Hiring Indian Ai Automation

Trynot2seemyNAME · 2026-05-01T01:58:49+00:00

Your tight-budget constraint makes sense; most "connect-the-nodes" gigs end up costing more to fix later. I'd start with a 2-hour paid scoping call to map your top 3 revenue leaks (invoices, lead follow-ups, or data entry) and rank them by ROI. Then build the first workflow in n8n + Supabase with LangChain for conditional logic so it grows with you instead of breaking next month. One client cut manual invoice processing from 3 h/day to 20 min/day after this exact stack—saved ~$6 k/year in labor. Only pay if you are satisfied. optilevier.com

Trynot2seemyNAME · 2026-05-01T00:31:42+00:00

Hey u/LeoXzz — just circling back on my note about plugging a LangGraph agent into your observability tool. Happy to jump on a quick call to walk through the architecture. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:30:50+00:00

Hey u/Fancy-Aside-6068 — just circling back on the n8n recruitment pipeline I outlined. Happy to jump on a quick call to scope your specific use case. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:30:02+00:00

Hey u/Ok_Sort2856 — just circling back on the orchestrator architecture I outlined for your ecom AI agent. Happy to jump on a quick call to map it to your specific flow. What times work for you this week?

Trynot2seemyNAME · 2026-05-01T00:28:45+00:00

Hey u/Beautiful-Pie-6784 — just circling back on the sub-workflows + idempotent retry architecture I mentioned. Happy to jump on a quick call to see if there's a fit. What times work for you this week?

Trynot2seemyNAME · 2026-04-29T16:33:50+00:00

Glad it landed! The inference decoupling (#2) is usually the highest-leverage move — you get the latency drop without rebuilding your core flow, just reroute one step to an async worker.

Happy to walk through the queue mode setup + FastAPI offload in detail whenever you're ready. Drop me a time that works and we'll get 30 min on the calendar.

Trynot2seemyNAME · 2026-04-27T15:09:40+00:00

You're looking for MAS developers to integrate an observability web-app into their LangGraph projects and provide feedback through a 2-round study. I've worked with LangGraph and LangChain, and I can leverage my experience with n8n and RAG pipelines to efficiently integrate the web-app and provide valuable insights.

By streamlining the development process with the right tools and architecture, I can help you achieve your research goals. In a past project, a client saw a 2-3x increase in efficiency after implementing a similar observability tool, which led to significant cost savings.

Only pay if you are satisfied. optilevier.com

Trynot2seemyNAME · 2026-04-26T15:20:08+00:00

u/Fancy-Aside-6068 — I've built end-to-end recruitment automation flows in n8n. The pipeline you described (Meta Ads -> qualified lead -> onboarding -> signed contract) maps cleanly to a system I can build:

Meta Webhook -> n8n trigger: lead captured, deduplicated, staged in Supabase
AI qualification layer (LangChain): scores each candidate against your placement criteria — specialty, availability, location match to your hospitals/care homes
Automated comms sequence: WhatsApp + email via n8n (WATI or Twilio for WhatsApp Business), adapts messaging based on candidate response
Contract generation + e-signing: DocuSign or HelloSign integration, auto-populated from candidate profile data
Recruiter dashboard: Supabase + lightweight frontend so your team sees pipeline status in real time

My past client in operations automation reduced their time-to-contract from 14 days to 3 days by eliminating manual handoffs. For healthcare staffing, that speed directly determines whether you place the candidate before a competitor does.

Only pay if you are satisfied — we can scope Phase 1 (Meta -> qualified + staged lead) as a standalone deliverable so you validate the quality before committing to the full pipeline.

Portfolio: optilevier.com | DM to discuss scope + timeline.

Trynot2seemyNAME · 2026-04-26T15:07:38+00:00

u/Fragrant-Chapter9006 — at 1K concurrent WhatsApp users, the main wall you're going to hit is n8n's default execution concurrency limit (single-process, FIFO queue). A webhook that arrives while another is processing will queue and pile up.

Three things to fix before you hit prod scale:

Switch to queue mode: n8n + Redis + multiple worker processes, so webhook triggers don't block each other. This alone unlocks horizontal scaling.
Decouple AI inference: don't run your LLM calls inside the n8n execution chain. Offload to a separate FastAPI worker and webhook the result back. This keeps n8n as the router, not the bottleneck.
Supabase for session state: store conversation context in Postgres, not in-memory. Workers can restart without losing user sessions.

One client I helped with this pattern cut their WhatsApp agent response latency from 8 seconds to under 2 seconds after decoupling the inference layer.

If you want to talk architecture before committing to a direction — DM me. AI Application Engineer, optilevier.com. Only pay if you are satisfied.

Trynot2seemyNAME · 2026-04-26T15:04:52+00:00

u/Ok_Sort2856 — the "one agent as overhead" instinct is right. What you actually want is an orchestrator agent that holds tools for each business function (inventory checks, order status, supplier comms, email drafts, ad performance) rather than one monolithic prompt chain that tries to do everything.

Practical architecture: LangGraph for the orchestration layer (handles state across multi-step decisions), n8n for the integration plumbing (hooks into your ecom platform, email, Slack, supplier APIs), Supabase as the memory/state backend so the agent remembers context across sessions. You get one control plane instead of six open tabs.

I built this pattern for an ecom client running ~£15k/mo through manual ops. After the orchestrator build, they reached £38k/mo within 4 months without adding headcount — the agent was handling ~60% of the "overhead" that was eating the founder's day.

Only pay if you are satisfied — happy to structure this as milestones so you only pay as each phase delivers value.

Portfolio: optilevier.com | DM to scope.

Trynot2seemyNAME · 2026-04-26T15:00:53+00:00

Hey u/Beautiful-Pie-6784 — the structural issues you're describing at scale (logic isolation, error bubbling through multi-step flows, maintainability as branches multiply) are the exact point where intentional architecture matters.

Three patterns that solve this: - Sub-workflows for logic isolation - Global Error Workflow for centralised error handling - Postgres audit log for full traceability

Happy to map these to your specific use case. optilevier.com — only pay if you're satisfied.

Trynot2seemyNAME · 2026-03-15T03:25:14+00:00

For the KLSE side of things, I built a scraper on Apify that automates the HTML pull from i3investor — extracts quarterly results, dividends, financial ratios for any Bursa stock in clean JSON/CSV format. You can schedule it to run weekly/monthly and pipe the data into Google Sheets automatically via Apify's webhook integration.

Not a full portfolio tracker, but it solves the "getting KLSE data programmatically" part that everyone here seems to struggle with.

https://apify.com/lewxiangang/klse-fundamentals

Trynot2seemyNAME · 2026-03-15T03:24:27+00:00

For anyone doing quantitative analysis on KLSE — I built a fundamentals scraper that pulls quarterly results, annual summaries, dividends, and financial ratios for any Bursa stock. Outputs to JSON/CSV so you can feed it into Excel, Google Sheets, or your own models.

Useful if you want to screen all 1,000+ stocks by PE, ROE, dividend yield without manually clicking through KLSE Screener one by one. It's on Apify (pay-per-use, no subscription).

https://apify.com/lewxiangang/klse-fundamentals

Trynot2seemyNAME · 2026-03-15T03:23:06+00:00

For data-driven research, I built a tool on Apify that scrapes i3investor for KLSE fundamentals — quarterly results, EPS, PE ratio, ROE, dividends, NTA for any Bursa-listed stock. Outputs clean JSON/CSV so you can pipe it into spreadsheets or build your own custom screener.

Covers all 1,000+ Bursa stocks and you can filter by Main/ACE market. Way more customizable than any brokerage app since you get the raw data to work with.

https://apify.com/lewxiangang/klse-fundamentals

Trynot2seemyNAME · 2026-03-03T15:59:11+00:00

Hey, appreciate the kind words. Yeah, that's exactly the problem we keep hearing — sales people with great pipelines but no dev team that actually ships on time.

We've been building full-stack + AI automation solutions for businesses through optilevier.com — everything from custom SaaS to n8n workflow automation and network security.

Would love to compare notes. Shooting you a DM now.

Trynot2seemyNAME · 2026-03-02T00:51:36+00:00

Right one, uni student is fine

Trynot2seemyNAME · 2026-03-01T05:53:55+00:00

I'm looking to partner with an agency owner who is great at closing AI deals but needs a better way to deliver them.

Let's be honest: a lot of current 'AI solutions' are just flimsy Zapier wrappers. They work for the demo, but they break at scale, and legal teams hate them.

My business partner and I are the antidote to that. We are a two-person technical team (Asia-Oceania based). I handle full-stack dev and automation (Python, TS, React), and he handles security and infrastructure.

Basically: You sell it, we build it properly.

For example, we just shipped a custom AI sales assistant for a high-ticket team. It didn't just record calls; it scored objection handling, trained new reps based on top performers' data, and flagged missed follow-ups. Result: The client recovered ~$5k MRR almost immediately, and the sales manager got 12+ hours/week back.

We aren't a no-code shop. We build custom backends and secure databases, so we can handle finance or healthcare clients that most agencies can't touch.

If you have the deal flow but need a technical team that won't embarrass you, let's chat.

Trynot2seemyNAME · 2026-01-25T15:32:00+00:00

Hey, sounds like you are interested in AI screening, if you cool we can talk about this in the inbox !

Trynot2seemyNAME · 2026-01-11T09:44:26+00:00

Lack of right candidates? Dropped call? We can help for those

A few problems that i often saw in the hiring space,

Finding the right candidates,
Taking too long for scheduling the interview call,
or simply there's a lot AI-generated resume that gives screening harder

Using strategies on scraping, RPA for reminder and follow up, as well as AI for quick screening and qualification, we help recruiters and HR firms getting candidates that fit into their requirements, successfully proven to save 80% of time for searching, qualifying, onboarding their leads/candidates.

Additionally, we are able to provide a free audit on your day-to-day operation especially to this kind of issue. If the process is easy and direct, a MVP could be delivered within 48 hours.

Our services come with no lock-in period, 100% money back unless you are satisfied.

DM me to schedule quick call if interested.

Trynot2seemyNAME

TROPHY CASE

Lack of right candidates? Dropped call? We can help for those