed up implementation, but planning, review, testing and release still run at human speed. The bottleneck moves; it doesn’t disappear.

  3. 03

    Autonomy without control

    Without permissions, approvals, logs and evaluations, agents can’t be trusted with real work — and leaders can’t see what they are doing.

How we work with AI

  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

Three things you can hire us to do.

Each service stands on its own. Together they take AI from isolated experiments to a governed, AI-native organization.

01 — Organizational Agent Harness

Make AI agents work as a system, not as isolated experiments.

The problem

Teams are experimenting with agents, but context is fragmented, instructions are inconsistent, tools are disconnected, and nobody can see what the agents are doing. Long-running work breaks when a session ends.

What we implement

A reusable organizational harness: the engineering foundation, integrations, operating rules and controls that make agents practical to deploy and manage. Not a chatbot, and not an agent framework installed and left behind — the infrastructure that lets useful agents operate reliably.

Deliverables

  • Shared instructions, context & reusable skills
  • Orchestration, long-running workflows & task handoffs
  • Tool integrations (APIs, MCP) with scoped access
  • Evaluations, approval gates, audit trail & handover

What changes

  • Consistent AI behavior across teams
  • Less duplicated implementation work
  • Control over autonomous operations
  • Reliable execution of longer workflows
  • Reusable capabilities across business functions
  • Systems your own teams can understand and maintain
GuardrailsScoped permissions · Approval for irreversible actions · Full action log · Instant stop

Discuss an agent harnessRead the case study

AGENT HARNESS CONTEXT SKILLS ORCHESTRATION EVALS GOVERNANCE OBSERVABILITY REQUEST AGENT TOOL VALIDATE APPROVE OUTPUT plan · act approved checks human gate
09:14:02 route request → procurement-agent 09:14:03 call erp.read · approved tool 09:14:04 check output validated 09:14:05 wait approval required → owner

02 — Agentic SDLC Transformation

Don’t just accelerate coding. Transform software delivery.

The problem

AI coding tools speed up implementation, but the bottlenecks sit around it: unclear requirements, slow reviews, manual testing, cautious release processes and knowledge that lives in people’s heads.

What we implement

An AI-native software lifecycle: intent and specifications captured as version-controlled artifacts, agents that build against shared project knowledge, automated validation at every stage, and human approval where judgement and accountability matter. It is a loop, not a line.

Deliverables

  • SDLC maturity assessment & AI-native roadmap
  • Agent configuration, project context & reusable skills
  • Workflows, hooks, evaluations & governance controls
  • GitHub / Azure DevOps, PR review, CI/CD & team enablement

What changes

  • Shorter software delivery cycles
  • Less repetitive engineering work
  • More consistent quality controls
  • Faster feedback and better traceability
  • More engineering capacity for architecture and business decisions

We measure cycle time, rework and review load against your own baseline. We don’t promise numbers before we have seen your process.

Discuss your SDLCRead the case study

CONTINUOUS EVALS PLAN DESIGN BUILD TEST DEPLOY MAINTAIN capture intent policy as skills agents · hooks feedback · evals review · CI/CD signals · incidents intent.md spec.md · plan.md diff+tests PR + findings released change incident → intent.md approve approve approve EVERY STAGE COMMITS AN ARTIFACT THE NEXT STAGE READS IT CONTINUOUS EVALS PLAN DESIGN BUILD TEST DEPLOY MAINTAIN capture intent policy as skills agents · hooks feedback · evals review · CI/CD signals · incidents intent.md · approve spec + plan · approve diff + tests PR + findings · approve released change incident → intent.md
  1. 01

    Plan

    Capture intent and business requirements as structured, version-controlled artifacts.

  2. 02

    Design

    Turn approved intent into technical specifications, using your architecture and policy constraints.

  3. 03

    Build

    Coding agents work from shared project context, reusable skills and guardrails, in parallel where it helps.

  4. 04

    Test

    Continuous feedback: automated tests, browser verification and evaluation suites run as the work happens.

  5. 05

    Deploy

    Agentic code review, CI/CD and policy checks, with human approval where it counts.

  6. 06

    Maintain

    Operational signals and incidents flow back into the loop as the next piece of intent.

03 — AI Architecture & Consulting

From AI ambition to production-ready systems.

The problem

Leaders know AI matters, but not where it belongs first, how it fits their data and security landscape, or what it takes to run it safely. Strategy decks and stand-alone prototypes rarely close that gap.

What we do

Practical decisions, backed by hands-on engineering. We assess where AI creates value, design the architecture, and build prototypes or production components alongside your team — then hand over what we built so you can run it.

Capabilities

  • Opportunity & readiness assessment, strategy and roadmap
  • Solution architecture, cloud, data & enterprise integration
  • LLM, RAG & agent systems with security, identity & governance
  • Prototyping, evaluation & knowledge transfer

What changes

  • A prioritized roadmap tied to business value
  • Architecture decisions made before money is spent
  • A working prototype on your own data and systems
  • A clear security, ownership and governance model
  • Teams equipped to carry on without us

Discuss your AI roadmapRead the case study

VALUE ↑ FEASIBILITY → START HERE

How they fit

  1. Decide

    Consulting

    Where AI belongs, how it fits, what to build first.

  2. Foundation

    Agent harness

    The shared, governed base every business agent runs on.

  3. Delivery

    Agentic SDLC

    The same principles applied to how your software is built and run.

How the systems work

Controlled execution, not blind autonomy.

Whichever service you start with, the same six mechanisms make agent work dependable. Select a layer to see where it acts.

  1. Shared instructions, skills and organizational knowledge are loaded before an agent starts, so every agent works from the same rules.

  2. Work is routed to the right agent, split into tasks and handed over with structured state, so long jobs survive across sessions.

  3. Agents reach business systems only through approved connections — APIs and MCP servers with scoped permissions.

  4. Permissions, policy checks and approval gates are enforced in code at the moment an agent acts, not in a policy document.

  5. Test suites and regression checks run continuously, so behavior is measured rather than assumed and changes don’t silently break what worked.

  6. People approve what is irreversible or regulated, and can see a full trace of what happened and why.

BUSINESS REQUEST CONTEXTinstructions · skills · memory ORCHESTRATIONroute · delegate · hand off TOOLSMCP · APIs · business systems CONTROLSpermissions · policy · approval OUTPUT + AUDIT TRAIL EVALUATIONS HUMANdecides

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 your goals, systems, teams and constraints, and find where AI will pay off first.

You receiveReadiness assessment, prioritized opportunities and a scoped plan

02

Design

Define the architecture, controls and operating model — including where people approve and who owns what.

You receiveReference architecture, governance model and implementation roadmap

03

Implement

Build the harness, the delivery workflows or the prototype with your engineers, on your own systems and data.

You receiveWorking, integrated components in your environment

04

Validate

Measure quality, reliability, cost and risk against the success criteria agreed at the start.

You receiveAn evaluation suite and a report against agreed criteria

05

Enable & improve

Hand over, train your teams and set up the feedback loops that keep the system improving.

You receiveDocumentation, runbooks, trained teams and a roadmap for what’s next

Experience

Selected work.

Systems of the kind we design and build across knowledge, finance, data and management reporting. Each is shown as an anonymised, illustrative example of the system itself.

P—01

Enterprise Knowledge Agent

An assistant connecting internal documents, policies and operational systems.

Illustrative example

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.

Illustrative example

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.

Illustrative example

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.

Illustrative example

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

Worked cases: finance agent harness · agentic SDLC in energy · AI assistant for refugees.

Examples are illustrative and pending client approval. No client names, metrics or results are claimed.

Contact

Ready to make AI operational?

Whether you’re building your first organizational agent harness or transforming your engineering lifecycle, let’s discuss where to start.

Start a conversation

Official partners of Anthropic, AWS and Microsoft

Email
contact@core.ai
LinkedIn
linkedin.com/company/coreai-dk
Location
Copenhagen, Denmark