AI case studies (measurable results)
No vague promises — real projects, concrete metrics, lessons learned.
Customer support automation at a regional bank
The problem
The customer service team manually handled 2,000 requests per week. Response times exceeded 48 hours, and teams spent 60% of their time on recurring questions (balances, transfers, documents). Management wanted to cut delays without hiring.
The approach
Audit of the 200 most frequent request types. Deployment of an AI agent connected to the internal knowledge base (RAG architecture), with human validation on sensitive cases (complaints, fraud). Rollout in 3 phases: automated FAQs, then handling of simple requests, then advisor assistance.
The results
The project started with a 10-day audit to map the requests and identify quick wins. The priority: the 15 request types that represented 70% of the volume.
The AI agent uses a RAG architecture connected to internal procedures and existing FAQs. Every answer cites its source and offers a link to the full document. Complex cases (complaints, suspected fraud) are systematically escalated to a human advisor.
Within 3 months, the system handled 80% of requests without human intervention. Advisors now focus on high-value advisory work and sensitive situations.
Accelerating the product roadmap of a tech startup
The problem
6 months behind on the product roadmap. The team of 8 developers was overwhelmed by technical debt, manual testing and documentation. The Series A imposed strict deadlines on key features.
The approach
A 4-week Augmented Code Sprint: express audit of the codebase, identification of bottlenecks, then integration of AI tools into the daily workflow (test generation, assisted refactoring, automated documentation). Developer training in parallel.
The results
The team was already using GitHub Copilot, but superficially — code completion only. The sprint introduced complete workflows: generation of unit and integration tests, assisted refactoring with systematic review, and technical documentation generated then validated by the devs.
The most impactful change: setting up an AI-generated automated test pipeline, which took coverage from 35% to 78% in 2 weeks. Detected regressions dropped by 60%.
The team continued with a monthly subscription to embed the practices and keep optimizing its workflow.
RAG on a technical knowledge base in manufacturing
The problem
15,000 technical documents (procedures, safety sheets, machine manuals) scattered across 3 different systems. Technicians spent an average of 45 minutes a day searching for information. Safety risks linked to the use of outdated documents.
The approach
Ingestion and indexing of the 15,000 documents with cleaning, deduplication and version management. RAG architecture with access control by role and site. Conversational search interface with systematic citations and links to the source document. Quality monitoring (faithfulness, coverage, latency).
The results
The biggest challenge wasn't technical but organizational: 3 documentation systems, duplicated files (sometimes with conflicting versions) and no single source of truth. The first step was a data quality workshop to clean, deduplicate and version everything.
The RAG architecture uses an automated ingestion pipeline that detects updates and continuously reindexes. Each answer displays the source, the document date and the confidence level. Outdated documents are flagged but remain accessible for traceability.
Two months after deployment, 87% of technicians were using the tool daily. Average search time dropped from 45 to 7 minutes a day.
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