AI implementation
AI agents, document processing and forecasting models running in production, with quality evaluation and an audit trail.
Overview
We implement AI in processes where it delivers a measurable result: handling tickets and complaints, document analysis, search across company knowledge, demand and staffing forecasts. Every project starts by defining how the result will be measured.
Before launch, the model is tested on the client's data and on edge cases. In processes where an error has consequences, a person approves the decision, and the system records what its answer was based on.
We build solutions so that the model provider can be changed without rewriting the system. Data can remain in the client's infrastructure.
Scope of work
- Assessment of the process for AI use
- Test set with a quality metric
- Agent, assistant or model in production
- Human oversight and decision log
- Quality and cost monitoring
- Documentation for AI Act purposes
Project flow
- 01
Choosing the process
We assess processes by how repetitive they are, the cost of an error and the availability of data. We pick the one where the result can be measured fastest.
- 02
Pilot
Within 4-6 weeks we build a solution on the client's data and compare the results with the current process.
- 03
Deployment
We integrate the solution with company systems, define which decisions a person approves and launch monitoring.
- 04
Maintenance
We track quality and costs, update the test set and respond when the data or the process changes.
When it makes sense
- The support team spends most of its time on repetitive tickets and complaints.
- Documents, contracts or invoices are reviewed manually in large numbers.
- Company knowledge is scattered across documents and hard to find quickly.
- Inventory or staffing planning relies on experience and spreadsheets.
Technologies
Technologies are selected to suit the project and the client's environment. The ones we most often work with:
- Claude
- OpenAI
- open-source models
- RAG
- LangGraph
- Python
- MLflow
- Langfuse
Frequently asked questions
Before the pilot we agree on a success measure, for example ticket handling time or forecast accuracy. The pilot ends with a comparison against the current process.