Services

AI integrated where it earns money

Not an AI strategy deck — models embedded in the tools your team already opens every morning.

The productive way to adopt AI is rarely a new product; it is removing specific expensive steps inside processes you already run. Documents that someone reads and retypes, emails drafted from the same three templates, tickets classified by hand, contracts searched by scrolling — these are integration points where a language model, correctly wired into your ERP, CRM or document system, removes measurable hours of manual work. Our Romanian engineering team does that wiring: model selection matched to task and budget, retrieval over your own data where accuracy matters, evaluation suites that prove the accuracy before go-live, and fallbacks to humans where confidence drops. Data handling is designed for European reality — EU processing where required, provider terms reviewed, and your legal team given real answers instead of hand-waving.

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office@northdan.com · +40 752 070 247

What you get

Inside existing systems

AI appears as a feature of tools your team already uses — a button, a pre-filled draft, an auto-tag — with no new interface to learn.

Accuracy proven with evals

We build test sets from your real cases and measure model output against them before and after launch — “it seems to work” is not a deliverable.

Model-agnostic and swappable

Integrations go through an abstraction layer, so switching providers or dropping in a cheaper model next year is configuration, not a rebuild.

Integrations with results we can point at

Extracting structured data from supplier invoices, delivery notes and contracts that arrive as PDFs. Classifying and routing inbound email or tickets. Drafting replies a person edits rather than writes from scratch. Searching years of documents by meaning instead of exact wording. Summarising a long thread before a handover. Each of these replaces a specific, countable manual step.

Before go-live we build an evaluation set from your own historical cases and measure accuracy against it, so the decision to deploy rests on numbers rather than on a good demo. We also design for model change — providers deprecate, prices move, better models appear — by keeping the integration layer independent of any single vendor’s API shape.

Frequently asked questions

Which processes give the fastest AI payback?

High-volume text processing: extracting data from documents, first-draft replies, categorising and routing requests, semantic search over internal knowledge. If people read-then-retype anywhere, start there.

How do you deal with model mistakes in production?

Confidence thresholds, human review queues for edge cases, structured output validation and continuous sampling of results. The design assumption is that the model will sometimes be wrong — the workflow catches it.

Do our documents end up training someone’s model?

No — we use API tiers with contractual no-training clauses, or EU-hosted and self-hosted models where policy demands it, and we document data flows so your DPO can verify rather than trust.