AI Automation & Integration
In development. Taking enquiries.
- ·Repetitive work moved into pipelines: document processing, content extraction, reporting, routing.
- ·Wired into the tools a business already runs through their APIs, not a replacement for them.
- ·Current example: content extraction for 20+ legacy WordPress sites at Lift Legal Marketing.
| Becomes live when | A pipeline sample a stranger can open is published, with its export and one recorded run. The client work behind this service is real and named on the career record, and none of it is public. |
| Built with | Claude API · OpenAI API · LangChain · Dify · RAG pipelines · Node.js |
| Delivered by | automation-engineer · ai-engineer |
| Skills used | n8n-delivery · ghl-delivery |
What this involves
AI automation moves repetitive work into a pipeline that runs without a person in the loop: document processing, content extraction, reporting, routing and reply. The work is wired into the tools a business already runs through their APIs rather than replacing them, because a business that has to change CRM to get automation usually does neither.
The current example is live work rather than a demonstration. At Lift Legal Marketing, content extraction for 20+ legacy WordPress sites runs through AI pipelines instead of manual copy and paste, and a Claude Agent Plugin system for the same team was built in two days. That two-day figure is on the career record at /about, not in a brochure.
The design rule on every pipeline is that it stays inspectable. Orchestration lives somewhere a person can open and read the flow, the CRM stays mainstream so the business is not locked to one contractor, and each step logs what it did rather than only that it finished.
There is also a published demonstration build, labelled as one. The Speed-to-Lead Engine is a Sample project: a web lead is answered in about a minute by SMS and email, the owner gets a Slack ping, and no reply within fifteen minutes escalates. The business in it is invented, there is no client behind it, and its impact arithmetic is a modeled projection from cited public benchmarks shown as arithmetic so anyone can rerun it with real numbers.
Content extraction for 20+ legacy WordPress sites at Lift Legal Marketing runs through AI pipelines instead of manual copy and paste.
How the work runs
1. Discovery
We agree what the project has to achieve, what it must not break, and how we will both know it worked, before any code is written.
2. Build
The work is built with an AI agent as the working partner and a human reviewing every change before it lands. You get commits you can read rather than a status update.
3. Review
You review running software at each milestone, not a screenshot of it, and the next milestone absorbs what you send back.
4. Launch
We deploy to production with checks in the pipeline and alerting that reaches a human, and you hold the repository and the infrastructure accounts.
Questions people actually ask
Can I hire an AI automation developer based in the Philippines?
Yes. James Lorenz Santos is based in Manila, Philippines, works in GMT+8, and takes remote clients worldwide by default. His current role already runs across timezones, working GMT+8 against an Australian company's hours. See /hire for the real cost bands, the timezone answer and the four-step process.
Which tools and platforms can be automated?
Anything with an API, which in practice is most of a modern stack. The integrations with a receipt in this repo are n8n, GoHighLevel, Slack, Google Sheets, Puppeteer, WordPress and the Claude and OpenAI APIs; the full list with the project each one was used on is at /projects. A tool not on that list is not a refusal, it is just one with no public receipt yet.
How do I know a process is worth automating?
A process is worth automating when it is rule-based, happens often, and costs more human time than the pipeline will cost to build and maintain. The third condition is the one that gets skipped: a task that runs twice a month rarely repays the maintenance, and a discovery call that says so is cheaper than a build that proves it.
What happens to sensitive data in an AI automation pipeline?
That is a decision to make explicitly before the first pipeline ships, not a default to inherit. The available choices are to strip or tokenize identifying fields before anything reaches a model, to route only non-sensitive steps through an external API, or to keep the whole step inside infrastructure the business already controls. Which one applies depends on the data and the jurisdiction, and it belongs in the written scope rather than in a reassurance.
Does automation replace the people doing the work now?
In the engagements on this site it has replaced tasks rather than roles, and it is worth being plain that this is a claim about these engagements and not a law. Content extraction across 20+ sites removed copy-and-paste work, not the people who decide what the content should say.