The problem

«We want to use AI in a concrete process, not another pilot that stays a demo.»

Connecting a language model takes an afternoon. Making it work with real documents, incomplete data, users with different permissions and a cost budget is a different job, and it is engineering. Most AI projects get stuck in that gap.

What we do

  • Document processing

    Data extraction from invoices, contracts or forms, with checks and a confidence score for every field.

  • Search over your information

    Plain-language questions about your internal documentation, with answers that cite the source and respect who can see what.

  • Classification and routing

    Emails, tickets or requests classified and sent to the right place without manual work.

  • Assisted workflows

    The model prepares the work and a person decides: drafts, reviews, summaries, checks.

  • Agents, when it fits

    Models that take actions in your systems, with explicit limits and human approval on sensitive steps.

  • Evaluation and monitoring

    Real test cases, quality metrics, cost per operation and a record of every decision the system makes.

When it makes sense

  • There is a high-volume process full of unstructured information: PDFs, emails, free text.
  • The company's knowledge exists, but nobody finds it in time.
  • You tried a generic tool and it didn't reach the accuracy the process needs.

What you get

A process that works in production, with measured accuracy, predictable costs and a clear path for when the model gets it wrong.

How we build it

Rules first, models second. If a rule or a deterministic step solves the case, it is cheaper and easier to audit. The model comes in where it adds value, and whatever doesn't reach the required confidence goes to a person.

That's how Alfred thinks: finer and finer stages, with AI exactly where it adds the most. Read how we designed it

Frequently asked questions

Which models do you use?

Whichever solves the case best in quality, cost and privacy. We design the integration so you can switch providers without rebuilding the system.

Will our data end up training third-party models?

No. We use services and settings that exclude your data from training, and we document it before processing sensitive information.

How do you know the AI is working well?

With a set of labelled real cases that we measure before going live and on every change. If accuracy drops, we see it in the metrics, not in a complaint.

Is AI always needed?

No. If a rule or an integration solves the problem, we'll tell you: it is cheaper and easier to maintain.

Let's talk about your project.

Tell us what you need to solve. The first conversation is to understand the problem, not to sell you something.

We reply within 1 business day.