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The Developers
// service 03 — AI implementation New

AI that actually saves time.

No demos that look impressive but go nowhere. We deploy AI exactly where your team loses time today on repetitive work — admin, customer queries, document handling — and we measure whether it actually helps.

// applications

Where AI moves the needle.

Six concrete tracks we build for SMBs today. Not everything at once — we pick the approach with the biggest time win for your situation, together.

// 01 workflows.svc

Workflow automation

Repetitive admin — drafting quotes, sending emails, keeping schedules, retyping data — running on its own. What costs half a day per week goes down to minutes.

// 02 chatbot.svc

Custom chatbots

For customer support, sales assist or internal use (HR questions, IT helpdesk). Answers FAQs 24/7, escalates what it doesn’t know — and learns from your own documentation.

// 03 docs.ai

Document processing

Incoming invoices, contracts, purchase orders or forms read automatically, the right fields extracted. Classic OCR + modern AI — works even on badly scanned PDFs.

// 04 rag.search

Knowledge assistant (RAG)

Your own ChatGPT, but grounded in your company documents: manuals, procedures, product info. Answers with source citations. No hallucinated facts.

// 05 sales.ai

Lead qualification & email drafts

Incoming leads get classified, email replies prepared, CRM updated. Your sales team only sees the leads that matter, with context ready to go.

// 06 predict.io

Predictive insights

Demand forecasting, inventory, churn — built on your own historical data. Decide ahead, not on gut feel or on last month’s numbers.

// approach

Start small. Measure fast. Scale where it works.

The biggest AI pitfall: building for months on something that doesn’t land in production. We work in short iterations with a measurable goal per phase.

// 01

Quick scan

We map your work processes. Where does your team lose time? Which tasks are repeatable enough to hand off to AI?

// 02

Pilot in 2-3 weeks

One concrete process. A working prototype your team can use — not a PowerPoint.

// 03

Integrate & roll out

Wire it into your existing tools (inbox, CRM, ERP). Gradual rollout, first alongside the old process, then replacing it.

// 04

Monitor & refine

Measure accuracy, correct wrong answers, retrain on real usage data. AI only improves with that feedback loop.

// principles

Our AI rules.

Not every problem is a good fit for AI. And even where it does fit, a few things need to be clear upfront.

Privacy first

Your company data is never used to train public models. We use European providers where possible (Mistral, Azure OpenAI EU, or self-hosted), and we’re transparent about where data goes.

Human in the loop, until it can work without one

AI suggests, your team decides — until we have enough measurable confidence that it can run unsupervised. No "AI handles it, fingers crossed".

Not everything is AI

Sometimes a good search query, a template or a simple automation is cheaper and more reliable. We say so when AI isn’t the right answer.

Your data stays yours

No vendor lock-in on the AI model. We build so you can switch providers without the system breaking — models replace each other every six months these days.

Measurable ROI, not a "wow" factor

For every project we define upfront what counts as a win: minutes saved per week, lead times, accuracy, customer satisfaction. No measurable win within three months? We pull the plug.

GDPR and business risk

AI touches personal data, IP and liability. We help you through the DPIA, clearly flag which AI output reaches customers, and build audit trails where needed.

// who for

Is this a fit for your business?

AI pays off mostly where there’s repetitive work. One client with a unique problem is usually handcraft — a thousand customer queries that look alike, that’s an AI case.

// yes, for you

  • A lot of incoming emails, forms or customer queries
  • Stacks of invoices, contracts or files processed by hand
  • Internal team answering the same questions again and again
  • Historical data that’s nowhere put to work

// less of a match

  • Volume so low manual work stays cheaper
  • Use cases where 100% accuracy is legally required (medical, financial advice)
  • "We want to do something with AI" without a concrete problem

// let’s talk

Curious where AI could save you time?

A 20-minute call usually surfaces a few concrete tracks. We’ll also be honest where it wouldn’t be a good investment.