Eighteen ways in. One method.
Consulting, build, agents, retrieval and the data work underneath, each scoped to a decision you already face.
what we have running
how quickly value shows up
how many clients come back
in the AI practice
Core AI
Where an AI programme starts — the strategy, the build, the integration into systems you already run, and the governance that keeps it defensible.
Generative AI & Agents
Language models put to work: copilots, retrieval over your own documents, agents that carry out multi-step tasks, and models tuned to your domain.
Machine Learning & Applied AI
Models that predict, classify and perceive — pointed at the specific decisions and processes your operation repeats every day.
Data & Analytics
The layer everything above depends on: the pipelines, the warehouse, the reporting, and the strategy that decides what is worth measuring.
Delivered with the Aurvik Method
Five phases from business problem to production AI — an evaluation gate at the end of every one.
Define
1 to 2 weeks
The business outcome, the metric, and who owns the decision it supports.
You getA scoped problem statement and a success metric
Audit
1 to 2 weeks
Data readiness, permissions and the honest gap between them and the goal.
You getA data readiness report and a risk register
Architect
1 to 2 weeks
System design, model strategy and the evaluation approach.
You getArchitecture decision records and an evaluation plan
Build and evaluate
4 to 8 weeks, depending on scope
Implementation scored against the evaluation set at every change.
You getA working system with a full evaluation history
Deploy and optimise
1 to 2 weeks to release, then an ongoing optimisation window
Production release, monitoring and the handover to your team.
You getA deployed system, monitoring and trained operators
Let's pick one workflow and prove it.
Bring the decision that still gets made on instinct. We'll scope it, agree the metric it has to move, and show you what reaching it takes.