#01
Automating a process
Your team loses time on repetitive tasks that AI could handle faster and more accurately: document processing, email triage, customer replies. You don't need a new product — just to speed up the existing workflow.
SERVICE
AI integration
Adding AI features to existing systems — workflows, automation, tools.
Let's talk about your projectWe'll reply within a few days with a concrete proposal.
What we deliver
What this looks like in practice.
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.
Use case audit
Process survey, AI use case prioritisation with ROI estimates. What's worth automating and what isn't.
Model & infra selection
OpenAI, Anthropic, local model, or fine-tuning — the best fit for the task, with cost in mind.
Integration into your system
API layer, data flow design, security. Not a separate island — a natural part of your system.
Prompt engineering
Structured prompt design, evaluation harness for automated tests, A/B testing.
Production monitoring
Cost, quality, latency tracked in real time. Dashboards for leadership and developers alike.
Iteration
Model and prompt refinement based on real usage data. Continuous improvement, not one-off implementation.
When you need this
This is where we come in.
#02
AI features in existing system
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
AI agent in workflows
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
What an engagement looks like in practice.
Anonymised, illustrative project example.
Starting point
A Hungarian media company wanted to automate image-tagging in their publishing pipeline.
Week 1
Audit
Process survey, prioritisation of top use cases: image-tagging, SEO meta generation, content categorisation. Image-tagging first.
Weeks 2–3
PoC
Image-tagging PoC on real articles (Claude Vision API). Measured: high accuracy, fast processing.
Weeks 4–7
Integration
Built into the CMS publishing flow, fallback to manual tagging, monitoring dashboard.
Weeks 8+
Optimisation
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.
FAQ
Common questions
How do we know which AI use case to start with?
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.
What does a PoC cost?
A focused PoC typically takes 2–3 weeks plus model costs. We give a concrete estimate once the use case is defined.
How does the PoC reach production?
We build PoC code to be production-suitable — security, error handling, monitoring built in. After PoC validation we integrate into your existing system in the next 2–6 weeks.
How do we measure whether the AI solution is working?
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
Have a project in mind?
Let's talk about it. We'll reply within a few days, and a 30-minute call will tell us whether we're a fit.
Send an Email
Write us about your project. We'll reply within a few days.
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