Long documents, read in minutes
Tender books, contracts or regulations summarised, with the requirements and risks pulled out.
ExampleLicitor: AI analysis of tender documentation
AI agents & assistants
Agents and assistants that read documents, answer from your own knowledge and draft the routine — inside your systems, with every important decision approved by a person.
Situations
Not chatbots for the sake of it. Jobs that take people hours and follow a pattern.
Tender books, contracts or regulations summarised, with the requirements and risks pulled out.
ExampleLicitor: AI analysis of tender documentation
Find the right record even when people describe it differently than it was written.
Examplesemantic search in Licitor
Incoming messages classified, routine ones answered as drafts for a person to approve.
New colleagues ask the assistant instead of interrupting the senior ones — answers cite the source.
Fields extracted from documents into your systems, with a person checking anything uncertain.
Consistent written assessments generated from measured data, reviewed before they go out.
ExamplePrestigiu: website audits on 8 criteria
What we work with
We pick the model by task — a fast, cheap one for sorting, a strong one for analysis — and track what every request costs.
When it is not worth it
If a plain rule solves the problem, we do not use AI: it is cheaper, faster and fully predictable. We use AI where rules break down.
In production
A few of the systems where this already works, every day.
Public tenders aggregator with AI analysis — over 20 million tenders, updated daily.
Prestigiu score 9.3/10 · verify it (opens in a new tab)Automated website audit on 8 criteria in 30 seconds — in Romania and, as appfox.com, in the US.
Prestigiu score 9.7/10 · verify it (opens in a new tab)Psychologists and psychotherapists across Romania, checked against the official register.
Prestigiu score 9.8/10 · verify it (opens in a new tab)Questions
We use business APIs whose terms exclude training on your requests, and we only send the data a task needs.
That is why a person approves anything important, and why we test on your real cases before launch.
It depends on volume and model. We estimate it before we start and track the real cost per request afterwards.
Next step