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Vduce Technology
AI development company

AI that ships to production, not to a slide deck.

We design and build AI features and agentic workflows for companies that want measurable results: fewer manual steps, faster answers, better decisions. From retrieval-augmented assistants over your documents to computer-vision pipelines like our YOLOv8 plate recognition, we engineer AI like any other critical system: tested, observable and secure.

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At a glance

Typical timeline
AI sprint: 2 weeks. Production rollout: typically 6–12 weeks.
  • Claude
  • OpenAI
  • Mistral
  • Llama
  • YOLOv8 / YOLO11
  • EasyOCR
  • Python
  • FastAPI
Who it is for

For SMEs and enterprise teams who have identified a costly manual process, a knowledge bottleneck or a camera/sensor feed that nobody has time to watch, and who need a partner who can take AI from proof of concept to daily operations.

Problems we solve

  • Your team copies data between emails, PDFs and systems by hand.
  • A ChatGPT pilot impressed everyone, but nobody trusts it with real customer data.
  • You are unsure which data may leave the EU, and legal is blocking the project.
  • Earlier AI experiments never made it past the demo because nobody measured quality.
  • Video or sensor data is collected but never analysed in time to act.
What you get

AI & Agentic Automation

AI opportunity assessment

A ranked list of use cases with effort, risk and expected value, based on interviews and a look at your real data.

RAG assistants

Assistants that answer from your documents, tickets or wiki with citations, access control and a feedback loop.

Agentic workflows

Agents that read, decide and act through your APIs (create tickets, update CRM records, draft replies) with human approval where it matters.

Document intelligence

Extraction and classification of invoices, delivery notes, forms and contracts into structured data.

Computer vision

Detection and recognition pipelines (YOLO, OCR) for cameras at gates, production lines or facilities.

Evaluation & guardrails

Test sets, quality metrics, prompt-injection defences, cost monitoring and audit logs before anything goes live.

Our approach

AI development company

  1. 01

    Pick one measurable process

    We start with a process where success is countable: minutes saved, tickets closed, errors caught. No vague 'AI strategy'.

  2. 02

    Two-week AI sprint

    A working proof of concept on your data, with an evaluation set, so you decide on numbers rather than impressions.

  3. 03

    Harden and integrate

    Authentication, permissions, logging, fallbacks and integration with the systems your team already uses.

  4. 04

    Operate and improve

    Dashboards for quality, latency and cost, plus a regular review where we retrain, re-prompt or switch models.

Why most AI projects stall, and how we avoid it

Most AI pilots fail for boring reasons: nobody defined what "good" means, the data was messier than expected, or the security review came too late. We address all three on day one. Every project gets an evaluation set, a small collection of real inputs with the expected outputs, so we can measure every change. We look at your data before we promise anything. And we involve your data-protection officer early, with a clear diagram of where data flows and which processor handles it.

Agentic automation, explained without hype

An "agent" is software that uses a language model to decide which step to take next, then calls a tool, such as your ticket system, ERP or email, to do it. Agents are powerful for workflows with many small decisions: triaging support requests, preparing quotes, reconciling documents. They are risky when they can take irreversible actions without oversight. Our agents work with explicit permissions, human approval for sensitive actions and a full audit trail, so your team stays in control.

Computer vision we have actually shipped

AI is not only chat. For a smart-parking module we built an ALPR system that detects licence plates with YOLOv8, reads them with EasyOCR and raises real-time alerts for unauthorised vehicles through AWS SNS. The same pattern (detect, recognise, decide, notify) applies to safety equipment checks, occupancy counting and quality inspection.

Your data stays where it should

For German and EU companies we offer three hosting options: EU-region managed models (Azure OpenAI in Sweden or Germany, AWS Bedrock in Frankfurt), self-hosted open-weight models on EU servers, or fully on-premise for sensitive environments. Read more in our guide to self-hosted AI for German companies.

Book a 2-week AI sprint

Related case studies

Similar project? Let’s talk

Client confidential · For 4CT · Smart DigiBuild parking module

Real-time licence plate recognition for smart parking

  • Python
  • YOLOv8
  • EasyOCR
View case study

Four Corners Technology (4CT)

Smart DigiBuild: a smart building platform developed for Four Corners Technology

  • NestJS
  • Next.js
  • Flutter
View case study
Further reading

AI & Agentic Automation

The EU AI Act for SMEs: what applies when

Compliance4 min read

The EU AI Act for SMEs: what applies when

Which AI Act rules already apply, what the 2026 Digital Omnibus postponed, and a practical checklist for small and mid-sized companies that use or build AI.

RAG explained: AI assistants over your own documents

AI4 min read

RAG explained: AI assistants over your own documents

How retrieval-augmented generation lets an AI assistant answer from your manuals, contracts and tickets, what it costs to get right, and how to keep the data GDPR-compliant.

Self-hosted AI for German companies: keeping data in the EU

AI5 min read

Self-hosted AI for German companies: keeping data in the EU

Managed EU models, open-weight models on German servers or fully on-premise? A practical guide to AI hosting options, GDPR, the EU AI Act and what each option costs.

Frequently asked questions

  • Do we need a lot of data to start with AI?

    Usually not. Modern language models work well with your existing documents and a few hundred good examples for evaluation. For computer vision, public datasets plus a few thousand of your own images are often enough for a first model.

  • Is ChatGPT allowed under GDPR?

    It depends on the contract, the data and the hosting region. Enterprise offerings with EU data residency and a data processing agreement can be used compliantly; consumer accounts usually cannot. We document the data flow so your DPO can decide.

  • What does the 2-week AI sprint include?

    A kickoff workshop, access to a sample of your data, a working prototype for one use case, an evaluation report with accuracy and cost per request, and a recommendation: build, adjust or stop.

  • Which model do you use?

    The one that fits your quality, cost and data-residency needs. We build model-agnostic so you can switch between Claude, OpenAI, Mistral or self-hosted models without rewriting the application.

  • How do you prevent hallucinations?

    By grounding answers in retrieved sources with citations, restricting the model to what it may answer, testing against an evaluation set and showing uncertainty to users instead of guessing.

  • Can AI run on our own servers?

    Yes. Open-weight models such as Llama or Mistral run on your own GPU servers or a German hosting provider. We size the hardware and set up monitoring.

Let’s talk about your AI & Agentic Automation project.

A 30-minute call with a senior engineer. You leave with a clear next step, whether you work with us or not.

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