01
Pick the moment
Find one product moment with the highest leverage - where users get stuck, repeat work or wait for someone - and define what success looks like.
Digital products & platforms
AI-Enabled Products
Make agentic AI a native part of your product - inline guidance, copilots and suggested actions that share one intelligence layer, not bolt-on chatbots beside it.
Native
AI moments inside the product, not a chatbot beside it
One layer
shared intelligence many product features can reuse
2-6 weeks
from kickoff to the first AI feature live in production
One product intelligence layer. Many AI moments.
Instead of stitching point AI features into different parts of the product, one shared layer powers every moment - search, guidance, copilots, actions and insight - using the same data, rules and design language your product already has.
- One AI layer connected to your product data and APIs
- Features reuse the same prompts, tools and guardrails
- Shipped like any other product feature - your team owns it
Native means it lives in the flows users already know - not in a separate window they have to remember to open.
Where it shows up in the business
Where AI-native features live in your product
From customer-facing surfaces to internal tools and analytics - the same intelligence layer powers AI moments wherever your users already work.
Engineering & data tools
Project & data
AI-native features inside PLM, PDM and engineering tools - sourcing, indexing and document generation built in.
Engineering & production
Production
Reduce downtime, changeovers, and manual coordination on the floor.
From product moment to live feature
How an AI feature actually lands in your product
Each AI feature follows the same four steps - so it ships like any other product capability, with metrics, ownership and a roadmap, not a one-off experiment.
02
Design it native
Design the AI moment inside the existing flow - same data, same UI patterns, same auth - so it feels like a feature, not a bolt-on chatbot.
03
Build on the platform
Build the workflow on the agentic platform: prompts, tools, retrieval and guardrails - reusable across other moments, not stitched into the product code.
04
Ship, measure, expand
Release to a slice of users, instrument adoption and quality, iterate, then reuse the same building blocks for the next AI moment.
Customer stories
Already running in production
From a Nordic media intelligence platform to an enterprise LMS used by 40,000+ learners worldwide - agentic AI shipped as native product capability.
MedieX
Media intelligence platform · Editorial product
~80%
of all digital editorial media in Norway covered by an
agentic product that turns noise into structured intelligence.
AI is built into the product itself - ingestion, contextual sentiment, risk assessment and summaries are native features of MedieX, not a separate chatbot. EU AI Act compliant by design.
MedieX: From media noise to actionable insight
Read the full story →
Samelane
Enterprise LMS · Competency Intelligence
40K+
learners on an LMS that ships Dify-powered course
generation, report summaries and semantic search as
native product capability.
Used by Comcast, Grupa Azoty, Science Pharma, Medivet and more. AI lives inside the product flows trainers and learners already use - the natural step from LMS to Competency Intelligence.
Samelane: From LMS to AI-native Competency Intelligence
Read the full story →
FAQ
Questions we often hear
Short answers on what it means to make AI a native part of your product, where to start, and how it ships alongside your team.
What is an AI-enabled product?
An AI-enabled product has agentic AI built into the product itself - as inline guidance, copilots, suggested actions and insight - instead of as a separate chatbot bolted on the side. The AI shares the product's data, logic and design system, so it feels like a native feature.
How is this different from adding a chatbot?
A chatbot lives next to the product. AI-enabled features live inside it: a suggestion in the form your user is filling, a summary at the top of the dashboard, an action they can apply with one click. They reuse the product's auth, data and UI, and are shipped like any other product feature.
Where does AI usually show up first inside a product?
Usually in the moments where users get stuck or repeat the same work: onboarding, search, configuring something, summarising activity, drafting a response. Those are the highest-impact spots to start - small AI moments that change how the product feels.
Do we need to rebuild our product?
No. We add AI features alongside what you already have using your existing APIs, data and design system. The AI layer can grow inside your product over time - one feature at a time - without a rewrite.
Who owns the product features we ship together?
You do. We work alongside your product, design and engineering teams - the AI features land in your codebase or your platform, follow your release process, and your team owns the roadmap.
How do you handle data, privacy and model choice?
All AI features run on the agentic platform with full audit trail, role-based access and EU data residency by default. We choose models per use case (open or proprietary) and can deploy in our managed Nordic environment, your own cloud or on-premise if security requires it.
How long until the first AI feature is live?
Typically 2-6 weeks from kickoff to first AI moment in production - scoped to one high-value feature, with metrics in place to measure adoption and impact before expanding.
Shall we grab a coffee?
A short conversation to find the first AI moment that's worth shipping inside your product.
