Applied AI

Service 02

We start with a number for “good,” then the architecture, then the monitoring. Retrieval, fallbacks, and a human loop when the model should stay quiet. If we cannot measure it, we will not ship it as production.

Problems we take on

• The demo dies on the first messy, real question.
• There is no way to score model output before a release.
• Hallucinations are treated as a prompt tweak.
• Spend on models with no account of whether the product is better.

How we work

1. 01

   Define

   What “good” means in a metric a stakeholder can refuse.

2. 02

   Evaluate

   Datasets, automated scoring, and a harness on every change.

3. 03

   Architect

   Retrieval, fallbacks, and the human loop when the model should not answer.

4. 04

   Observe

   Drift, latency, and quality on a dashboard.

5. 05

   Improve

   Versioned prompts, structured feedback, and rollbacks.

Capabilities

• LLM evaluation and benchmarking
• RAG design and optimisation
• Agent architecture
• Prompting and fine-tuning strategy
• AI observability
• Knowledge platform development
• Safety and reliability frameworks

Outcomes

• AI in production with a quality line you can point at.
• Release checks measured in hours, not review meetings.
• Evaluation a non-specialist can read.
• Retrieval that grows with the corpus.