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Connected AI workflows

AI & Business Automation

I build automation around a measurable workflow rather than adding AI for its own sake. The result might be an MCP app, an AI-assisted publishing tool, an internal agent, a content pipeline or an API workflow connecting the systems your team already uses.

Discuss your project See related work

A good fit when

  • Your team repeatedly moves information between systems
  • AI output needs controlled access to real tools and data
  • Content or support work needs a reliable approval workflow
  • An API-driven process needs monitoring and failure handling

What I can deliver

A focused system, not a pile of features.

01

MCP apps and structured AI tool integrations

02

LLM and ChatGPT-powered product features

03

n8n, Zapier and Make workflow automation

04

Content, social publishing and YouTube workflows

05

Authenticated API orchestration and data transformation

06

Human review, logging and operational safeguards

How the work moves

  1. 01

    Choose one workflow and define the time or quality target

  2. 02

    Map triggers, inputs, systems, approvals and failure cases

  3. 03

    Prototype the smallest useful automated path

  4. 04

    Add permissions, validation, observability and human review

  5. 05

    Measure the result and expand only where the workflow proves useful

What success looks like

  • Fewer repetitive handoffs and copy-paste tasks
  • AI actions constrained by permissions and structured inputs
  • A workflow the team can inspect, adjust and improve

Typical technology

MCPLLM APIsChatGPT AppsJSON-RPCREST APIsn8nZapierMake

Related work

Proof from real implementations.

SocialRelay — AI-Assisted Social Publishing screenshot

SocialRelay — AI-Assisted Social Publishing

A reusable publishing interface with structured responses for AI-driven workflows.

Read case study
AIToolBucket — AI Tools SaaS Platform screenshot

AIToolBucket — AI Tools SaaS Platform

A live SaaS platform offering a growing collection of free tools from a single branded product.

Read case study

Common questions

Before we start.

How do you decide whether a workflow should use AI?

I separate deterministic steps from judgment-heavy steps. Rules and APIs handle predictable work; AI is used where classification, extraction, drafting or flexible interpretation genuinely improves the workflow.

Can you connect AI assistants to our existing tools?

Yes. Depending on the system, I can use its API directly, build an MCP integration or orchestrate it through n8n, Zapier or Make with authentication, validation and controlled actions.

Will people still review AI-generated work?

When the action carries reputational, financial or operational risk, I design an explicit review or approval step. The goal is useful acceleration with clear control, not invisible automation.

Have a relevant project?

Let’s define the practical first step.

Share the current setup, the outcome you need and any constraints already known.

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