Which processes can be automated with AI?

The most common cases: extracting information from emails or PDFs, classifying support tickets, drafting standard replies, meeting summaries, CRM data enrichment. Any process requiring a human to read, understand and produce a repetitive text output can be automated.

Is AI automation GDPR-compliant?

Yes, with the right architecture. If the processed data is personal, the pipeline is configured to use a model hosted in Europe (Mistral AI, OVHcloud) or on-premise. For non-sensitive data, US providers with a signed DPA are acceptable depending on context.

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AI automation

Name: Pierre Kasparian - AI Integration & Data
Rating: 5

Cutting repetitive tasks by connecting AI to existing tools and processes.

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What is it?

AI automation connects software agents combining LLMs, APIs and business logic to execute repetitive tasks without human intervention. Extracting information from emails or PDFs, classifying documents, drafting standard replies: these processes run continuously and integrate with existing tools.

How it works

1
Process mapping
Identifying high-volume repetitive tasks, estimating time savings and prioritising by business impact.
2
Agent design
Agent architecture (n8n, LangChain): defining triggers, which LLMs to use per step and required integrations.
3
Development and integrations
Building the workflows, connecting to existing APIs (CRM, internal tools, email) and testing on real cases.
4
Testing and deployment
Output validation on a representative sample, production deployment with error monitoring and weekly check-ins for the first few weeks.
5
Maintenance
Onboarding support for automated workflows, real-world adjustments and agent evolution as business needs change.

What it covers

Document classification, drafting standard replies, information extraction, automatic summaries
Agents that orchestrate multiple AI models and connect to existing tools (CRM, APIs, databases)
Cost control: not all AI providers are equal depending on the task

Risks and compliance

Confidence thresholds and human escalation to avoid risky automatic decisions.
Complete log of inputs, outputs, models used and actions performed.
Guardrails against agent loops, cost overruns and repeated actions.
GDPR handling through field minimization before LLM calls and hosting matched to data sensitivity.

Related projects

Batch Inference Service - PrettoA central service that processes large volumes of documents with AI while cutting costs.Quantified results15,000+ LLM analyses per day−50% inference costsReused by 10+ projects LLM Platform Reliability - PrettoImprovement of an unstable AI platform to remove service outages and make the teams' day-to-day reliable.Quantified results80 % -> 95 % uptime−50% worker load Prompt Auto-improvement Pipeline - PrettoA system that spots a prompt's weaknesses from real cases on its own, then proposes and evaluates an improved version.Quantified results5h saved / week / person75% of prompts improved automatically

Frequently asked questions

Which processes can be automated with AI?: The most common cases: extracting information from emails or PDFs, classifying support tickets, drafting standard replies, meeting summaries, CRM data enrichment. Any process requiring a human to read, understand and produce a repetitive text output can be automated.
Is AI automation GDPR-compliant?: Yes, with the right architecture. If the processed data is personal, the pipeline is configured to use a model hosted in Europe (Mistral AI, OVHcloud) or on-premise. For non-sensitive data, US providers with a signed DPA are acceptable depending on context.

Let's discuss a project

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