AI-powered solutions
AI is transforming the way we use digital products, both when it comes to our internal tools and our favourite apps. We help our clients develop AI-features in their products as well as facilitate their everyday lives with automated workflows.
AI-powered products
We help design, develop and integrate AI agents and features into your digital product.
Leading legal tech company
We develop the core of an AI agent for a world-leading legal tech company. We support our client with building the integrations that let the AI agent tap into external legal sources.
HAALE
We designed and developed the HAALE app, that connects with their smart ring. The app relies heavily on AI to offer personalised insights on the users health data.
Workflow & process automation
We help organizations automate the manual, repetitive work that holds a business together behind the scenes. The tasks where people spend their day copying information between systems, chasing documents, and pushing work from one queue to the next.
Try our our demo here to see how it could work in practice.
Machine learning & data analysis
We help companies understand and structure their data, and develop and apply advanced machine learning models.
DHL Freight
We helped DHL Freight understand and analyse massive amount of shipment data to optimize pickup points and network utilization for more efficient business operations.
DAHL
We helped Dahl get a better understanding on their varying customer base, by clustering their clients to better understand their buying behaviours.
How we help our clients with AI
Advisory & discovery
We run focused discovery work to map where AI can realistically create value in your product and operations, what’s worth piloting now versus parking, and what it would take to ship. You come out with a prioritized roadmap and a clear-eyed view of effort, risk and impact.
Design & development
The main part of what we do is the engineering behind the AI use you read about on this page. That means designing the tools the agent uses, handling errors, building guardrails and testing.
AI team augmentation
When you already know what to do but lack some extra pair of hands our experienced AI engineers come handy. They bring patterns from other AI projects and are ready to deliver value from day 1.
Ready to discuss AI?
Let’s talk about your needs and how we would solve them.
The agentic systems breakdown
Knowledge & grounding agents
Knowledge & grounding agents are grounded in your own documents and data, so its answers and actions come from your material rather than whatever the model happened to learn in training. This is often called RAG (retrieval-augmented generation).
Good for: questions and tasks where the right answer lives in internal content such as policies, product docs, past cases, or contracts. It cuts down on wrong or made-up answers.
Workflow agents
What it is: an agent that runs along a path you’ve laid out, handling the judgement-heavy steps while the overall flow stays defined in code. The steps are known in advance.
Good for: processes where you already know the shape of the work and want it to run the same way every time. The most predictable kind, and usually the cheapest and easiest to make reliable.
Task agents
What it is: you give the agent a defined job and it completes it from start to finish, such as gathering data, drafting a document, doing research or triaging incoming items. It works out the steps itself instead of following a fixed path.
Good for: multi-step work where the exact steps can’t be known up front. More flexible than a workflow agent but harder to make reliable, so it’s usually run with an approval step before anything critical happens.
Chat agents
What it is: an agent you interact with through conversation, in chat or increasingly by voice. It can answer questions, and if it’s connected to your systems it can carry out the request rather than just pointing at an article. The same idea works internally as agent-assist, sitting next to staff during a live conversation and pulling up information or drafting replies.
Good for: customer support and internal help, as long as it’s grounded in your own content so it doesn’t make things up. The agent-assist version is a lower-risk start, since a person still sends the final reply.
Coding agents
A coding agent writes and runs code to get things done. At one end that’s working in a codebase, writing, editing, fixing, and reviewing software with a developer approving what ships.
The more interesting end is in-product: instead of a fixed set of tools, the agent gets a sandbox and writes and runs code on the fly to handle problems you couldn’t anticipate, especially work over large or messy datasets. Code becomes the agent’s universal tool, run inside a constrained harness so it can do a lot without being able to do damage.
Good for: speeding up software delivery on well-defined work, and dynamic, data-heavy tasks where the request is different every time, like slicing a big dataset a new way for every question or making sense of whatever a user uploads.
Multi-agent systems
What it is: several specialised agents working together on a larger job, where one breaks the work down and hands pieces to the others.
Good for: complex tasks that are too much for a single agent, or where different steps need different skills or different access. There are more moving parts, so this is used when the problem genuinely calls for it.
Connecting AI to your tools
Agent harness
The harness is everything around the model that you need to run an agent in production rather than just demo it. A demo usually skips all of it, which is why demos are easy and production systems are hard.
Four of them matter most.
- Evaluation is the test suite that tells you whether the agent is good enough to ship, and whether it stays that way as the model, the prompts, or the data change underneath it.
- Guardrails are the checks on what goes in and out, plus the approval points where a person signs off before anything critical happens.
- Context management is deciding what the agent sees and remembers at each step, so it keeps the thread without running past the model’s limits.
- Observability is the logging and tracing that show what the agent did and why, so you can debug it and catch a drop in quality before users do.
Skip these and you have a demo. Build them and you have something you can put in front of real work.
MCP and integrations
Integrations to third-party (or other internal tools) is crucial for effective agentic applications. Integrations allow agents to access data in real-time, perform actions, and orchestrate between different systems in a seamless way.
The Model Context Protocol (MCP) also enables your agent to easily connect to other SaaS tools, but it can also let other agents (such as Claude or ChatGPT) connect and use your services over a standardised and controlled protocol.
Data engineering
AI runs on data, and most of the work in any real AI project is getting the right data to the right place in the right shape. This area is the pipelines and platforms that feed AI systems: getting data out of source systems, cleaning and transforming it, storing it (warehouses, lakes, lakehouses), and the newer pieces AI needs specifically, like vector databases for search by meaning, feature stores for ML, and streaming for things that cannot wait for an overnight batch.
Report: Where AI is creating value in finance 2026
We sat down with leaders in the financial sector to discuss where they see AI creating value in customer experience today, and what’s prohibiting it. The result is a glimpse of the AI reality inside financial institutions – the value, the worries and the one question they all are asking “What will happen to customer experience when we go towards agent-to-agent interactions?” Download the report here.
AI is part of our everyday work
At Qvik, AI is part of how we build products every day. It helps us move faster, keep quality high and get more done. But it also comes with the responsibility of using it the right way.
Skilled experts in control
AI doesn't replace judgement. Our skilled consultants direct the tools, evaluate every output, and make the calls. Accountability stays with our people, and that means faster delivery without trading away quality.
Strong technical foundation
Projects with clean architecture, tests, well-defined APIs, consistent patterns and solid documentation get dramatically more value from AI assistance. Part of our consulting approach is helping clients build or improve these foundations.
Domain expertise
Our expert consultants genuinely understand your industry and know how to apply AI in the most efficient way. In regulated industries technical skill is not enough. We also understand the context and constraints, and know what's possible with strict regulation and sensitive data.
Let's talk about AI
Robert Seege, Sales Director