Hi,
I’ve got solid experience building production n8n workflows that match what you’re looking for. I’ve designed and deployed three systems in live environments, including a call transcription and analysis workflow that processes structured data at scale and a lead qualification chatbot that integrates multiple APIs. I’m comfortable working with Airtable, webhooks, and building reliable processes that go beyond simple point-to-point automations.
The fintech focus is interesting. While my production work has been in different verticals, I’ve worked extensively with data transformation, API integration, and webhook orchestration, which are the core skills needed for financial workflows. I understand the importance of dependable, auditable processes in that context, and I’m keen to learn the specific requirements of your environment.
Cheers,
Brandon
Just dropped you a dm too
Hey Darius I just came across the post and I think I’m the perfect fit for this as-
I have worked with similar qualification and cold email campaigns before
I have went ahead and built not only a detailed proposal but also a sample of the first workflow for us to reiterate and work upon
Am okay with partial payments and not a PIF in the beginning
Looking for long term work with vision driven people
I would to show you your workflow on a call whenever you are available use this link to book a call-
Hi Darius — this is exactly the type of work I do.
I design and build n8n lead-gen systems with:
source discovery/scraping → enrichment → AI qualification scoring → human QA gate → daily delivery to Sheets/Airtable/CRM.
Your 2 projects (Poland workplace food + EU fintech signals) are a strong fit.
I can propose architecture + sources and start with a fixed-scope Diagnostic Sprint (5 days) with real sample output.
Suggested start format:
- Day 1: source map + scoring logic
- Day 2-3: working n8n flow (real sample data)
- Day 4-5: QA, handover, next sprint plan
If useful, I can send a 1-page scope here in DM today.
How much does a set up like this cost?
Hey Steven,
If you are interested in an building a B2B Lead generation system. I can help you set it up.
I literally just built this system for a client a week ago, I can help you build a scalable system with even more advancement like lead enrichment, and autopilot email campaign at a discount quote in a quick timelines.
Kindly Dm here or message folafoluwaolaneye@gmail.com if this feels like a good fit.
Hi Darius,
For the first proof I would avoid building a giant scraper and prove the lead-quality ledger first:
approved source set → company qualification → enrichment fields → AI/rule score → Sheets or Airtable output + review/error log
That keeps bad source matches, missing contacts, duplicate companies, and low-confidence scores visible before the workflow becomes a daily machine.
My lane is written and fixed-scope. I also do not build around bypassing platform rules, so the source set needs to be allowed and stable. If that works, send one ICP, the approved sources you want to use first, and the exact output columns. I can scope a first paid proof slice in writing.
I can also show a small public proof shape for the artifact format before you send anything sensitive.
Alex Reed
WorkflowPatch
alex@workflowpatch.com
Hi,
I can help with automated B2B lead generation workflows.
A solid setup usually includes:
- lead source ingestion
- scraping or API-based enrichment
- deduplication
- filtering/scoring
- AI-based classification or personalization
- export to Google Sheets/CRM
- alerts and error logs
- optional outreach preparation
I’ve built similar logic around scrapers, APIs, dashboards and automation pipelines. If you share the current lead sources and target output, I can suggest the cleanest workflow structure.Hi,
I can help with automated B2B lead generation workflows.
A solid setup usually includes:
- lead source ingestion
- scraping or API-based enrichment
- deduplication
- filtering/scoring
- AI-based classification or personalization
- export to Google Sheets/CRM
- alerts and error logs
- optional outreach preparation
I’ve built similar logic around scrapers, APIs, dashboards and automation pipelines. If you share the current lead sources and target output, I can suggest the cleanest workflow structure.
Hi — for these lead-gen projects I’d avoid starting with scraping volume. The first milestone should prove lead quality.
Suggested PoC:
- one narrow ICP/source for Poland workplace food companies
- enrich only required fields
- AI score with visible reasons, not a black box
- push to Sheet/Airtable with confidence + next-action columns
- daily run log and duplicate suppression
I can help map or build that first slice. A sensible starting point is a fixed $75 workflow audit/spec so you know sources, fields, scoring rules, and risks before a full build.
Hey @Darius888 — both workflows here are solidly in my wheelhouse.
Background on me: I came up through manufacturing operations where bad data costs real money, so I build automation with a heavy emphasis on reliability and clean output. Self-taught n8n, working with it in production for real clients. Available for ongoing work right now.
For the Poland food solutions project, the architecture I’d propose: scheduled daily trigger → Apollo or Clearbit API pull filtered by country + headcount (50+) → AI scoring node using structured Claude prompts against your ICP criteria → deduplication check against existing Airtable/Sheets records → append only net-new leads. Employee count filtering at the API level keeps the workflow lean and avoids burning tokens scoring leads that fail the basic threshold.
For the EU fintech project, the harder part is sourcing newly registered businesses at scale across multiple countries since there is no single unified EU business registry. I’d combine a few sources: national registry APIs where available (Companies House UK, KVK Netherlands, etc.), Crunchbase API for startup launches, and Google News/RSS for e-commerce launch signals. Then a Claude scoring pass against banking/payments fit signals before enrichment and contact lookup.
Both workflows outputting daily deltas to Sheets/Airtable rather than full re-pulls keeps it manageable and makes it easy to spot what changed each day.
I work independently and push for a working proof-of-concept before any full payment commitment, which aligns exactly with how you want to structure this.
One question before I go deeper on architecture: for the fintech project, are you targeting any specific European markets as priority, or is it a full EU sweep from day one?
Hey,
Lead gen automation at scale is core to what I do — this is exactly my territory.
On your two projects:
Project 1 (Poland workplace food — 50+ employee companies):
I’d pull from Apollo/LinkedIn via API, filter by employee count and location, run AI scoring in n8n using Claude to qualify fit against your ICP, enrich decision-maker contacts, and push daily to Google Sheets/Airtable. I’ve built this stack for a GTM consultancy (Clay + n8n + Claude + Apollo).
Project 2 (European fintech — newly registered businesses, e-commerce launches):
I’d use Companies House-equivalent APIs per country + LinkedIn org signals + e-commerce platform registrations (Shopify Partners API etc.) as data sources. AI scoring via Claude to flag likelihood of needing banking/payment services. Daily digest to Airtable with enriched founder contacts.
What I can offer:
- Working proof-of-concept on your choice of Project 1 or 2 with real data before full payment
- Architecture proposal first — you decide if it makes sense before we start
- Long-term ongoing collaboration is exactly the model I prefer
I work remotely from South Africa (UTC+2), async-first. Available to start immediately.
Let’s talk architecture. narotsuhail@gmail.com or DM here.
— Suhail Narot | Fajr AI | fajrai.net
This is my wheelhouse — I’ve built production B2B lead gen pipelines in n8n using Apollo, LinkedIn, Hunter, and custom scrapers with AI scoring layers (Claude/OpenAI) to qualify and route leads into Airtable or Google Sheets on daily schedules.
Both projects fit well: the Poland food company job (50+ employees, decision-maker enrichment, daily runs) and the European fintech signal-monitoring build. Happy to discuss both scopes and ongoing rate. DM me.
Hi Darius. I can help with the n8n lead-intelligence workflows, but I would keep the first milestone bounded and compliant: one source set, clear filtering rules, enrichment only where allowed, AI scoring with evidence links, and Google Sheets/Airtable output with manual review before any outreach.
I work on backend/API, workflow automation, data workflows and AI systems. One question: which source should prove the first workflow first: Polish workplace food prospects or the fintech business-launch signal?
Hi Darius — I’d love to discuss this.
I specialize in AI systems and workflow automation, with a strong focus on n8n-based lead intelligence, enrichment, scoring, and production-ready orchestration.
For this project, I’d approach it as an architecture-first build:
-
narrow POC first using real data,
-
modular scraping/enrichment/scoring pipeline,
-
AI-assisted qualification rules,
-
clear handoff for maintenance and scaling.
My background includes operational AI systems, workflow orchestration, API integrations, stateful workflows, and lead intelligence automation. I’m comfortable building the system in a way that is reliable, extensible, and easy to maintain.
Portfolio: https://theworkflow_dev.vercel.app
GitHub: theworkflow-dev (theworkflow_dev) · GitHub
If helpful, I can outline a POC plan for both lead-generation use cases and show how I’d structure the workflow before we start.
Hey Darius,
I see you’ve got a lot of responses here, so I’ll keep this direct.
Your two projects share the same core pipeline pattern — source discovery → enrichment → AI scoring → CRM output — but the data sources and scoring models differ enough that they’re two separate architectures sharing a skeleton.
For Project 1 (Poland B2B), I’d propose:
-
Source: CEIDG + GUS business registry for headcount filtering, supplemented by Apollo or Clearbit for firmographic enrichment
-
Decision-maker enrichment: Layered approach — Proxycurl or Phantombuster for LinkedIn contact data, plus email pattern discovery via n8n HTTP
-
AI scoring: LLM node in n8n with a structured prompt scoring ICP fit on headcount, industry sub-code, growth signals, and recent hiring activity
-
Output: Airtable with automated status tracking + Slack notification for high-scored leads
For Project 2 (fintech), the source layer changes — business registry APIs across EU member states + Crunchbase for e-commerce detection + incorporation date filtering — but the enrichment and scoring layers carry over with different ICP parameters.
What makes my approach different: I’m a technical implementation specialist who partners with a senior automation architect (decades of enterprise experience). He handles the strategic architecture and client relationship; I build, test, and deliver the working workflows. You get senior-level design without the senior-level rate.
If you want to explore this further, email me at EclipsiaCodesmith@zohomail.com and I’ll send a detailed architecture breakdown for one of the two projects as a starting point.
-– Eclipsia
Hey,
I build n8n automation workflows for lead generation, WhatsApp outreach with auto follow-up, and RAG-based AI systems — all locally hosted for full data privacy. Delivered and running in production. Open for a 15-min call this week?
Portfolio: hksautomations.github.io
WhatsApp: +91 99257 98962
Gmail: hks.automations@gmail.com
Regards,
Harvish Shah
Hi — B2B lead generation automation is one of the core use cases I build in n8n, and the ongoing model fits perfectly: this kind of system needs iteration, monitoring, and adjustment as data sources shift.
What I’d bring to this:
- Multi-source lead enrichment pipelines: scraping/API → deduplication → qualification scoring → CRM write
- n8n webhook + cron orchestration with retry logic and dead-letter handling for failed enrichment steps
- Claude/OpenAI for lead classification, personalization scoring, and ICP matching
- Lightweight alerting on high-value leads via Slack or email with configurable thresholds
My approach: start with one bounded pipeline — one source, one qualification rule set, one CRM destination. Once that’s stable and monitored in production, expand. Much less risk than building the full system at once.
Relevant production work: I run a live lead capture automation (webhook → enrichment → CRM → personalized AI follow-up) and an AI email triage system, both in n8n with proper error handling and logging.
Available immediately for ongoing work. Happy to review your current setup and suggest one concrete improvement before any paid engagement.
Oktay | Flowmatic Works | flowmatic.works
Hey,
I build n8n automation workflows for lead generation, WhatsApp outreach with auto follow-up, and RAG-based AI systems — all locally hosted for full data privacy. Delivered and running in production. Open for a 15-min call this week?
Portfolio: hksautomations.github.io
WhatsApp: +91 99257 98962
Gmail: hks.automations@gmail.com
Regards,
Harvish Shah
Hi Darius,
This is a good fit for a narrow proof-of-concept before the larger build.
For the first version I would keep it to one ICP and one output table:
- define the company source and employee-count filter
- enrich a small sample of decision makers
- run simple rules + AI scoring for fit
- write clean rows to Google Sheets/Airtable
- include a run log and notes on data source limits/costs
That gives you a real-data proof before expanding to the fintech workflow or daily production runs.
I can work async and start from a short ICP brief plus your preferred enrichment source. A small paid workflow map / POC would be the cleanest first step.
Best,
Tim
Hi Darius,
This is a strong fit for a small paid proof-of-concept before a full lead-gen build.
For Project 1 I would start with one narrow pipeline:
- source list for companies in Poland with 50+ employees
- enrichment fields agreed upfront
- AI scoring with explainable reasons, not just a black-box score
- output to Sheets/Airtable with source URL, confidence, and next action
- dedupe, run log, failed-row log, and a handoff note so the workflow is maintainable
For Project 2 I would use the same pattern but change the source and scoring rules around new businesses/e-commerce/payment intent.
I prefer proving the system with 20-50 real rows first, then turning it into a daily workflow once the quality bar is clear. That protects both sides from paying for a scraper that produces noisy leads.
I can take a first fixed POC or workflow map and deliver the n8n structure, test rows, QA notes and suggested next milestone.
Best,
Tim