The Automation Stack We Use to Build Real AI Systems for Businesses

In 2026, automation is no longer a nice to have. For most businesses, it is the difference between scaling smoothly and burning time on manual work.

At TUSTRA, we design and build AI driven systems that sit underneath day to day operations. The tools we choose matter because they need to be reliable, flexible, and able to work together without creating complexity for the client.

Below is the core stack we use and why each tool plays a specific role.

n8n

The backbone of automation

n8n is the orchestration layer behind most of the systems we build. It allows us to design complex workflows that connect data, AI models, and business tools in a controlled and auditable way.

Why we use it:

• Full control over logic and data flow

• Self hosted for security and flexibility

• Ideal for complex, multi step processes

• Scales with the business instead of locking you into usage based pricing

This is where automation actually happens.

Supabase

The structured data layer

Supabase acts as the central data store for many of our systems. It is where clean, structured information lives once it has been pulled from CRMs, emails, forms, or documents.

Why we use it:

• Reliable relational database

• Real time updates

• Works well with AI and automation workflows

• Gives businesses ownership of their data

It replaces fragile spreadsheets with something far more robust.

AWS

Infrastructure and reliability

AWS provides the underlying infrastructure for hosting, storage, and compute when systems need to run reliably in the background.

Why we use it:

• Proven, production grade infrastructure

• Scales without redesigning systems

• Suitable for long running and critical workflows

Most clients never see AWS directly, but they benefit from its stability.

Notion

Operational clarity

Notion is often used as the front facing workspace for teams. It is where tasks, documentation, and internal processes live.

Why we use it:

• Familiar to most teams

• Flexible enough to model real workflows

• Easy to connect into automation systems

Notion works best when it is kept clean and supported by automation behind the scenes.

OpenAI, Claude, Gemini, and Perplexity

Different AI models for different jobs

Not all AI models are good at the same thing. We select models based on the task rather than forcing one tool to do everything.

• OpenAI is strong for reasoning, structured outputs, and general purpose automation

• Claude excels at longer context and code related tasks

• Gemini performs well with multimodal inputs and Google ecosystem integrations

• Perplexity is useful for research, summarisation, and source aware queries

Using the right model in the right place keeps systems accurate and predictable.

Airtable

Flexible operational databases

Airtable is useful when teams need a visual, editable database without the overhead of a full backend.

Why we use it:

• Clear views for non technical users

• Strong filtering and relationships

• Works well as an input layer for automation

It is often used where teams want control without complexity.

Slack

Where automation meets people

Slack is where automation becomes visible. Alerts, approvals, summaries, and handovers often happen here.

Why we use it:

• Keeps humans in the loop where needed

• Reduces email noise

• Makes automation feel supportive rather than intrusive

Well designed systems communicate clearly without overwhelming teams.

Go High Level

Sales and customer operations

For businesses that rely on outbound, inbound, and follow up workflows, Go High Level provides a strong operational layer.

Why we use it:

• Centralises CRM, messaging, and follow ups

• Works well with AI assisted workflows

• Reduces tool sprawl for sales focused teams

It is most effective when combined with external intelligence and automation.

Canva

Consistent content at scale

Canva is used where content needs to be produced quickly but still remain on brand.

Why we use it:

• Simple for non designers

• Easy to integrate into content workflows

• Supports repeatable output

Automation helps reduce the time spent recreating assets from scratch.

Google NotebookLM

Internal knowledge and understanding

NotebookLM is useful for turning internal documents into something teams can actually query and learn from.

Why we use it:

• Helps teams explore their own data

• Useful for onboarding and internal research

• Adds value without rebuilding documentation systems

It works best as a layer on top of existing knowledge.

Final Thoughts

Automation is not about tools. It is about systems.

The right stack depends on how a business operates today, not what is trending. At TUSTRA, we use these tools because they let us design systems that are reliable, understandable, and adaptable over time.

We start with a lightweight audit, understand how work actually gets done, and then apply the right combination of automation and AI to remove friction where it matters most.