The AI Automation Stack in 2025
The tools available for AI automation have matured dramatically. You no longer need a team of ML engineers to build powerful automation systems. Here's the complete stack we use at BestoAI International.
Workflow Orchestration
Activepieces ⭐ Our Top Pick
What it is: Open-source workflow automation platform (think Zapier, but more powerful and self-hostable)
Why we love it:
- 200+ pre-built integrations
- Visual workflow builder
- Self-hostable for data privacy
- Supports custom code steps
- Active open-source community
Best for: Connecting apps, triggering automations, building multi-step workflows
Pricing: Free self-hosted, cloud plans from $0
n8n
What it is: Another powerful open-source workflow automation tool
Why we use it: More developer-friendly than Activepieces, excellent for complex data transformations
Best for: Technical teams who want maximum flexibility
AI and LLM Layer
OpenAI GPT-4 ⭐ Our Top Pick
What it is: The most capable LLM for business automation tasks
Why we love it:
- Best reasoning and instruction-following
- Function calling for tool use
- JSON mode for structured outputs
- Reliable API with high uptime
Best for: Content generation, data extraction, classification, reasoning
Pricing: Pay-per-token, ~$0.01-0.03 per 1K tokens
Anthropic Claude 3.5
What it is: Strong alternative to GPT-4 with excellent long-context handling
Best for: Processing long documents, nuanced writing tasks
AI Agent Frameworks
LangChain ⭐ Our Top Pick
What it is: Python/JS framework for building LLM-powered applications and agents
Why we love it:
- Massive ecosystem of integrations
- Built-in support for RAG (retrieval-augmented generation)
- Agent and tool abstractions
- Active community and documentation
Best for: Building AI agents, RAG systems, complex LLM pipelines
LangGraph
What it is: Extension of LangChain for building stateful, multi-agent workflows
Best for: Complex agent orchestration, multi-step reasoning workflows
Data and Storage
PostgreSQL ⭐ Our Top Pick
What it is: Powerful open-source relational database
Why we use it: Reliable, scalable, excellent JSON support, works great with pgvector for embeddings
Best for: Storing leads, automation logs, structured business data
Pinecone / pgvector
What it is: Vector databases for storing and searching embeddings
Best for: AI agent memory, semantic search, RAG knowledge bases
Infrastructure
AWS ⭐ Our Top Pick
What it is: Amazon Web Services cloud platform
Services we use:
- Lambda — Serverless functions for automation triggers
- S3 — File storage for documents and exports
- RDS — Managed PostgreSQL
- CloudFront — CDN for fast content delivery
- SES — Transactional email sending
Best for: Scalable, reliable production infrastructure
The Complete Stack Summary
| Layer | Tool | Purpose |
|---|---|---|
| Workflow | Activepieces | Connect apps, trigger automations |
| AI/LLM | OpenAI GPT-4 | Reasoning, generation, extraction |
| Agents | LangChain | Build AI agents and RAG systems |
| Database | PostgreSQL | Store and query business data |
| Vectors | pgvector | AI memory and semantic search |
| Cloud | AWS | Scalable infrastructure |
| Runtime | Node.js | Custom automation logic |
How We Choose Tools for Each Project
We don't use the same stack for every project. Our selection criteria:
- Data sensitivity — Self-hosted tools for sensitive data
- Scale requirements — Serverless for variable load
- Team expertise — Tools the client's team can maintain
- Budget — Open-source where possible
- Integration needs — What existing tools need to connect
Getting Started With AI Automation
The hardest part isn't the tools — it's knowing what to automate and how to architect the system correctly.
At BestoAI International, we've built automation systems using all of these tools across 50+ client projects. We know what works, what doesn't, and how to build systems that are reliable and maintainable.
Book a free AI automation audit → and let's discuss the right stack for your business.