AI Automations – practical implementations for eCommerce and business
AI makes sense when it reduces your team's workload and minimizes errors. You get AI-powered automations built the engineering way: integrations with your systems, queues and retry, logging, monitoring, quality and cost control.
Who is this for
- Online stores (PrestaShop and others) that want to automate content, support and integrations
- Service businesses with repetitive processes: emails, documents, tickets, case classification
- IT teams that need solid implementation: API, security, monitoring, rollback
Problems this solves
- ! Manually translating hundreds of product descriptions – expensive, slow and stylistically inconsistent
- ! Customer support drowning in repetitive tickets while response time keeps growing
- ! Data arrives in different formats (PDF, emails, HTML) – manual transcription is a waste of time
- ! System integrations require manual data copying and are prone to errors
- ! No control over API costs and result quality – AI works like a "black box"
- ! Shortcut AI implementations generate hallucinations and nonsense instead of valuable results
When you should consider this
- Product description translation and generation (PL↔EN) with quality control
- Meta title/description, summaries and short descriptions with style templates
- Customer ticket classification and prioritization with response suggestions
- Data extraction: PDF, emails, HTML → structured data (JSON)
- Data normalization: names, units, product attributes
- Webhooks and APIs (Python/Node.js) with queues, retry and idempotency
- Monitoring and alerts: logs, metrics, dashboards for AI processes
- Least privilege access, environment separation, data anonymization
- Action and result logging, prompt versioning, rollback capability
How the process works
Consultation (60 min)
Together we pin down the business goal, input data, risks and success metrics. You get a clear answer to: what exactly are we measuring.
Quick prototype (MVP)
1–2 scenarios + minimal API + logging. You see whether AI delivers sufficient quality on your real data.
Production integration
Your systems get connected with queues, retry, monitoring and cost control. Full observability from day one.
Quality control
Validation rules, acceptance samples, prompt versioning. AI doesn't work "in the dark" – every result is verifiable.
Maintenance and growth
Fixes, metric observation, cost optimization. Development of new automations based on results.
- 1
Consultation (60 min)
Together we pin down the business goal, input data, risks and success metrics. You get a clear answer to: what exactly are we measuring.
- 2
Quick prototype (MVP)
1–2 scenarios + minimal API + logging. You see whether AI delivers sufficient quality on your real data.
- 3
Production integration
Your systems get connected with queues, retry, monitoring and cost control. Full observability from day one.
- 4
Quality control
Validation rules, acceptance samples, prompt versioning. AI doesn't work "in the dark" – every result is verifiable.
- 5
Maintenance and growth
Fixes, metric observation, cost optimization. Development of new automations based on results.