LLM integrations
Connect language models to product features and relevant context. Define the expected output, handle failures, and design a useful experience around the model.
AI & Machine Learning Development.
Start with a task worth improving. We help teams explore, evaluate, and integrate AI into products and workflows, with attention to the data, limitations, and human decisions around it.
Talk about your projectWhat we can
build together.
For teams exploring an AI feature, connecting a language model to an existing product, or automating a repetitive workflow.
Workflow automation
Identify repeatable steps and integrate automation with existing tools and systems. Keep review and escalation available where a person needs to make the decision.
Custom models and evaluation
Assess whether an existing model meets the task or a more tailored approach is needed. Use representative examples and explicit evaluation criteria.
How we approach it.
Define a measurable use case
Agree on the problem, available data, acceptable outcomes, and failure cases. Decide what a useful result would look like before selecting a model.
Prototype and evaluate
Test an approach against realistic examples. Compare output quality, latency, and cost, then use the results to guide the next iteration.
Integrate with the product
Connect the chosen approach to the application and its operating environment. Include monitoring, fallbacks, and user feedback.
A few useful answers.
Does every AI project need a custom model?
No. An existing model with suitable context and integration may be enough. The choice should follow the task and evaluation results.
How do you handle unreliable model output?
We define evaluation cases and failure behaviour as part of the design. Depending on the task, that can include validation, restricted actions, human review, and a fallback experience.
Start with the problem.
Tell us what you’re working on and where you need a hand. We’re available to teams in Scandinavia, North Macedonia, and worldwide.
Let’s talk