A good fit for
- Teams testing a GenAI use case
- Products unlocking value from internal knowledge
- Business workflows with repetitive tasks
02
Expected outcome
An assistant, augmented search or automation evaluated in real conditions.
Generative AI matters when it improves a journey or a decision, not when it adds a novelty layer. We start with real work, available data and an observable quality threshold.
Responses are evaluated, limitations made visible and costs tracked from the prototype. The result is a controlled product capability, not just a demo.
Deliverables
- Use-case and data-readiness assessment
- RAG, assistant or automation prototype
- Evaluation set and safeguards
- Quality, limitation and cost measurement
Process / 01 to 04
A four-stage journey
Qualify the value
Prepare the data
Prototype and evaluate
Secure the integration
FAQ
Frequently asked questions
01Which AI use case is worth prototyping?+
A strong candidate concerns frequent, costly or hard-to-automate work, has usable data and can be evaluated against a concrete quality criterion. Framing removes use cases where AI would mainly add risk.
02How do you measure the quality of an assistant or agent?+
We build a representative evaluation set, track important errors, relevance, cost and latency, then test the system inside the real workflow. A convincing demonstration is not enough.
03Is data sent to a public model?+
The architecture depends on data sensitivity and organisational constraints. Data flows, providers, retention policies and safeguards are made explicit before integration.
An idea to validate?
Let’s start with proof.
Tell us about the problem. We’ll come back with a clear first take, with no jargon or detours.
Talk to ZEDEX