Dripify costs money n my n8n workflow automation chain for automated LinkedIn messages was free. LinkedIn updated a frontend selector last Tuesday n my headless browser flow started throwing errors. I spent three hours fixing it before my first call.

The DIY automation community makes linkedin message automation look easy. Scrape leads. Enrich them. Fire off connection requests. Then u deploy it n LinkedIn changes their DOM structure. Or ur proxy IP gets flagged. Has anyone here maintained a DIY automation stack for more than six months?

I've been trying to understand how AI outbound calling actually works because I'm seeing it mentioned more and more in automation project. From what I understand, there are a few pieces involved- the system making the calls, the AI voice handling the conversation, and the CRM tracking what happens afterward.

The part I'm still confused about is how these pieces usually connect. Is the dialer normally built into the voice platform, or do people usually connect separate tool together? Also curious how these setups handle scaling before things start getting messy.

If anyone ha worked with a real setup, I'd love to hear how it was structured.

building an automated enrichment layer for inbound leads from Meta and Linkedin forms. Goal is to append company context fast enough to influence the first automated touchpoint, ideally within the same session. The problem is input quality varies a lot. Linkedin leads are cleaner because profile data populates automatically. meta leads are messier, inconsistent company name formatting, sometimes personal emails, abbreviations. Need an api that handles fuzzy matching and ideally returns a confidence signal so I can route low confidence matches differently downstream. Also specifically interested in real time latency. Some APIs are clearly optimized for batch and the p95 response time breaks down under real-time conditions. what are people running in production for this kind of setup??

the failures that scare me are not the ones where the workflow crashes. those are obvious.

it's the partial success ones: it wrote the draft, skipped the approval note, used an old source, scheduled the wrong version, or marked the job done because one step returned ok.

i've started treating every automated content or ops flow as needing three outputs:

• what changed
• what was skipped
• what needs a human decision

that sounds boring, but it catches a lot of fake progress. if an agent cannot explain the skipped part, i don't really trust the completed part either.

curious how others handle this. do you log partial success as a failure, a warning, or just a normal run with notes?

I run an agency out of India which was limited till SaaS, Mobile apps, e-commerce, demand generation, etc. I’ve got clients mostly around USA, Australia and little bit in Europe as well. I’m not much of technical guy but I know how to market and sell very well.

I’m planning to expand into AI services, and looking for a trustable partner in India (No agencies please). We can initially work on some internal POCs (paid) and some project based contracts.

I’m open to partnering or co-running the AI vertical together in future if things workout well. I’m also open to discuss product/service ideas if you got.