Use-case assessment
We assess whether the task needs AI or whether simpler automation is enough. We review data quality, the risk of errors and potential time savings.
AI for business workflows
Find out whether AI can help your business.
We choose a task you want to improve and review the available data. A prototype lets us assess output quality, time spent on the task and costs before deciding on deployment.
Starting point
How can we help?
Assistants & search
We connect language models to company documents and systems. Answers include source references, and data access follows user permissions. We also define when a person should take over.
Forecasting & classification
We assess models for anomaly detection, document classification and forecasting. The approach depends on the problem and available data.
Deployment & control
We test answer quality, access controls and resistance to attempts to override model instructions. We monitor costs and errors and define which actions need human approval.
First engagement
Workshop: is AI worth implementing?
After the workshop, you receive a recommendation: build a prototype, use simpler automation or keep the current process.
- Scope
- Review of a selected workflow, a data sample, user needs and constraints.
- What you receive
- A feasibility assessment, a list of risks and quality criteria. If a prototype is warranted, we propose its scope and cost.
- Timing
- Preparation time and workshop duration depend on the scope. We usually plan 2–4 weeks for prototype development once the necessary data and access are available.
- Pricing
- Depends on workflows, data preparation and integrations. We separate workshop and prototype fees from estimated model usage costs.
Illustrative use cases
What can a prototype test?
Examples of possible applications and how to assess them. These are illustrative scenarios, not client case studies.
Customer-support assistance
- Problem
- An agent searches for answers across multiple sources.
- Approach
- An assistant drafts an answer with documentation references; the agent approves it.
- What we measure
- Handling time, answer accuracy and the proportion of answers needing correction.
Document analysis
- Problem
- A team manually reviews contracts or operational documents.
- Approach
- A tool highlights information and source passages for a specialist to review.
- What we measure
- Review time, missed information and faithfulness to the source.
Anomaly detection
- Problem
- Irregularities in data are difficult to spot in time.
- Approach
- A model flags unusual events for the team to verify.
- What we measure
- Alert precision, false alarms and time available to respond.
Deployment decision
We test the prototype on your data.
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Baseline
We measure how long the task takes and review how it is done. We agree on a data sample and expected test results.
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Prototype
We build a prototype for the selected task and make it available to an agreed group of users.
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Evaluation
We compare quality, time, cost and risks against the agreed baseline.
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Decision
We agree to deploy, change the approach or stop at this stage.
What if the prototype does not meet expectations?
You receive the test results and our recommendation. You can stop at this stage without commissioning a deployment.
How do you handle data and security?
Before starting, we agree which data can be used, who can access it and where the solution will run. We test security within the agreed scope and discuss legal questions with the people responsible for them in your company.