Artificial intelligence and machine learning

Turn your data into a tool your team can use.

Artificial intelligence can help retrieve information, interpret images and detect patterns that are difficult to review manually. Together we define a useful task, assess the available data and build a pilot with quality criteria to decide whether it should move into production.

What we deliver

Use case definition, data review and a baseline for comparison.
Pilot integrating existing models or training when justified.
Evaluation on examples held out from development, error analysis and known limitations.
Agreed interface or integration, documentation and estimated operating costs.

Start with a focused pilot

One task, a representative data sample and an agreed metric. Depending on the case, we evaluate accuracy, information retrieval, errors, latency and cost per use before expanding the project.

How we define scope

Quality depends on the data and operating environment. Labeling, training, model usage and monitoring are sized separately. We define human review when an incorrect answer could affect the operation.

Where it can help

Illustrative use cases. The solution is scoped for your project.

Search internal documentation with an assistant that cites its sources.
Classify images or detect objects with computer vision and review uncertain cases.
Forecast demand or detect anomalies using suitable historical data.
Run a model on a device when latency, connectivity or privacy require it.

Questions before you start

Do you use generative AI or train custom models?: We choose based on the problem. An assistant may integrate an existing model with your documents; computer vision or prediction may require fine-tuning or training. We compare quality, cost and data constraints first.
What if I do not have enough data yet?: We assess whether the existing data supports a useful test. If not, the first deliverable can be a collection, organization or labeling plan. We do not promise a reliable predictive model without a suitable foundation.
Is my data sent to an external provider?: Architecture and providers are defined before the pilot based on data permissions and requirements. We evaluate APIs, self-hosted infrastructure or on-device execution and their costs.
How do you handle incorrect answers?: We test representative cases, document failures and define behavior under uncertainty. Depending on the task, we include sources, confidence thresholds and human review. No model guarantees zero errors.

Contact us

Tell us what you need to improve. We review your situation and define the next step with you before preparing a scoped proposal.

Describe the decision or task you want to support, which data you have, who can use it and which errors would be unacceptable for your team.

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