#01
Automating a process
Your team loses time on repetitive, measurable tasks such as document processing or classification. We first validate whether AI can help at the required quality and an acceptable cost.
SERVICE
Production AI in existing workflows — measurable, governed, observable and safe to operate.
What we deliver
Not a new AI product, but how your existing system and processes can take advantage of AI. Classification, generation, agent-based automation, search & retrieval — added at carefully chosen points, with measurable return.
Process survey, AI use case prioritisation with ROI estimates. What's worth automating and what isn't.
OpenAI, Anthropic, local model, or fine-tuning — the best fit for the task, with cost in mind.
API layer, data flow design, security. Not a separate island — a natural part of your system.
Quality criteria, automated evaluations, human approval points, access control and safe fallbacks.
Cost, quality, latency tracked in real time. Dashboards for leadership and developers alike.
Model and prompt refinement based on real usage data. Continuous improvement, not one-off implementation.
When you need this
#01
Your team loses time on repetitive, measurable tasks such as document processing or classification. We first validate whether AI can help at the required quality and an acceptable cost.
#02
Your system is live, but missing features that are expected today: intelligent search, auto-tagging, personalised recommendations. These don't have to be built in-house — they can be integrated with AI quickly and at scale.
#03
You have a workflow where an AI assistant would help — customer communication, sales follow-up, internal support requests. Not a chatbot, but a deeper, integrated AI that actually does work.
A concrete example
Anonymised, illustrative project example.
Starting point
A Hungarian media company wanted to automate image-tagging in their publishing pipeline.
Week 1
Process survey, prioritisation of top use cases: image-tagging, SEO meta generation, content categorisation. Image-tagging first.
Weeks 2–3
Image-tagging PoC on real articles (Claude Vision API). Measured: high accuracy, fast processing.
Weeks 4–7
Built into the CMS publishing flow, fallback to manual tagging, monitoring dashboard.
Weeks 8+
Cost analysis drove model routing — smaller images to a cheaper model, complex ones to a stronger model.
Outcome
Many images per day automatically tagged, manual work substantially reduced, cost kept low.
Related services
Often these go together — adjacent services we also handle.
Platform Discovery, architecture, engineering and live operation — for systems whose business complexity demands senior technology ownership.
An independent senior read on your architecture — risks, scaling limits, and the smallest changes that unlock growth.
FAQ
That's exactly what the use case audit phase is for. We survey processes, estimate ROI, and pick the top 3–4 use cases to start with.
A focused PoC typically takes 2–3 weeks plus model costs. We give a concrete estimate once the use case is defined.
The PoC validates value and quality while accounting for production constraints. After validation, we build the hardened integration with security, error handling, monitoring and safe fallbacks.
Use-case-specific KPIs (e.g., processing time reduction, correct classification rate) plus model-level metrics (latency, cost, drift). Continuously tracked on a dashboard.
Get in touch
Tell us whether an existing business-critical system is holding you back or you are creating a new complex platform. In a 30-minute conversation we identify the right first engagement and whether we are a fit.
Best fit: multiple roles or tenants, critical integrations, sensitive data, complex business logic, long-term evolution, or direct business impact when the system is unavailable.
Write us about your project. We'll reply within a few days.
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+36 70 545 5832Schedule a call at a time that works for you.
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