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Fona

Service

Applied AI

We bring language models into processes that already exist in your company, connected to your data and with the evaluation needed to know whether they actually work.

Let's talkSee case studies
Abstract illustration of a neural network with nodes connected in layers

The problem

Between scepticism and hype there is engineering work

The demo impresses, the pilot stalls, and nobody knows what it costs or how often it is right. The distance between an experiment and a production system is the same as in any other software: data, evaluation and operations.

Signs this is you

  • Pilots that never reach production
  • Answers that sound right but invent facts
  • Cost per query unknown until the invoice arrives
  • Legal doubts about what information leaves the company
  • Every team using a different tool, with no shared criteria
  • Repetitive work still done by hand because nobody modelled it

What we do

What we do

Use case identification

Sessions with your teams to find where AI adds something measurable: high volume, fuzzy rules and a bounded tolerance for error. We come out with three or four candidates ranked by impact and effort.

Assistants grounded in your data

Search and answers over internal documentation, catalogues, regulation or incident history, with citations back to the source so every answer is verifiable.

Process automation

Document classification and extraction, email triage, draft writing, quality checks on records. Embedded in the tools the team already uses, not on a separate website.

Adoption and governance

Hands-on training, usage criteria, data policy and tool selection. So the whole company moves, not just the one person who took it up as a hobby.

1-2 wks
from discovery to prioritised use cases
€/op
cost per operation measured before scaling
GDPR
data handling defined at design time

How we do it

How we do it

  1. Discovery

    One or two weeks mapping processes, volumes and available data. We rule out what will not pay off before writing any code.

    DeliverablePrioritised use cases

  2. Evaluated prototype

    We build the winning case against a real test set: accuracy, cost per operation and latency measured rather than guessed.

    DeliverablePrototype and quality metrics

  3. Into production

    Integration into the real workflow, cost controls, usage limits, decision logging and an escalation path to a human when the model is not confident.

    DeliverableSystem in production

  4. Measure and tune

    We review the failed cases, adjust and extend. The quality of an AI system is maintained, not delivered and forgotten.

    DeliverableMonthly report and improvements

Technologies

  • LLM APIs
  • RAG
  • MCP
  • Python
  • Node.js
  • PostgreSQL
  • Vector databases
  • Automated evaluation

FAQ

What people ask us

Will our data end up training somebody else’s model?

Not if it is designed properly. The enterprise plans of the major providers do not train on API data, and when a case demands it we work with models hosted on your own infrastructure. That decision is made at design time and written down.

How do you stop the system from making things up?

By grounding answers in your documents, requiring citations, measuring accuracy against a set of real cases, and defining when the system must say "I don’t know" and hand the question to a person.

Is it expensive to run?

We measure cost per operation in the prototype, before scaling. From there you decide with data: sometimes a large model is worth it, and often a small, well-tuned one solves the same problem for a fraction of the price.

Could you train our team instead of doing it for them?

Yes, and it is often the better investment. We run a dedicated training service for technical and business teams, with material built around your tools and processes.

Next step

Half an hour well spent

Walk us through the problem on a short call. You leave with a first read on how we'd approach it and what it would involve — no commitment, no sales deck.

Let's talkHow we work