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THREE PRINCIPLES

How we make AI useful, safe and production-ready

Our approach is based on a pragmatic mix of AI, classic engineering, process understanding and delivery ownership

AI as a tool, not the goal

We do not force AI into every problem. We combine language models, structured data, deterministic logic, integrations and classic algorithms to design solutions that are reliable enough for business use.

Human in the loop by design

We build control into the process from the beginning. AI can accelerate work, but sensitive decisions, exceptions and quality checks are designed with human supervision and clear approval paths.

Value over price competition

We do not compete on the lowest hourly rate. Our advantage is scope, speed of value delivery and responsibility for the solution after it leaves the prototype stage.

AI should reduce operational friction, not create a new layer of complexity

We connect AI with the reality of commerce operations

In e-commerce, AI only matters when it improves a real process: product enrichment, sales support, customer requests, content operations, offer preparation, logistics or system integrations. We start with the business problem and build AI into the architecture where it creates value.

We move fast, but not blindly

Rapid prototyping helps clients see a working solution before committing to a larger implementation. This reverses the classic sales process: the client sees the system in action first, then decides what is worth developing.

We keep accountability on the human side

We design AI systems with human validation, control points and clear ownership. AI can classify, suggest, enrich and automate. The final responsibility for business logic, quality and production behaviour stays with the team that builds and operates the solution.

AI services built around commerce use cases

From a first working prototype to production integrations, AI apps, automation layers and team enablement.

AI RAPID PROTOTYPING

Working AI prototypes for e-commerce sales and operational processes

We build functioning AI prototypes for sales, customer enquiries, offer analysis, product data workflows, support teams and e-commerce logistics. The goal is simple: show how the system works in practice before the client commits to a wider implementation.

Within a few working days, we create a proof of concept, demonstrate real behaviour and iterate day by day based on client feedback. The result is a tangible prototype that helps the client decide what works, what needs improvement and what is worth scaling.

Customer request analysis and classification
Sales and offer support for e-commerce teams
AI-assisted customer service workflows
Operational prototypes for logistics and product data processes

Key advantages

Business case

We select a process where speed, quality or scale can improve.

Prototype

We build a working version instead of a long theoretical document.

Feedback loop

Users test, react and help us improve the system daily.

Decision

The client evaluates real output and chooses the next step.

AI AUTOMATION & AI APPS

Adaptive automations and micro-apps for daily operations

We build next-generation automations that go beyond rigid rules. AI helps handle ambiguity, context and edge cases that usually block classic workflows.

Smart product image assignment

Automated product data enrichment

Micro-apps for specific operational needs

AI workflows connected with PIM, ERP, DAM and e-commerce platforms

AI CONSULTING & DISCOVERY

AI potential mapped to real business processes

We analyse the company, processes and operational structure to identify where AI can create measurable value. Instead of a long analysis that never becomes an implementation, we move quickly from diagnosis to tested hypotheses.

High-level process and operational analysis

Identification of AI quick wins

Fast prototypes to verify assumptions

A practical implementation roadmap based on evidence

AI INTEGRATIONS & CONNECTORS

AI as an intelligence layer between business systems

We build connectors between client systems and add AI as a decision-making layer in data flows. The aim is not to create a separate AI experiment, but to improve everyday commerce, content and operational processes.

PIM - e-commerce - DAM integrations

AI enrichment for product data flows

Validation, classification and routing of operational data

Monitoring, maintenance and incident response after go-live

AI GROWTH PROGRAM

Training, tools and backlog generation for client teams

We help business, marketing and operational teams understand where AI fits their work. The programme focuses on real industry use cases and turns ideas into a prioritised backlog that can be estimated and implemented.

Workshops for non-technical teams

Industry-specific AI use cases

AI initiative backlog generation

Optional tools for managing AI ideas and initiatives

DELIVERY MODEL

From discovery to production without losing momentum

We combine consulting, rapid prototyping and production engineering so that AI initiatives do not get stuck between a workshop and a slide deck

Discover

We analyse processes, systems and operational bottlenecks to identify where AI makes commercial sense

Prototype

We build a working proof of concept quickly, using real examples and feedback from future users

Integrate

We connect the solution with the existing commerce ecosystem: PIM, ERP, DAM, e-commerce platforms and operational tools

Operate

We support the solution after launch with monitoring, maintenance and response to production incidents

THE ROLE OF STRIX

We do not expect the client to be the AI expert

Our role is to guide the client through the areas where AI makes sense for their business and deliver effects that can be measured.

AI-generated code has become widely available. What really matters is whether the system works correctly and safely, whether business assumptions are achieved and who takes responsibility for the production environment.

PRODUCTION RESPONSIBILITY

Responsibility means more than delivering a demo

Designing AI workflows that fit the business context, not only the technical architecture.

Building integrations that work in the client’s production environment.

Monitoring, maintaining and reacting to errors after deployment.

Keeping human validation and clear accountability where business decisions require control.

Improving data quality, operational speed and process scalability in measurable ways.

Contact

Let's talk about the possibilities that we can offer your company.