The situation
AI is on everyone's agenda, but most projects stall at the demo. The reasons are usually the same: the job being solved was never defined, nothing is measured, and nobody decided what happens when the model is wrong.
→Our approach
We start with one job — reading an incoming document, routing a request, summarising a long record. We measure accuracy, build a path that hands off to a person when confidence is low, and work out the running cost up front. We don't move to a second use case until the first one has proven its value.
Who it's for
- Teams processing high volumes of documents, forms or requests
- Companies whose support load keeps growing on repeat questions
- Organisations that need search and summarisation over their own data
- Product teams adding AI features to an existing product
What's included
- A defined task and an agreed measure of success
- Model selection with a cost model
- Answers grounded in your own data
- A hand-off path to a human when confidence is low
- Accuracy and cost monitoring
- Data privacy and GDPR/KVKK compliance
Process
Choosing the task
We pick one job where AI genuinely changes the outcome and write down how success will be measured.
Small-scale trial
A limited run on your real data. If accuracy isn't good enough, we stop here.
Building
We wire the workflow into your systems with human review and a failure path.
Monitoring
Accuracy and cost are tracked, and the model or approach changes if the numbers say so.
Common questions
How would AI fit into our business?
The healthiest start is one job. Classifying incoming email, extracting data from invoices, summarising long meeting records, answering common questions from your own documentation. We pick one, measure how accurate it is, and expand only if it earns its place.
Is our data used to train models?
No. Enterprise APIs don't feed data into training, and we configure those settings explicitly. You get a written record of what data goes where; if sensitive data must never leave your environment, we can run that part on your own infrastructure.
What happens when the model gets it wrong?
We never let a model decide a critical step alone. When confidence is low the work goes to a person, and every answer shows the source it came from. Accuracy is measured and reported continuously.
Are the running costs predictable?
Yes. We model cost per use up front and set a monthly ceiling. Because we measure real usage during the trial, there are no surprises when you go live.
Related services
- 05Process & AutomationWe hand the work your team repeats every day over to software.
- 01Custom Software DevelopmentInstead of bending your business to fit a product, we build the software around how you already work.
- 07API & System IntegrationWe connect the systems that currently only talk through a person copying between them.
If you have a question, let us start there.
Tell us what you are trying to do. On the first call we will tell you whether we are the right fit, roughly how long it takes and how we would approach it. No sales pitch.
orsenyazilim@gmail.com