Use-case discovery & prioritisation
Working with each department we surface concrete use cases and score them by impact and implementation difficulty. The output is a sequenced roadmap, not a list of forty ideas.
AI & LLM
Deciding where to apply AI is harder than applying it. We examine your processes, prioritise the use cases with genuine return, train your team and set up your internal usage policy — vendor-independent.
Working with each department we surface concrete use cases and score them by impact and implementation difficulty. The output is a sequenced roadmap, not a list of forty ideas.
We work out whether an idea is solvable with AI at all, what the monthly token and infrastructure cost would be, and how long it takes to pay for itself. We are equally direct about the ones that will not work.
Closed model, open-weight, or hybrid? Which provider, which contract, which data-processing terms? We compare independently and document the decision with its reasoning.
What may staff put into which tool, which outputs require human sign-off, how is confidential data handled? We write a usage policy that is enforceable and auditable, aligned with KVKK and the EU AI Act.
Strategy sessions for leadership, hands-on prompt and tooling workshops for teams, LLM integration workshops for developers — on site, using examples taken from your own work.
We implement the chosen use case in a bounded pilot, define success metrics up front and report the result in numbers. The decision to scale rests on data.
01
One to two days of sessions mapping processes, data assets and pain points.
02
Use cases, priority order, estimated cost and return, plus risk and compliance notes — delivered in writing.
03
We start with the highest-return, lowest-risk use case. Timeline and budget are fixed from the outset.
04
What works moves to other units, and your team is trained to take the process over.
No. We have no sales partnership with any model provider; our recommendation follows your requirements and our measurements. Sometimes that means a local open-weight model, sometimes a commercial API.
Yes, and it often pays off faster. Decision cycles are short in small teams, so measurable gains within a month are realistic. We scope the engagement to your budget.
Fine-tuning, LoRA/QLoRA, instruction tuning, DPO and evaluation
Models that run on your own servers and keep data in-house
Answers grounded in your own documents, with citations
In a short call we listen, then tell you plainly which approaches fit your situation and which do not. We reply within 24 hours.