SPECIALIZATION · REAL AI DEPLOYMENTS

AI in e-commerce, not in marketing presentations.

Most agencies talk about AI in the future tense. We have three specific AI deployments running with real clients today — self-checkout assistant, object recognition as a search engine, catalog automation. Each solves a specific business problem with measurable ROI.

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Production AI deployments

Real cases

Not technology demos

Measurable

Business ROI for each

01 How we work on AI deployments

Five steps, from business hypothesis to production.

We don't deploy AI for the sake of AI. Every AI project starts with a business hypothesis — what problem we're solving, what the measurable KPI of success is. Only then we choose the technology and build the deployment.

Business hypothesis and use case definition

OpenAI GPT-4 · Omnichannel · Implemented for 1 retail client

A customer stands at a self-service checkout, can't find a product, has a question about a promotion, or can't scan the code. Instead of asking an employee, they use an AI assistant that has access to the product database, price lists, promotions, and order status. Reduction of employee support time by about 60% in the first months. Implementation based on OpenAI GPT-4 plus custom product catalog embedding. Price range 60-120 thousand PLN depending on the store's scale.

Selection of AI technology (LLM, vision, NLP)

Vision models · Implemented for 2 D2C clients

The client takes a picture of the product (e.g. furniture, clothing, accessories) they want to buy. AI recognizes the object and categorizes it, then searches for visually similar products in the store's catalog. Conversion increase of around 12-18% in stores with a rich visual catalog. Implementation based on vision models (CLIP or equivalent) with embedding of the entire product catalog. Price range: 80-160 thousand PLN.

Prototype and validation on real data

LLM + store data · Implemented for 1 B2B client

The store owner wants to ask in Polish "Show me the 10 most profitable products of the last 30 days with YoY trends". Instead of writing SQL queries or clicking through 4 reports, they use an AI assistant with access to sales data. Democratization of data — analytics available without the mediation of the IT department. Implementation based on LLM (Claude/GPT-4) plus integration with the orders, customers, and products database. Price range: 100-200 thousand PLN.

02 Our approach to AI

Why our AI implementations work where others' demos fail.

AI is not a magical ingredient added to every problem. It's a tool that solves specific business problems — and only then does it make sense to implement it. Our approach to AI is pragmatic, not trendy.

What we do in AI for e-commerce

Data audit and use case identification — we start with your data, not technology. We map business problems that AI can realistically solve.

Production-ready implementations — not demos, not PoCs. Solutions that work for clients every day, with monitoring, alerts, and SLA.

Integration with the existing stack — AI enters your store (Sylius, PrestaShop, Shopify, WooCommerce), ERP, and POS. We don't build separate islands.

What we do NOT do

We don't implement AI for the sake of AI — if a problem can be solved with a simpler tool, we'll tell you straight away.

We don't promise magical results — AI is a tool, not a magic wand. We provide real ROI based on data, not marketing promises.

We don't build FAQ chatbots — the market is full of cheap chatbots. We solve problems that a chatbot won't solve.

03 AI FAQ

AI questions we hear most often.

How much does AI implementation in a store cost?

It depends on the use case. A self-service checkout assistant costs 80-150 thousand PLN. Visual search costs 40-90 thousand PLN. LLM Business Analyst costs 60-200 thousand PLN. Each implementation starts with a paid data audit (5-15 thousand PLN), which shows the real ROI before you invest in a full implementation.

Do I need a lot of data for AI to make sense?

Yes, but less than you think. For visual search, a product catalog with images is enough. For LLM Business Analyst, you need at least 6 months of order data. For a checkout assistant, a POS product base is required. A data audit shows whether your data is sufficient.

What AI models do you use?

We choose a model based on the problem, not the other way around. OpenAI GPT-4 / GPT-4o for NLP and assistants. Vision models (CLIP, DINOv2) for image recognition. Claude / GPT-4 for data analysis. Open-source models (Llama, Mistral) when data cannot leave the client's infrastructure.

How do you measure ROI from AI?

Before implementation, we define specific business KPIs: checkout throughput, search conversion, report generation time. After 90 days, we compare the metrics before and after. If the ROI is below the assumed threshold, we optimize or recommend withdrawal.

Will AI replace my employees?

Not in our approach. We implement AI as a supporting tool, not a replacement. The cashier assistant relieves staff, but does not eliminate them. The LLM Business Analyst accelerates analysis, but the decision is made by a human. AI does what it is good at — processing data on a scale that a human cannot handle.

FIRST STEP

Your AI deployment starts with a business hypothesis.

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