Use-case assessment
We compare AI with simpler automation, looking at available data, the cost of errors and potential time savings. This informs the decision to build a prototype.
AI for business workflows
Test the value of AI on your own data.
We start with one task: finding information, analysing a document or detecting an anomaly. We build a prototype and measure output quality, task time and running costs. You then decide whether deployment is worthwhile.
Starting point
From use-case assessment to deployment.
Assistants & search
We design assistants that use company documents and systems, with attention to answer sources, data permissions and situations that need a human decision.
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 a prototype worth building?
After the workshop, you know which task is worth testing, where the data gaps are and what risks to consider. We may also recommend simpler automation or keeping 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
Compare the results with your current process.
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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.