Clever Goat

SERVICE

Reliable AI Integration

Production AI in existing workflows — measurable, governed, observable and safe to operate.

Let's talk about your systemWe'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.

Evaluation & safeguards

Quality criteria, automated evaluations, human approval points, access control and safe fallbacks.

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.

#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.

#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.

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?

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.

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

Let's understand the problem before we build the solution.

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.