Case studies

AI automation case studies.

Real solutions in production. Three examples of how we apply AI where it moves business metrics, not headlines. With verifiable results.

Retail

Sales-reporting automation for a retail company

−8 hof manual reporting a week

Before8 h

Now0 h

  • n8n
  • BigQuery
  • Looker Studio

  1. The challenge

    The operations team at a retail company spent 8 hours a week manually consolidating sales, stock and incident data from different systems. Data arrived with a 7-day lag and decisions were made on stale information.

  2. The solution

    We built an automated pipeline with n8n that pulls data from the POS and the ERP every night, processes it in BigQuery and updates a Looker Studio dashboard with the previous day's data. Every Monday at 7am, the team also receives a weekly report.

  3. The result

    Zero manual reporting hours. The team recovered a full day each week. The operations director makes decisions on yesterday's data, not last week's.

Logistics

AI system for multi-fleet distribution route optimization

−25%operating costs

Before100%

Now75%

  • LangGraph
  • Python
  • FastAPI

  1. The challenge

    A logistics company managed its fleet's route assignment manually, with no real-time visibility and no ability to react to incidents. The operations lead spent hours planning routes that became obsolete at the first surprise.

  2. The solution

    We developed a route-optimization system with an AI agent that factors in delivery windows, vehicle capacity and real-time traffic. Integrated with their fleet management system to reassign routes automatically when incidents arise.

  3. The result

    25% lower operating costs. Fewer kilometers driven, more deliveries per vehicle and automatic reaction to surprises. The team went from planning routes to supervising exceptions.

Fintech

Conversational assistant handling 60% of customer queries

2.5×customer support capacity

Before4-6 h

Now<2 min

first response

  • GPT-4o
  • RAG
  • Pinecone

  1. The challenge

    A financial services company had 2 support agents handling more than 300 weekly queries. Response times were 4-6 hours and the abandonment rate was high. Scaling meant hiring more staff.

  2. The solution

    We deployed a conversational agent with RAG trained on their product documentation, FAQs and use cases. It also answers on WhatsApp and works on their CRM: it checks contract status in real time, records the information in the CRM, creates tickets when a case needs follow-up and escalates to a human only when needed.

  3. The result

    60% of queries are resolved without human intervention. The first response arrives in under 2 minutes, down from 4-6 hours. Customer satisfaction rose from 6.8 to 8.4 out of 10. The 2 agents now handle only the complex cases.

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